From 2cdb27854a600290f8ed31896fb76ed75080b3c1 Mon Sep 17 00:00:00 2001 From: henning Date: Tue, 4 Jun 2024 14:11:02 +0200 Subject: [PATCH 01/22] Remove unused variable --- .../data_integration/database_integration.py | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/protzilla/data_integration/database_integration.py b/protzilla/data_integration/database_integration.py index 4abcbf38..4a090fed 100644 --- a/protzilla/data_integration/database_integration.py +++ b/protzilla/data_integration/database_integration.py @@ -116,24 +116,6 @@ def gene_mapping( protein_groups, database_names, use_biomart=use_biomart ) - # We need to unpack the dictionaries to be able to create a dataframe, as this is potentially a many-to-many mapping - gene_to_protein_groups_unpacked = [ - (key, value) - for key, values in gene_to_protein_groups.items() - for value in values - ] - gene_to_protein_groups_df = pd.DataFrame.from_records( - gene_to_protein_groups_unpacked, columns=["Gene", "Protein ID"] - ) - - protein_group_to_genes_unpacked = [ - (key, value) - for key, values in protein_group_to_genes.items() - for value in values - ] - protein_groups_to_groups_df = pd.DataFrame.from_records( - protein_group_to_genes_unpacked, columns=["Protein ID", "Gene"] - ) return { "protein_group_to_genes": pd.DataFrame( protein_group_to_genes.items(), columns=["Protein ID", "Gene"] From c3f94396fd73246cfcdff77be8b2520faa00d4ca Mon Sep 17 00:00:00 2001 From: henning Date: Tue, 4 Jun 2024 18:40:37 +0200 Subject: [PATCH 02/22] Revert "Remove unused variable" This reverts commit 2cdb27854a600290f8ed31896fb76ed75080b3c1. --- .../data_integration/database_integration.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/protzilla/data_integration/database_integration.py b/protzilla/data_integration/database_integration.py index 4a090fed..4abcbf38 100644 --- a/protzilla/data_integration/database_integration.py +++ b/protzilla/data_integration/database_integration.py @@ -116,6 +116,24 @@ def gene_mapping( protein_groups, database_names, use_biomart=use_biomart ) + # We need to unpack the dictionaries to be able to create a dataframe, as this is potentially a many-to-many mapping + gene_to_protein_groups_unpacked = [ + (key, value) + for key, values in gene_to_protein_groups.items() + for value in values + ] + gene_to_protein_groups_df = pd.DataFrame.from_records( + gene_to_protein_groups_unpacked, columns=["Gene", "Protein ID"] + ) + + protein_group_to_genes_unpacked = [ + (key, value) + for key, values in protein_group_to_genes.items() + for value in values + ] + protein_groups_to_groups_df = pd.DataFrame.from_records( + protein_group_to_genes_unpacked, columns=["Protein ID", "Gene"] + ) return { "protein_group_to_genes": pd.DataFrame( protein_group_to_genes.items(), columns=["Protein ID", "Gene"] From a936cbe4ca783b4cb7eeeecec7d1de0203edcb1c Mon Sep 17 00:00:00 2001 From: JanniRoebbecke <50395935+JanniRoebbecke@users.noreply.github.com> Date: Wed, 5 Jun 2024 17:42:57 +0200 Subject: [PATCH 03/22] Peptides (#496) * evidence import * tests * filter proteins * filter sampels * Select Peptides by group or sample for coverage visualisation (#410) * untested working, needs cleanup * linter * remove dupliacte method (merge screw-up) * add doc strings, fix first few tests (miss. args) * metadata df now optional param + adjusted asserts, fixed test * formatting * conditional metadata input for protein graphs * import logging levels, formatting * add grouping tests for peptides, add error handling * spelling, formatting * remove unused import, format * fix error msgs in tests * fix line length * Convert has_metadata to property * Use logger instead of print statement * An attempt at consistent text-casing * formatting --------- Co-authored-by: henninggaertner * tests for filter_proteins with peptide file * tests for filter_proteins with peptide file, better mock dataframe * output types * added testing that only filtered Proteins are removed from peptides_df * tests for filter_samples with peptide file * extract test that peptide filtering matches protein filtering into Method * refactor: shortened tests by integrating extracted method and mock data * exclude peptides * tests * minor fixes in test * minor fixes in test * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * match mock protein dataframe to mock peptide dataframe * extract test that peptide filtering matches protein filtering into Method * implementation * tests * tests improve error logging * tests git testing * peptide import added missing column * merge peptide filtering and outlier_detection * merge peptide transformation * implement passing of peptide data between preprocessing steps * complete implement passing of peptide data between preprocessing steps * implement overhead for new step evidence import * Fix EvidenceImport class (now the right method is called) * implement suggested changes: remove default values and correct grammar mistake * make preprocessing work without peptide_df * clean up * add cleaning of protein groups to evidence import * clean Protein IDs for peptide_import and name intensity column Intensity * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used --------- Co-authored-by: janni.roebbecke --- protzilla/constants/location_mapping.py | 1 + protzilla/constants/workflow_meta.json | 21 ++++ .../data_preprocessing/filter_proteins.py | 23 +++- .../data_preprocessing/filter_samples.py | 52 +++++++-- .../data_preprocessing/outlier_detection.py | 20 +++- .../data_preprocessing/transformation.py | 14 ++- protzilla/importing/peptide_import.py | 74 +++++++++++-- protzilla/methods/data_preprocessing.py | 39 +++---- protzilla/methods/importing.py | 16 ++- protzilla/steps.py | 6 +- .../test_filter_proteins.py | 19 +++- .../data_preprocessing/test_filter_samples.py | 67 +++++++++--- .../test_outlier_detection.py | 28 ++++- .../test_peptide_preprocessing.py | 68 +++++++++++- .../data_preprocessing/test_transformation.py | 102 +++++++++++++++++- .../importing/test_peptide_import.py | 99 ++++++++++++++++- tests/test_data/peptides/evidence-vsmall.txt | 4 + .../{ => peptides}/peptides-vsmall.txt | 0 tests/ui/test_views.py | 4 +- ui/runs/form_mapping.py | 1 + ui/runs/forms/importing.py | 31 ++++++ 21 files changed, 620 insertions(+), 69 deletions(-) create mode 100644 tests/test_data/peptides/evidence-vsmall.txt rename tests/test_data/{ => peptides}/peptides-vsmall.txt (100%) diff --git a/protzilla/constants/location_mapping.py b/protzilla/constants/location_mapping.py index 17f1998a..1205e590 100644 --- a/protzilla/constants/location_mapping.py +++ b/protzilla/constants/location_mapping.py @@ -55,6 +55,7 @@ "metadata_column_assignment", ): metadata_import.metadata_column_assignment, ("importing", "peptide_import", "peptide_import"): peptide_import.peptide_import, + ("importing", "peptide_import", "evidence_import"): peptide_import.evidence_import, ( "data_preprocessing", "filter_proteins", diff --git a/protzilla/constants/workflow_meta.json b/protzilla/constants/workflow_meta.json index a0d438b0..d8c2295a 100644 --- a/protzilla/constants/workflow_meta.json +++ b/protzilla/constants/workflow_meta.json @@ -160,6 +160,27 @@ "default": "Intensity" } } + }, + "evidence_import": { + "name": "Evidence import", + "description": "Import peptide and PTM data, from an evidence file", + "parameters": { + "file_path": { + "name": "Evidence file", + "type": "file", + "default": null + }, + "intensity_name": { + "name": "Intensity parameter (same as MS-Data)", + "type": "categorical", + "categories": [ + "LFQ intensity", + "Intensity", + "iBAQ" + ], + "default": "Intensity" + } + } } } }, diff --git a/protzilla/data_preprocessing/filter_proteins.py b/protzilla/data_preprocessing/filter_proteins.py index ae03d9ba..5e0bb7b8 100644 --- a/protzilla/data_preprocessing/filter_proteins.py +++ b/protzilla/data_preprocessing/filter_proteins.py @@ -4,14 +4,19 @@ from ..utilities.transform_dfs import long_to_wide -def by_samples_missing(protein_df: pd.DataFrame, percentage: float) -> dict: +def by_samples_missing( + protein_df: pd.DataFrame | None, + peptide_df: pd.DataFrame | None, + percentage: float = 0.5, +) -> dict: """ This function filters proteins based on the amount of samples with nan values, if the percentage of nan values is below a threshold (percentage). - :param df: the intensity dataframe that should be filtered - :param percentage: ranging from 0 to 1. Defining the relative share of samples the proteins need to be present - in in order for the protein to be kept. + :param protein_df: the protein dataframe that should be filtered + :param peptide_df: the peptide dataframe that should be filtered in accordance to the intensity dataframe (optional) + :param percentage: ranging from 0 to 1. Defining the relative share of samples the proteins need to be present in, + in order for the protein to be kept. :return: returns the filtered df as a Dataframe and a dict with a list of Protein IDs that were discarded and a list of Protein IDs that were kept """ @@ -25,9 +30,17 @@ def by_samples_missing(protein_df: pd.DataFrame, percentage: float) -> dict: filtered_proteins_list = ( transformed_df.drop(remaining_proteins_list, axis=1).columns.unique().tolist() ) - filtered_df = protein_df[(protein_df["Protein ID"].isin(remaining_proteins_list))] + filtered_df = protein_df[ + (protein_df["Protein ID"].isin(remaining_proteins_list)) + ] + filtered_peptide_df = None + if peptide_df is not None: + filtered_peptide_df = peptide_df[ + (peptide_df["Protein ID"].isin(remaining_proteins_list)) + ] return dict( protein_df=filtered_df, + peptide_df=filtered_peptide_df, filtered_proteins=filtered_proteins_list, remaining_proteins=remaining_proteins_list, ) diff --git a/protzilla/data_preprocessing/filter_samples.py b/protzilla/data_preprocessing/filter_samples.py index a37fb6ad..626aabb4 100644 --- a/protzilla/data_preprocessing/filter_samples.py +++ b/protzilla/data_preprocessing/filter_samples.py @@ -5,12 +5,15 @@ def by_protein_intensity_sum( - protein_df: pd.DataFrame, deviation_threshold: float + protein_df: pd.DataFrame, + peptide_df: pd.DataFrame | None, + deviation_threshold: float, ) -> dict: """ This function filters samples based on the sum of the protein intensities. :param protein_df: the intensity dataframe that should be filtered + :param peptide_df: the peptide dataframe that should be filtered in accordance to the intensity dataframe (optional) :param deviation_threshold: defining the maximally allowed deviation from the median (in standard deviations) to keep a sample :return: the filtered df as a Dataframe and a dict with a list of Sample IDs that have been filtered @@ -27,18 +30,32 @@ def by_protein_intensity_sum( (median + deviation_threshold * sd), ) ].index.tolist() + + filtered_df = protein_df[~(protein_df["Sample"].isin(filtered_samples_list))] + filtered_peptide_df = None + if peptide_df is not None: + filtered_peptide_df = peptide_df[ + ~(peptide_df["Sample"].isin(filtered_samples_list)) + ] + return dict( - protein_df=protein_df[~(protein_df["Sample"].isin(filtered_samples_list))], + protein_df=filtered_df, + peptide_df=filtered_peptide_df, filtered_samples=filtered_samples_list, ) -def by_protein_count(protein_df: pd.DataFrame, deviation_threshold: float) -> dict: +def by_protein_count( + protein_df: pd.DataFrame, + peptide_df: pd.DataFrame | None, + deviation_threshold: float, +) -> dict: """ This function filters samples based on their deviation of amount of proteins with a non-nan value from the median across all samples. :param protein_df: the intensity dataframe that should be filtered + :param peptide_df: the peptide dataframe that should be filtered in accordance to the intensity dataframe (optional) :param deviation_threshold: float, defining the allowed deviation (in standard deviations) from the median number of non-nan values to keep a sample :return: the filtered df as a Dataframe and a dict with a list of Sample IDs that have been filtered @@ -59,18 +76,32 @@ def by_protein_count(protein_df: pd.DataFrame, deviation_threshold: float) -> di (median + deviation_threshold * sd), ) ].index.tolist() + + filtered_df = protein_df[~(protein_df["Sample"].isin(filtered_samples_list))] + filtered_peptide_df = None + if peptide_df is not None: + filtered_peptide_df = peptide_df[ + ~(peptide_df["Sample"].isin(filtered_samples_list)) + ] + return dict( - protein_df=protein_df[~(protein_df["Sample"].isin(filtered_samples_list))], + protein_df=filtered_df, + peptide_df=filtered_peptide_df, filtered_samples=filtered_samples_list, ) -def by_proteins_missing(protein_df: pd.DataFrame, percentage: float) -> dict: +def by_proteins_missing( + protein_df: pd.DataFrame, + peptide_df: pd.DataFrame | None, + percentage: float, +) -> dict: """ This function filters samples based on the amount of proteins with nan values, if the percentage of nan values is below a threshold (percentage). :param protein_df: the intensity dataframe that should be filtered + :param peptide_df: the peptide dataframe that should be filtered in accordance to the intensity dataframe (optional) :param percentage: ranging from 0 to 1. Defining the relative share of proteins that were detected in the sample in inorder to be kept. :return: the filtered df as a Dataframe and a dict with a list of Sample IDs that have been filtered @@ -87,8 +118,17 @@ def by_proteins_missing(protein_df: pd.DataFrame, percentage: float) -> dict: filtered_samples_list = sample_protein_count[ ~sample_protein_count.ge(total_protein_count * percentage) ].index.tolist() + + filtered_df = protein_df[~(protein_df["Sample"].isin(filtered_samples_list))] + filtered_peptide_df = None + if peptide_df is not None: + filtered_peptide_df = peptide_df[ + ~(peptide_df["Sample"].isin(filtered_samples_list)) + ] + return dict( - protein_df=protein_df[~(protein_df["Sample"].isin(filtered_samples_list))], + protein_df=filtered_df, + peptide_df=filtered_peptide_df, filtered_samples=filtered_samples_list, ) diff --git a/protzilla/data_preprocessing/outlier_detection.py b/protzilla/data_preprocessing/outlier_detection.py index 85e1ed1e..be7b8eb8 100644 --- a/protzilla/data_preprocessing/outlier_detection.py +++ b/protzilla/data_preprocessing/outlier_detection.py @@ -14,7 +14,10 @@ def by_isolation_forest( - protein_df: pd.DataFrame, n_estimators: int = 100, n_jobs: int = -1 + protein_df: pd.DataFrame, + peptide_df: pd.DataFrame | None, + n_estimators: int = 100, + n_jobs: int = -1, ) -> dict: """ This function filters out outliers using a clustering @@ -59,9 +62,12 @@ def by_isolation_forest( ].index.tolist() protein_df = protein_df[~(protein_df["Sample"].isin(outlier_list))] + peptide_df = (None if peptide_df is None + else peptide_df[~(peptide_df["Sample"].isin(outlier_list))]) return dict( protein_df=protein_df, + peptide_df=peptide_df, outlier_list=outlier_list, anomaly_df=df_isolation_forest_data[["Anomaly Score", "Outlier"]], ) @@ -70,6 +76,7 @@ def by_isolation_forest( encoded as NaN. Consider preprocessing your data to remove NaN values." return dict( protein_df=protein_df, + peptide_df=peptide_df, outlier_list=None, anomaly_df=None, messages=[dict(level=logging.ERROR, msg=msg, trace=str(e))], @@ -78,6 +85,7 @@ def by_isolation_forest( def by_local_outlier_factor( protein_df: pd.DataFrame, + peptide_df: pd.DataFrame | None, number_of_neighbors: int = 20, n_jobs: int = -1, ) -> dict: @@ -117,8 +125,12 @@ def by_local_outlier_factor( outlier_list = df_lof_data[df_lof_data["Outlier"]].index.tolist() protein_df = protein_df[~(protein_df["Sample"].isin(outlier_list))] + peptide_df = (None if peptide_df is None + else peptide_df[~(peptide_df["Sample"].isin(outlier_list))]) + return dict( protein_df=protein_df, + peptide_df=peptide_df, outlier_list=outlier_list, anomaly_df=df_lof_data[["Anomaly Score", "Outlier"]], ) @@ -127,6 +139,7 @@ def by_local_outlier_factor( encoded as NaN. Consider preprocessing your data to remove NaN values." return dict( protein_df=protein_df, + peptide_df=peptide_df, outlier_list=None, anomaly_df=None, messages=[dict(level=logging.ERROR, msg=msg, trace=str(e))], @@ -135,6 +148,7 @@ def by_local_outlier_factor( def by_pca( protein_df: pd.DataFrame, + peptide_df: pd.DataFrame | None, threshold: int = 2, number_of_components: int = 3, ) -> dict: @@ -218,9 +232,12 @@ def by_pca( df_transformed_pca_data["Outlier"] ].index.tolist() protein_df = protein_df[~(protein_df["Sample"].isin(outlier_list))] + peptide_df = (None if peptide_df is None + else peptide_df[~(peptide_df["Sample"].isin(outlier_list))]) return dict( protein_df=protein_df, + peptide_df=peptide_df, outlier_list=outlier_list, pca_df=df_transformed_pca_data, explained_variance_ratio=list(pca_model.explained_variance_ratio_), @@ -231,6 +248,7 @@ def by_pca( encoded as NaN. Consider preprocessing your data to remove NaN values." return dict( protein_df=protein_df, + peptide_df=peptide_df, outlier_list=None, anomaly_df=None, messages=[dict(level=logging.ERROR, msg=msg, trace=str(e))], diff --git a/protzilla/data_preprocessing/transformation.py b/protzilla/data_preprocessing/transformation.py index 17bc78be..221b01ab 100644 --- a/protzilla/data_preprocessing/transformation.py +++ b/protzilla/data_preprocessing/transformation.py @@ -5,7 +5,7 @@ from protzilla.utilities import default_intensity_column -def by_log(protein_df: pd.DataFrame, log_base="log10") -> dict: +def by_log(protein_df: pd.DataFrame, peptide_df: pd.DataFrame | None, log_base="log10") -> dict: """ This function log-transforms intensity DataFrames. Supports log-transformation to the base @@ -13,6 +13,7 @@ def by_log(protein_df: pd.DataFrame, log_base="log10") -> dict: :param protein_df: a protein data frame in long format :type protein_df: pd.DataFrame + :param peptide_df: a peptide data frame, that is to be transformed the same way as the protein data frame :param log_base: String of the used log method "log10" (base 10) or "log2" (base 2). Default: "log10" :type log_base: str @@ -23,15 +24,24 @@ def by_log(protein_df: pd.DataFrame, log_base="log10") -> dict: """ intensity_name = default_intensity_column(protein_df) transformed_df = protein_df.copy() + transformed_peptide_df = peptide_df.copy() if peptide_df is not None else None # TODO 41 drop data when intensity is 0 and return them in dict if log_base == "log2": transformed_df[intensity_name] = np.log2(transformed_df[intensity_name]) + if transformed_peptide_df is not None: + transformed_peptide_df["Intensity"] = np.log2( + transformed_peptide_df["Intensity"] + ) elif log_base == "log10": transformed_df[intensity_name] = np.log10(transformed_df[intensity_name]) + if transformed_peptide_df is not None: + transformed_peptide_df["Intensity"] = np.log10( + transformed_peptide_df["Intensity"] + ) else: raise ValueError("Unknown log_base. Known log methods are 'log2' and 'log10'.") - return dict(protein_df=transformed_df) + return dict(protein_df=transformed_df, peptide_df=transformed_peptide_df) def by_log_plot(method_inputs, method_outputs, graph_type, group_by): diff --git a/protzilla/importing/peptide_import.py b/protzilla/importing/peptide_import.py index e9dc124c..2a393fbd 100644 --- a/protzilla/importing/peptide_import.py +++ b/protzilla/importing/peptide_import.py @@ -3,8 +3,10 @@ import pandas as pd +from protzilla.importing.ms_data_import import clean_protein_groups -def peptide_import(file_path, intensity_name) -> dict: + +def peptide_import(file_path, intensity_name, map_to_uniprot) -> dict: try: assert intensity_name in [ "Intensity", @@ -14,7 +16,6 @@ def peptide_import(file_path, intensity_name) -> dict: assert Path(file_path).is_file(), f"Cannot find Peptide File at {file_path}" except AssertionError as e: return dict( - peptide_df=None, messages=[dict(level=logging.ERROR, msg=e)], ) @@ -23,7 +24,7 @@ def peptide_import(file_path, intensity_name) -> dict: "LFQ intensity" if intensity_name == "iBAQ" else intensity_name ) - id_columns = ["Proteins", "Sequence", "PEP"] + id_columns = ["Proteins", "Sequence", "Missed cleavages", "PEP"] read = pd.read_csv( file_path, sep="\t", @@ -42,14 +43,75 @@ def peptide_import(file_path, intensity_name) -> dict: pd.concat([id_df, intensity_df], axis=1), id_vars=id_columns, var_name="Sample", - value_name=peptide_intensity_name, + value_name="Intensity", ) molten = molten.rename(columns={"Proteins": "Protein ID"}) ordered = molten[ - ["Sample", "Protein ID", "Sequence", peptide_intensity_name, "PEP"] + ["Sample", "Protein ID", "Sequence", "Intensity", "PEP"] ] ordered.dropna(subset=["Protein ID"], inplace=True) ordered.sort_values(by=["Sample", "Protein ID"], ignore_index=True, inplace=True) - return dict(peptide_df=ordered) + new_groups, filtered_proteins = clean_protein_groups( + ordered["Protein ID"].tolist(), map_to_uniprot + ) + cleaned = ordered.assign(**{"Protein ID": new_groups}) + + return dict(peptide_df=cleaned) + + +def evidence_import(file_path, intensity_name, map_to_uniprot) -> dict: + try: + assert intensity_name in [ + "Intensity", + "iBAQ", + "LFQ intensity", + ], f"Unknown intensity name: {intensity_name}" + assert Path(file_path).is_file(), f"Cannot find Peptide File at {file_path}" + except AssertionError as e: + return dict(messages=[dict(level=logging.ERROR, msg=e)]) + + # Intensity -> Intensity, iBAQ -> LFQ, LFQ -> LFQ + peptide_intensity_name = ( + "LFQ intensity" if intensity_name == "iBAQ" else intensity_name + ) + + id_columns = [ + "Experiment", + "Proteins", + "Sequence", + peptide_intensity_name, + "Modifications", + "Modified sequence", + "Missed cleavages", + "PEP", + "Raw file", + ] + + read = pd.read_csv( + file_path, + sep="\t", + low_memory=False, + na_values=["", 0], + keep_default_na=True, + ) + + df = read[id_columns] + + df = df.rename(columns={"Proteins": "Protein ID"}) + df = df.rename(columns={"Experiment": "Sample"}) + + df.dropna(subset=["Protein ID"], inplace=True) + df.sort_values( + by=["Sample", "Protein ID", "Sequence", "Modifications"], + ignore_index=True, + inplace=True, + ) + + new_groups, filtered_proteins = clean_protein_groups( + df["Protein ID"].tolist(), map_to_uniprot + ) + df = df.assign(**{"Protein ID": new_groups}) + + return dict(peptide_df=df) diff --git a/protzilla/methods/data_preprocessing.py b/protzilla/methods/data_preprocessing.py index 819378b3..0565eaf0 100644 --- a/protzilla/methods/data_preprocessing.py +++ b/protzilla/methods/data_preprocessing.py @@ -29,6 +29,7 @@ def __init__(self, *args, **kwargs): def insert_dataframes(self, steps: StepManager, inputs: dict) -> dict: inputs["protein_df"] = steps.protein_df + inputs["peptide_df"] = steps.get_step_output(Step, "peptide_df") return inputs def plot(self, inputs: dict = None): @@ -67,7 +68,7 @@ class FilterProteinsBySamplesMissing(DataPreprocessingStep): "Filter proteins based on the amount of samples with nan values" ) - input_keys = ["percentage", "protein_df"] + input_keys = ["protein_df", "peptide_df", "percentage"] def method(self, inputs): return filter_proteins.by_samples_missing(**inputs) @@ -81,7 +82,7 @@ class FilterByProteinsCount(DataPreprocessingStep): operation = "filter_samples" method_description = "Filter by protein count per sample" - input_keys = ["deviation_threshold", "protein_df"] + input_keys = ["protein_df", "peptide_df", "deviation_threshold"] def method(self, inputs): return filter_samples.by_protein_count(**inputs) @@ -97,7 +98,7 @@ class FilterSamplesByProteinsMissing(DataPreprocessingStep): "Filter samples based on the amount of proteins with nan values" ) - input_keys = ["percentage", "protein_df"] + input_keys = ["protein_df", "peptide_df", "percentage"] def method(self, inputs): return filter_samples.by_proteins_missing(**inputs) @@ -111,7 +112,7 @@ class FilterSamplesByProteinIntensitiesSum(DataPreprocessingStep): operation = "filter_samples" method_description = "Filter by sum of protein intensities per sample" - input_keys = ["deviation_threshold", "protein_df"] + input_keys = ["protein_df", "peptide_df", "deviation_threshold"] def method(self, inputs): return filter_samples.by_protein_intensity_sum(**inputs) @@ -125,7 +126,7 @@ class OutlierDetectionByPCA(DataPreprocessingStep): operation = "outlier_detection" method_description = "Detect outliers using PCA" - input_keys = ["number_of_components", "threshold", "protein_df"] + input_keys = ["protein_df", "peptide_df", "number_of_components", "threshold"] def method(self, inputs): return outlier_detection.by_pca(**inputs) @@ -139,7 +140,7 @@ class OutlierDetectionByLocalOutlierFactor(DataPreprocessingStep): operation = "outlier_detection" method_description = "Detect outliers using the local outlier factor" - input_keys = ["number_of_neighbors", "protein_df"] + input_keys = ["protein_df", "peptide_df", "number_of_neighbors"] def method(self, inputs): return outlier_detection.by_local_outlier_factor(**inputs) @@ -153,7 +154,7 @@ class OutlierDetectionByIsolationForest(DataPreprocessingStep): operation = "outlier_detection" method_description = "Detect outliers using Isolation Forest" - input_keys = ["n_estimators", "protein_df"] + input_keys = ["protein_df", "peptide_df", "n_estimators"] def method(self, inputs): return outlier_detection.by_isolation_forest(**inputs) @@ -167,7 +168,7 @@ class TransformationLog(DataPreprocessingStep): operation = "transformation" method_description = "Transform data by log" - input_keys = ["log_base", "protein_df"] + input_keys = [ "protein_df", "peptide_df", "log_base"] def method(self, inputs): return transformation.by_log(**inputs) @@ -181,7 +182,7 @@ class NormalisationByZScore(DataPreprocessingStep): operation = "normalisation" method_description = "Normalise data by Z-Score" - input_keys = ["protein_df"] + plot_input_names = ["protein_df"] def method(self, inputs): return normalisation.by_z_score(**inputs) @@ -195,7 +196,7 @@ class NormalisationByTotalSum(DataPreprocessingStep): operation = "normalisation" method_description = "Normalise data by total sum" - input_keys = ["protein_df"] + plot_input_names = ["protein_df"] def method(self, inputs): return normalisation.by_totalsum(**inputs) @@ -209,7 +210,7 @@ class NormalisationByMedian(DataPreprocessingStep): operation = "normalisation" method_description = "Normalise data by median" - input_keys = ["percentile", "protein_df"] + input_keys = ["protein_df", "percentile"] def method(self, inputs): return normalisation.by_median(**inputs) @@ -223,7 +224,7 @@ class NormalisationByReferenceProtein(DataPreprocessingStep): operation = "normalisation" method_description = "Normalise data by reference protein" - input_keys = ["reference_protein", "protein_df"] + input_keys = ["protein_df", "reference_protein"] def method(self, inputs): return normalisation.by_reference_protein(**inputs) @@ -237,7 +238,7 @@ class ImputationByMinPerDataset(DataPreprocessingStep): operation = "imputation" method_description = "Impute missing values by the minimum per dataset" - input_keys = ["shrinking_value", "protein_df"] + input_keys = ["protein_df", "shrinking_value"] def method(self, inputs): return imputation.by_min_per_dataset(**inputs) @@ -251,7 +252,7 @@ class ImputationByMinPerProtein(DataPreprocessingStep): operation = "imputation" method_description = "Impute missing values by the minimum per protein" - input_keys = ["shrinking_value", "protein_df"] + input_keys = ["protein_df", "shrinking_value"] def method(self, inputs): return imputation.by_min_per_protein(**inputs) @@ -265,7 +266,7 @@ class ImputationByMinPerSample(DataPreprocessingStep): operation = "imputation" method_description = "Impute missing values by the minimum per sample" - input_keys = ["shrinking_value", "protein_df"] + input_keys = ["protein_df", "shrinking_value"] def method(self, inputs): return imputation.by_min_per_protein(**inputs) @@ -282,7 +283,7 @@ class SimpleImputationPerProtein(DataPreprocessingStep): "sklearn.SimpleImputer class" ) - input_keys = ["strategy", "protein_df"] + input_keys = ["protein_df", "strategy"] def method(self, inputs): return imputation.by_simple_imputer(**inputs) @@ -300,7 +301,7 @@ class ImputationByKNN(DataPreprocessingStep): "the features that neither is missing are close." ) - input_keys = ["number_of_neighbours", "protein_df"] + input_keys = ["protein_df", "number_of_neighbours"] def method(self, inputs): return imputation.by_knn(**inputs) @@ -314,7 +315,7 @@ class ImputationByNormalDistributionSampling(DataPreprocessingStep): operation = "imputation" method_description = "Imputation methods include normal distribution sampling per protein or per dataset" - input_keys = ["strategy", "down_shift", "scaling_factor", "protein_df"] + input_keys = ["protein_df", "strategy", "down_shift", "scaling_factor"] def method(self, inputs): return imputation.by_normal_distribution_sampling(**inputs) @@ -328,7 +329,7 @@ class FilterPeptidesByPEPThreshold(DataPreprocessingStep): operation = "filter_peptides" method_description = "Filter by PEP-threshold" - input_keys = ["threshold", "peptide_df", "protein_df"] + input_keys = ["protein_df", "peptide_df", "threshold"] output_keys = ["protein_df", "peptide_df", "filtered_peptides"] def method(self, inputs): diff --git a/protzilla/methods/importing.py b/protzilla/methods/importing.py index ce5ec345..9f79e739 100644 --- a/protzilla/methods/importing.py +++ b/protzilla/methods/importing.py @@ -10,7 +10,7 @@ max_quant_import, ms_fragger_import, ) -from protzilla.importing.peptide_import import peptide_import +from protzilla.importing.peptide_import import peptide_import, evidence_import from protzilla.steps import Step, StepManager @@ -123,8 +123,20 @@ class PeptideImport(ImportingStep): operation = "peptide_import" method_description = "Import peptide data" - input_keys = ["file_path", "intensity_name"] + input_keys = ["file_path", "intensity_name", "map_to_uniprot"] output_keys = ["peptide_df"] def method(self, inputs): return peptide_import(**inputs) + + +class EvidenceImport(ImportingStep): + display_name = "Evidence import" + operation = "peptide_import" + method_description = "Import an evidence file" + + input_keys = ["file_path", "intensity_name", "map_to_uniprot"] + output_keys = ["peptide_df"] + + def method(self, inputs): + return evidence_import(**inputs) \ No newline at end of file diff --git a/protzilla/steps.py b/protzilla/steps.py index 4122c451..fb6cded8 100644 --- a/protzilla/steps.py +++ b/protzilla/steps.py @@ -129,6 +129,7 @@ def handle_outputs(self, outputs: dict) -> None: """ if not isinstance(outputs, dict): raise TypeError("Output of calculation is not a dictionary.") + outputs = {key: value for key, value in outputs.items() if value is not None} if not outputs: raise ValueError("Output of calculation is empty.") self.output = Output(outputs) @@ -421,7 +422,7 @@ def check_instance_identifier(step): def get_step_input( self, - step_type: type[Step], + step_type: type[Step] | list[type[Step]], input_key: str, instance_identifier: str | None = None, ): @@ -440,9 +441,10 @@ def check_instance_identifier(step): else True ) + step_type = [step_type] if not isinstance(step_type, list) else step_type for step in reversed(self.previous_steps): if ( - isinstance(step, step_type) + any(isinstance(step, st) for st in step_type) and check_instance_identifier(step) and input_key in step.inputs ): diff --git a/tests/protzilla/data_preprocessing/test_filter_proteins.py b/tests/protzilla/data_preprocessing/test_filter_proteins.py index 8a0ee579..c829b8c2 100644 --- a/tests/protzilla/data_preprocessing/test_filter_proteins.py +++ b/tests/protzilla/data_preprocessing/test_filter_proteins.py @@ -6,6 +6,10 @@ by_samples_missing, by_samples_missing_plot, ) +from tests.protzilla.data_preprocessing.test_peptide_preprocessing import ( + peptides_df, + assert_peptide_filtering_matches_protein_filtering, +) @pytest.fixture @@ -71,7 +75,7 @@ def filter_proteins_by_samples_missing_df(): def test_filter_proteins_by_missing_samples( - filter_proteins_by_samples_missing_df, show_figures + filter_proteins_by_samples_missing_df, peptides_df, show_figures ): method_output = by_samples_missing( filter_proteins_by_samples_missing_df, percentage=1.0 @@ -87,6 +91,13 @@ def test_filter_proteins_by_missing_samples( "Protein5", ] + assert_peptide_filtering_matches_protein_filtering( + method_output["protein_df"], + None, + method_output["peptide_df"], + "Protein ID", + ) + method_output = by_samples_missing( filter_proteins_by_samples_missing_df, percentage=0.5 ) @@ -95,8 +106,12 @@ def test_filter_proteins_by_missing_samples( assert method_output["filtered_proteins"] == ["Protein4", "Protein5"] method_output = by_samples_missing( - filter_proteins_by_samples_missing_df, percentage=0.0 + filter_proteins_by_samples_missing_df, peptides_df, percentage=0.0 ) method_output["filtered_proteins"] assert method_output["filtered_proteins"] == [] + + assert_peptide_filtering_matches_protein_filtering( + method_output["protein_df"], peptides_df, method_output["peptide_df"], "Protein ID" + ) diff --git a/tests/protzilla/data_preprocessing/test_filter_samples.py b/tests/protzilla/data_preprocessing/test_filter_samples.py index 63a60245..deff6d3a 100644 --- a/tests/protzilla/data_preprocessing/test_filter_samples.py +++ b/tests/protzilla/data_preprocessing/test_filter_samples.py @@ -9,6 +9,10 @@ by_proteins_missing, by_proteins_missing_plot, ) +from tests.protzilla.data_preprocessing.test_peptide_preprocessing import ( + peptides_df, + assert_peptide_filtering_matches_protein_filtering, +) @pytest.fixture @@ -29,7 +33,7 @@ def filter_samples_df(): ["Sample3", "Protein3", "Gene3", 10], ["Sample3", "Protein4", "Gene4", 20], ["Sample3", "Protein5", "Gene5", 20], - ["Sample4", "Protein1", "Gene1"], + ["Sample4", "Protein1", "Gene1", 0], ["Sample4", "Protein2", "Gene2"], ["Sample4", "Protein3", "Gene3"], ["Sample4", "Protein4", "Gene4"], @@ -47,10 +51,10 @@ def filter_samples_df(): return filter_samples_df -def test_by_proteins_missing(filter_samples_df, show_figures): +def test_by_proteins_missing(filter_samples_df, show_figures, peptides_df): method_input1 = {"protein_df": filter_samples_df, "percentage": 0.5} method_output1 = by_proteins_missing(**method_input1) - method_input2 = {"protein_df": filter_samples_df, "percentage": 0.6} + method_input2 = {"protein_df": filter_samples_df, "peptide_df": peptides_df, "percentage": 0.6} method_output2 = by_proteins_missing(**method_input2) method_input3 = {"protein_df": filter_samples_df, "percentage": 0.65} method_output3 = by_proteins_missing(**method_input3) @@ -67,7 +71,7 @@ def test_by_proteins_missing(filter_samples_df, show_figures): "Sample2", "Sample4", ], f"excluded samples do not match \ - Sample2 and Sampel4, but are {list_samples_excluded_1}" + Sample2 and Sample4, but are {list_samples_excluded_1}" assert list_samples_excluded_2 == [ "Sample2", @@ -79,13 +83,25 @@ def test_by_proteins_missing(filter_samples_df, show_figures): "Sample3", "Sample4", ], f"excluded samples do not match \ - Sample2, Sampel3 and Sample, but are {list_samples_excluded_3}" + Sample2, Sampel3 and Sample4, but are {list_samples_excluded_3}" + assert_peptide_filtering_matches_protein_filtering( + method_output1["protein_df"], + None, + method_output1["peptide_df"], + "Sample", + ) + assert_peptide_filtering_matches_protein_filtering( + method_output2["protein_df"], + peptides_df, + method_output2["peptide_df"], + "Sample", + ) -def test_filter_samples_by_protein_count(filter_samples_df, show_figures): +def test_filter_samples_by_protein_count(filter_samples_df, show_figures, peptides_df): method_input1 = {"protein_df": filter_samples_df, "deviation_threshold": 0.3} method_output1 = by_protein_count(**method_input1) - method_input2 = {"protein_df": filter_samples_df, "deviation_threshold": 1.0} + method_input2 = {"protein_df": filter_samples_df, "peptide_df": peptides_df, "deviation_threshold": 1.0} method_output2 = by_protein_count(**method_input2) list_samples_excluded_1 = method_output1["filtered_samples"] @@ -101,20 +117,32 @@ def test_filter_samples_by_protein_count(filter_samples_df, show_figures): assert list_samples_excluded_1 == [ "Sample1", - "Sample2", + "Sample4", ], f"excluded samples do not match \ - Sample1 and Sample2, but are {list_samples_excluded_1}" + Sample1 and Sample4, but are {list_samples_excluded_1}" assert list_samples_excluded_2 == [ - "Sample1" + "Sample1", ], f"excluded samples do not match \ Sample1, but are {list_samples_excluded_2}" + assert_peptide_filtering_matches_protein_filtering( + method_output1["protein_df"], + None, + method_output1["peptide_df"], + "Sample", + ) + assert_peptide_filtering_matches_protein_filtering( + method_output2["protein_df"], + peptides_df, + method_output2["peptide_df"], + "Sample", + ) -def test_filter_samples_by_protein_intensity_sum(filter_samples_df, show_figures): +def test_filter_samples_by_protein_intensity_sum(filter_samples_df, show_figures, peptides_df): method_input1 = {"protein_df": filter_samples_df, "deviation_threshold": 1.0} method_output1 = by_protein_intensity_sum(**method_input1) - method_input2 = {"protein_df": filter_samples_df, "deviation_threshold": 0.3} + method_input2 = {"protein_df": filter_samples_df, "peptide_df": peptides_df, "deviation_threshold": 0.3} method_output2 = by_protein_intensity_sum(**method_input2) list_samples_excluded_1 = method_output1["filtered_samples"] @@ -125,7 +153,7 @@ def test_filter_samples_by_protein_intensity_sum(filter_samples_df, show_figures fig.show() assert list_samples_excluded_1 == [ - "Sample3" + "Sample3", ], f"excluded samples do not match \ Sample3, but are {list_samples_excluded_1}" @@ -134,3 +162,16 @@ def test_filter_samples_by_protein_intensity_sum(filter_samples_df, show_figures "Sample4", ], f"excluded samples do not match \ Sample2 and Sample3, but are {list_samples_excluded_2}" + + assert_peptide_filtering_matches_protein_filtering( + method_output1["protein_df"], + None, + method_output1["peptide_df"], + "Sample", + ) + assert_peptide_filtering_matches_protein_filtering( + method_output2["protein_df"], + peptides_df, + method_output2["peptide_df"], + "Sample", + ) diff --git a/tests/protzilla/data_preprocessing/test_outlier_detection.py b/tests/protzilla/data_preprocessing/test_outlier_detection.py index f2306e64..e57d6f7d 100644 --- a/tests/protzilla/data_preprocessing/test_outlier_detection.py +++ b/tests/protzilla/data_preprocessing/test_outlier_detection.py @@ -10,7 +10,10 @@ by_pca, by_pca_plot, ) - +from tests.protzilla.data_preprocessing.test_peptide_preprocessing import ( + peptides_df, + assert_peptide_filtering_matches_protein_filtering, +) # TODO #21: implement actual tests for outlier detection @@ -27,6 +30,9 @@ def outlier_detection_df(): ["Sample3", "Protein1", "Gene1", 100], ["Sample3", "Protein2", "Gene2", 50], ["Sample3", "Protein3", "Gene3", 80], + ["Sample4", "Protein1", "Gene1", 100], + ["Sample4", "Protein2", "Gene2", 50], + ["Sample4", "Protein3", "Gene3", 80], ) outlier_detection_df = pd.DataFrame( @@ -59,9 +65,10 @@ def outlier_detection_df_with_nan(): return outlier_detection_df -def test_outlier_detection_with_isolation_forest(show_figures, outlier_detection_df): +def test_outlier_detection_with_isolation_forest(show_figures, outlier_detection_df, peptides_df): method_inputs = { "protein_df": outlier_detection_df, + "peptide_df": peptides_df, "n_estimators": 50, "n_jobs": -1, } @@ -70,6 +77,10 @@ def test_outlier_detection_with_isolation_forest(show_figures, outlier_detection if show_figures: fig.show() + assert_peptide_filtering_matches_protein_filtering( + method_outputs["protein_df"], peptides_df, method_outputs["peptide_df"], "Sample" + ) + def test_outlier_detection_with_isolation_forest_and_nan(outlier_detection_df_with_nan): method_inputs = { @@ -83,9 +94,10 @@ def test_outlier_detection_with_isolation_forest_and_nan(outlier_detection_df_wi assert "NaN values" in methtod_outputs["messages"][0]["msg"] -def test_outlier_detection_by_local_outlier_factor(show_figures, outlier_detection_df): +def test_outlier_detection_by_local_outlier_factor(show_figures, outlier_detection_df, peptides_df): method_inputs = { "protein_df": outlier_detection_df, + "peptide_df": peptides_df, "number_of_neighbors": 35, "n_jobs": -1, } @@ -93,6 +105,9 @@ def test_outlier_detection_by_local_outlier_factor(show_figures, outlier_detecti fig = by_local_outlier_factor_plot(method_inputs, method_outputs)[0] if show_figures: fig.show() + assert_peptide_filtering_matches_protein_filtering( + method_outputs["protein_df"], peptides_df, method_outputs["peptide_df"], "Sample" + ) def test_outlier_detection_by_local_outlier_factor_and_nan( @@ -109,9 +124,10 @@ def test_outlier_detection_by_local_outlier_factor_and_nan( assert "NaN values" in method_outputs["messages"][0]["msg"] -def test_outlier_detection_with_pca(show_figures, outlier_detection_df): +def test_outlier_detection_with_pca(show_figures, outlier_detection_df, peptides_df): method_inputs = { "protein_df": outlier_detection_df, + "peptide_df": peptides_df, "threshold": 2, "number_of_components": 3, } @@ -120,6 +136,10 @@ def test_outlier_detection_with_pca(show_figures, outlier_detection_df): if show_figures: fig.show() + assert_peptide_filtering_matches_protein_filtering( + method_outputs["protein_df"], peptides_df, method_outputs["peptide_df"], "Sample" + ) + def test_outlier_detection_with_pca_and_nan(outlier_detection_df_with_nan): method_inputs = { diff --git a/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py b/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py index 40dc18e1..92aa421f 100644 --- a/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py +++ b/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py @@ -31,9 +31,75 @@ def filtered_peptides_list(): return ["AAQSTAMNR"] +@pytest.fixture +def peptides_df(): + df = pd.DataFrame( + ( + ["Sample1", "Protein1", "SEQA", 1000000, 0.00001], + ["Sample1", "Protein2", "SEQB", 2000000, 0.00002], + ["Sample1", "Protein2", "SEQC", 3000000, 0.00003], + ["Sample1", "Protein2", "SEQD", 4000000, 0.00004], + ["Sample1", "Protein3", "SEQE", 5000000, 0.00005], + ["Sample1", "Protein3", "SEQF", 6000000, 0.00006], + ["Sample1", "Protein3", "SEQG", 7000000, 0.00007], + ["Sample1", "Protein4", "SEQH", 8000000, 0.00008], + ["Sample1", "Protein5", "SEQI", 9000000, 0.00009], + ["Sample2", "Protein1", "SEQJ", 10000000, 0.0001], + ["Sample2", "Protein2", "SEQK", 11000000, 0.00011], + ["Sample2", "Protein3", "SEQL", 12000000, 0.00012], + ["Sample2", "Protein4", "SEQM", 13000000, 0.00013], + ["Sample2", "Protein5", "SEQN", 14000000, 0.00014], + ["Sample3", "Protein1", "SEQO", 15000000, 0.00015], + ["Sample3", "Protein2", "SEQP", 16000000, 0.00016], + ["Sample3", "Protein3", "SEQQ", 17000000, 0.00017], + ["Sample3", "Protein4", "SEQR", 18000000, 0.00018], + ["Sample3", "Protein5", "SEQS", 19000000, 0.00019], + ["Sample4", "Protein1", "SEQT", 20000000, 0.0002], + ["Sample4", "Protein2", "SEQU", 21000000, 0.00021], + ["Sample4", "Protein3", "SEQV", 22000000, 0.00022], + ["Sample4", "Protein4", "SEQW", 23000000, 0.00023], + ), + columns=["Sample", "Protein ID", "Sequence", "Intensity", "PEP"], + ) + + return df + + +def assert_peptide_filtering_matches_protein_filtering( + result_protein_df: pd.DataFrame, + initial_peptide_df: pd.DataFrame, + result_peptide_df: pd.DataFrame, + filtered_attribue: str, +) -> bool: + if initial_peptide_df is None: + assert ( + result_peptide_df is None + ), "Output peptide dataframe should be None, if no input peptide dataframe is provided" + else: + assert ( + result_peptide_df is not None + ), "Peptide dataframe should not be None, if an input peptide dataframe is provided" + assert ( + result_peptide_df[filtered_attribue] + .isin(result_protein_df[filtered_attribue]) + .all() + ), f"Peptide dataframe should not contain any {filtered_attribue} that were filtered out" + assert ( + initial_peptide_df[ + initial_peptide_df[filtered_attribue].isin( + result_protein_df[filtered_attribue] + ) + ] + .isin(result_peptide_df) + .all() + .all() + ), f"Peptide dataframe should contain all entry's on {filtered_attribue} that are in the filtered dataframe" + return True + + def test_pep_filter(show_figures, leftover_peptide_df, filtered_peptides_list): import_outputs = peptide_import.peptide_import( - file_path=f"{TEST_DATA_PATH}/peptides-vsmall.txt", + file_path=f"{TEST_DATA_PATH}/peptides/peptides-vsmall.txt", intensity_name="Intensity", ) diff --git a/tests/protzilla/data_preprocessing/test_transformation.py b/tests/protzilla/data_preprocessing/test_transformation.py index fab2981f..d3ae8173 100644 --- a/tests/protzilla/data_preprocessing/test_transformation.py +++ b/tests/protzilla/data_preprocessing/test_transformation.py @@ -3,6 +3,7 @@ import pytest from protzilla.data_preprocessing.transformation import by_log, by_log_plot +from tests.protzilla.data_preprocessing.test_peptide_preprocessing import peptides_df @pytest.fixture @@ -109,11 +110,78 @@ def log10_transformation_expected_df(): ) +@pytest.fixture +def log2_transformation_expected_peptide_intensities(): + return pd.Series( + [ + 19.93157, + 20.93157, + 21.51653, + 21.93157, + 22.25350, + 22.51653, + 22.73892, + 22.93157, + 23.10149, + 23.25350, + 23.39100, + 23.51653, + 23.63201, + 23.73892, + 23.83846, + 23.93157, + 24.01903, + 24.10149, + 24.17950, + 24.25350, + 24.32389, + 24.39100, + 24.45513, + ] + ) + + +@pytest.fixture +def log10_transformation_expected_peptide_intensities(): + return pd.Series( + [ + 6.00000, + 6.30103, + 6.47712, + 6.60206, + 6.69897, + 6.77815, + 6.84510, + 6.90309, + 6.95424, + 7.00000, + 7.04139, + 7.07918, + 7.11394, + 7.14613, + 7.17609, + 7.20412, + 7.23045, + 7.25527, + 7.27875, + 7.30103, + 7.32222, + 7.34242, + 7.36173, + ] + ) + + def test_log2_transformation( - show_figures, log2_transformation_df, log2_transformation_expected_df + show_figures, + log2_transformation_df, + log2_transformation_expected_df, + peptides_df, + log2_transformation_expected_peptide_intensities, ): method_inputs = { "protein_df": log2_transformation_df, + "peptide_df": peptides_df, "log_base": "log2", } method_outputs = by_log(**method_inputs) @@ -128,12 +196,24 @@ def test_log2_transformation( ), f"The results of the transformation: {result_df} \ are not equal to the expected result: {log2_transformation_expected_df}" + assert np.allclose( + method_outputs["peptide_df"]["Intensity"], + log2_transformation_expected_peptide_intensities, + rtol=1e-02, # Relative tolerance + atol=1e-04, # Absolute tolerance + ) + def test_log10_transformation( - show_figures, log10_transformation_df, log10_transformation_expected_df + show_figures, + log10_transformation_df, + log10_transformation_expected_df, + peptides_df, + log10_transformation_expected_peptide_intensities, ): method_inputs = { "protein_df": log10_transformation_df, + "peptide_df": peptides_df, "log_base": "log10", } method_output = by_log(**method_inputs) @@ -153,6 +233,24 @@ def test_log10_transformation( ), f"The results of the transformation: {result_df} \ are not equal to the expected result: {log10_transformation_expected_df}" + assert np.allclose( + method_output["peptide_df"]["Intensity"], + log10_transformation_expected_peptide_intensities, + rtol=1e-02, # Relative tolerance + atol=1e-04, # Absolute tolerance + ) + + +@pytest.mark.parametrize("log_base", ["log2", "log10"]) +def test_by_log_without_peptide_df(log2_transformation_df, log_base): + method_outputs = by_log( + log2_transformation_df, log_base=log_base + ) + + assert ( + method_outputs["peptide_df"] is None + ), "The peptide DataFrame should be None, if no input peptide DataFrame is given." + def test_log_by_0_transformation(): # TODO 41 test expected behaviour when 0 occurs in df diff --git a/tests/protzilla/importing/test_peptide_import.py b/tests/protzilla/importing/test_peptide_import.py index ac709479..12123055 100644 --- a/tests/protzilla/importing/test_peptide_import.py +++ b/tests/protzilla/importing/test_peptide_import.py @@ -1,3 +1,5 @@ +import logging + import numpy as np import pandas as pd import pytest @@ -59,13 +61,80 @@ def peptide_df(intensity_name): return peptide_df +def evidence_df(intensity_name): + # sample, protein id, sequence, intensity, pep + peptide_protein_list = ( + [ + "AD01_C1_INSOLUBLE_02", + "P36578", + "AAAAAAALQAK", + 1362600, + "Unmodified", + "_AAAAAAALQAK_", + None, + 0.05224, + "AD01_BA39-Cohort1_INSOLUBLE_02", + ], + [ + "AD06_C1_INSOLUBLE_01", + "P36578", + "AAAAAAALQAK", + 5739600, + "Unmodified", + "_AAAAAAALQAK_", + None, + 0.01578, + "AD06_BA39-Cohort1_INSOLUBLE_01", + ], + [ + "CTR17_C2_INSOLUBLE_01", + "O75822-2;O75822-3;O75822", + "AAAAAAAGDSDSWDADAFSVEDPVRK", + 13249000, + "Acetyl (Protein N-term)", + "_(Acetyl (Protein N-term))AAAAAAAGDSDSWDADAFSVEDPVRK_", + 1.00000, + 0.00000, + "CTR17_BA39-Cohort2_INSOLUBLE_01", + ], + ) + + peptide_df = pd.DataFrame( + data=peptide_protein_list, + columns=[ + "Sample", + "Protein ID", + "Sequence", + intensity_name, + "Modifications", + "Modified sequence", + "Missed cleavages", + "PEP", + "Raw file", + ], + ) + + peptide_df.sort_values( + by=["Sample", "Protein ID", "Sequence", "Modifications"], + ignore_index=True, + inplace=True, + ) + + return peptide_df + + @pytest.mark.parametrize("intensity_name", ["LFQ intensity", "Intensity"]) def test_peptide_import(intensity_name): outputs = peptide_import.peptide_import( - file_path=f"{TEST_DATA_PATH}/peptides-vsmall.txt", + file_path=f"{TEST_DATA_PATH}/peptides/peptides-vsmall.txt", intensity_name=intensity_name, ) + if "messages" in outputs and outputs["messages"]: + for message in outputs["messages"]: + if message["level"] == logging.ERROR: + assert False, message["msg"] + pd.testing.assert_frame_equal( outputs["peptide_df"], peptide_df(intensity_name), check_dtype=False ) @@ -73,10 +142,36 @@ def test_peptide_import(intensity_name): def test_peptide_import_ibaq(): outputs = peptide_import.peptide_import( - file_path=f"{TEST_DATA_PATH}/peptides-vsmall.txt", + file_path=f"{TEST_DATA_PATH}/peptides/peptides-vsmall.txt", intensity_name="iBAQ", ) pd.testing.assert_frame_equal( outputs["peptide_df"], peptide_df("LFQ intensity"), check_dtype=False ) + + +@pytest.mark.parametrize("intensity_name", ["Intensity"]) +def test_evidence_import(intensity_name): + outputs = peptide_import.evidence_import( + file_path=f"{TEST_DATA_PATH}/peptides/evidence-vsmall.txt", + intensity_name=intensity_name, + ) + + if "messages" in outputs and outputs["messages"]: + for message in outputs["messages"]: + if message["level"] == logging.ERROR: + assert False, message["msg"] + + assert np.allclose( + outputs["peptide_df"]["PEP"], + evidence_df(intensity_name)["PEP"], + rtol=1e-02, # Relative tolerance + atol=1e-04, # Absolute tolerance + ) + + pd.testing.assert_frame_equal( + outputs["peptide_df"].drop(columns=["PEP"]), + evidence_df(intensity_name).drop(columns=["PEP"]), + check_dtype=False, + ) diff --git a/tests/test_data/peptides/evidence-vsmall.txt b/tests/test_data/peptides/evidence-vsmall.txt new file mode 100644 index 00000000..eb857b76 --- /dev/null +++ b/tests/test_data/peptides/evidence-vsmall.txt @@ -0,0 +1,4 @@ +Sequence Length Modifications Modified sequence Acetyl (K) Probabilities GlyGly (K) Probabilities Oxidation (M) Probabilities Phospho (STY) Probabilities Acetyl (K) Score Diffs GlyGly (K) Score Diffs Oxidation (M) Score Diffs Phospho (STY) Score Diffs Acetyl (K) Acetyl (Protein N-term) GlyGly (K) Oxidation (M) Phospho (STY) Missed cleavages Proteins Leading proteins Leading razor protein Gene names Protein names Type Raw file Experiment MS/MS m/z Charge m/z Mass Uncalibrated - Calibrated m/z [ppm] Uncalibrated - Calibrated m/z [Da] Mass error [ppm] Mass error [Da] Uncalibrated mass error [ppm] Uncalibrated mass error [Da] Max intensity m/z 0 Retention time Retention length Calibrated retention time Calibrated retention time start Calibrated retention time finish Retention time calibration Match time difference Match m/z difference Match q-value Match score Number of data points Number of scans Number of isotopic peaks PIF Fraction of total spectrum Base peak fraction PEP MS/MS count MS/MS scan number Score Delta score Combinatorics Intensity Reverse Potential contaminant id Protein group IDs Peptide ID Mod. peptide ID MS/MS IDs Best MS/MS Acetyl (K) site IDs GlyGly (K) site IDs Oxidation (M) site IDs Phospho (STY) site IDs Taxonomy IDs +AAAAAAAGDSDSWDADAFSVEDPVRK 26 Acetyl (Protein N-term) _(Acetyl (Protein N-term))AAAAAAAGDSDSWDADAFSVEDPVRK_ 0 1 0 0 0 1 O75822-2;O75822-3;O75822 O75822-2 O75822-2 EIF3J Eukaryotic translation initiation factor 3 subunit J MULTI-MSMS CTR17_BA39-Cohort2_INSOLUBLE_01 CTR17_C2_INSOLUBLE_01 879.415588378906 3 879.068411 2634.1834 15.029 0.013211 -1.0339 -0.0009089 13.995 0.012302 879.401598818214 115.44 0.45189 116.76 116.51 116.96 1.3206 40 15 4 0 0 0 9.4578E-13 1 53321 96.903 83.448 1 13249000 0 323 0 0 0 0 -1 +AAAAAAALQAK 11 Unmodified _AAAAAAALQAK_ 0 0 0 0 0 0 P36578 P36578 P36578 RPL4 60S ribosomal protein L4 MULTI-SECPEP AD01_BA39-Cohort1_INSOLUBLE_02 AD01_C1_INSOLUBLE_02 478.218963623047 2 478.779816 955.545079 0.76017 0.00036395 -0.56584 -0.00027091 0.19433 9.3041E-05 478.779559835743 19.784 0.63164 32.073 31.777 32.409 12.289 93 40 3 0 0 0 0.052244 1 5843 54.157 31.138 1 1362600 1 1120 1 1 1 1 -1 +AAAAAAALQAK 11 Unmodified _AAAAAAALQAK_ 0 0 0 0 0 0 P36578 P36578 P36578 RPL4 60S ribosomal protein L4 MULTI-SECPEP AD06_BA39-Cohort1_INSOLUBLE_01 AD06_C1_INSOLUBLE_01 478.232604980469 2 478.779816 955.545079 0.88015 0.0004214 -0.51943 -0.00024869 0.36072 0.0001727 478.779886911335 19.656 0.28609 23.317 23.133 23.42 3.6613 34 23 2 0 0 0 0.015779 1 5995 67.277 45.989 1 5739600 2 1120 1 1 2 2 -1 \ No newline at end of file diff --git a/tests/test_data/peptides-vsmall.txt b/tests/test_data/peptides/peptides-vsmall.txt similarity index 100% rename from tests/test_data/peptides-vsmall.txt rename to tests/test_data/peptides/peptides-vsmall.txt diff --git a/tests/ui/test_views.py b/tests/ui/test_views.py index 795ffb5d..ce83b136 100644 --- a/tests/ui/test_views.py +++ b/tests/ui/test_views.py @@ -10,7 +10,7 @@ from protzilla.constants.paths import PROJECT_PATH, RUNS_PATH from protzilla.run_v2 import Run from protzilla.utilities import random_string -from ui.runs.views import active_runs, all_button_parameters +from ui.runs.views import (active_runs, all_button_parameters) def assert_response( @@ -162,4 +162,4 @@ def test_step_finished(): assert run.current_step.finished - rmtree(RUNS_PATH / run_name) + rmtree(RUNS_PATH / run_name) \ No newline at end of file diff --git a/ui/runs/form_mapping.py b/ui/runs/form_mapping.py index 13431322..178132c9 100644 --- a/ui/runs/form_mapping.py +++ b/ui/runs/form_mapping.py @@ -21,6 +21,7 @@ importing.MetadataImportMethodDiann: importing_forms.MetadataImportMethodDiannForm, importing.MetadataColumnAssignment: importing_forms.MetadataColumnAssignmentForm, importing.PeptideImport: importing_forms.PeptideImportForm, + importing.EvidenceImport: importing_forms.EvidenceImportForm, data_preprocessing.FilterProteinsBySamplesMissing: data_preprocessing_forms.FilterProteinsBySamplesMissingForm, data_preprocessing.FilterByProteinsCount: data_preprocessing_forms.FilterByProteinsCountForm, data_preprocessing.FilterSamplesByProteinsMissing: data_preprocessing_forms.FilterSamplesByProteinsMissingForm, diff --git a/ui/runs/forms/importing.py b/ui/runs/forms/importing.py index 36e6552c..fb7323bb 100644 --- a/ui/runs/forms/importing.py +++ b/ui/runs/forms/importing.py @@ -126,3 +126,34 @@ class PeptideImportForm(MethodForm): intensity_name = CustomChoiceField( choices=IntensityType, label="Intensity parameter (same as MS-Data)" ) + map_to_uniprot = CustomBooleanField( + label="Map to Uniprot IDs using Biomart (online)", required=False + ) + + def fill_form(self, run: Run) -> None: + from protzilla.methods.importing import MaxQuantImport, MsFraggerImport, DiannImport + + self.fields["intensity_name"].initial = ( + run.steps.get_step_input([MaxQuantImport, MsFraggerImport, DiannImport], "intensity_name")) + + self.fields["map_to_uniprot"].initial = ( + run.steps.get_step_input([MaxQuantImport, MsFraggerImport, DiannImport], "map_to_uniprot")) + + +class EvidenceImportForm(MethodForm): + file_path = CustomFileField(label="Evidence file") + intensity_name = CustomChoiceField( + choices=IntensityType, label="Intensity parameter" + ) + map_to_uniprot = CustomBooleanField( + label="Map to Uniprot IDs using Biomart (online)", required=False + ) + + def fill_form(self, run: Run) -> None: + from protzilla.methods.importing import MaxQuantImport, MsFraggerImport, DiannImport + + self.fields["intensity_name"].initial = ( + run.steps.get_step_input([MaxQuantImport, MsFraggerImport, DiannImport], "intensity_name")) + + self.fields["map_to_uniprot"].initial = ( + run.steps.get_step_input([MaxQuantImport, MsFraggerImport, DiannImport], "map_to_uniprot")) \ No newline at end of file From 96eb4353e0ca726e4b2de4c8d1303255f9978015 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henning=20G=C3=A4rtner?= <104069093+henninggaertner@users.noreply.github.com> Date: Fri, 7 Jun 2024 17:35:11 +0200 Subject: [PATCH 04/22] 503 bug protquantplot volcano plot scatter plot is being saved in yaml (#504) --- protzilla/methods/data_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/protzilla/methods/data_analysis.py b/protzilla/methods/data_analysis.py index 4dca2149..f74909dc 100644 --- a/protzilla/methods/data_analysis.py +++ b/protzilla/methods/data_analysis.py @@ -32,7 +32,7 @@ class PlotStep(DataAnalysisStep): def handle_outputs(self, outputs: dict): super().handle_outputs(outputs) - plots = outputs.pop("plots", []) + plots = self.output.output.pop("plots", []) self.plots = Plots(plots) From 5eb11c5f5b19aaa66e0a2a8f711197cee4c734eb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henning=20G=C3=A4rtner?= <104069093+henninggaertner@users.noreply.github.com> Date: Mon, 10 Jun 2024 16:05:00 +0200 Subject: [PATCH 05/22] Gene mapping updated (#501) --- protzilla/constants/location_mapping.py | 409 --- protzilla/constants/workflow_meta.json | 3150 ----------------- .../data_integration/database_integration.py | 21 +- protzilla/data_integration/database_query.py | 39 +- .../data_integration/enrichment_analysis.py | 37 +- .../enrichment_analysis_gsea.py | 31 +- protzilla/methods/data_integration.py | 14 +- .../data_integration/test_database_query.py | 36 +- .../test_enrichment_analysis.py | 170 +- .../enrichment_data/gene_mapping.csv | 164 + ui/runs/forms/data_integration.py | 8 +- 11 files changed, 321 insertions(+), 3758 deletions(-) delete mode 100644 protzilla/constants/location_mapping.py delete mode 100644 protzilla/constants/workflow_meta.json create mode 100644 tests/test_data/enrichment_data/gene_mapping.csv diff --git a/protzilla/constants/location_mapping.py b/protzilla/constants/location_mapping.py deleted file mode 100644 index 1205e590..00000000 --- a/protzilla/constants/location_mapping.py +++ /dev/null @@ -1,409 +0,0 @@ -from ..data_analysis import ( - classification, - clustering, - differential_expression, - dimension_reduction, - model_evaluation, - model_evaluation_plots, - plots, - protein_graphs, -) -from ..data_integration import database_integration, di_plots, enrichment_analysis -from ..data_preprocessing import ( - filter_proteins, - filter_samples, - imputation, - normalisation, - outlier_detection, - peptide_filter, - transformation, -) -from ..importing import metadata_import, ms_data_import, peptide_import - -# In this data structure, a method is associated with a location. The location is -# determined by the section, step, and method keys found in the workflow_meta -# file that correspond to the method. -method_map = { - ( - "importing", - "ms_data_import", - "max_quant_import", - ): ms_data_import.max_quant_import, - ( - "importing", - "ms_data_import", - "ms_fragger_import", - ): ms_data_import.ms_fragger_import, - ( - "importing", - "ms_data_import", - "diann_import", - ): ms_data_import.diann_import, - ( - "importing", - "metadata_import", - "metadata_import_method", - ): metadata_import.metadata_import_method, - ( - "importing", - "metadata_import", - "metadata_import_method_diann", - ): metadata_import.metadata_import_method_diann, - ( - "importing", - "metadata_import", - "metadata_column_assignment", - ): metadata_import.metadata_column_assignment, - ("importing", "peptide_import", "peptide_import"): peptide_import.peptide_import, - ("importing", "peptide_import", "evidence_import"): peptide_import.evidence_import, - ( - "data_preprocessing", - "filter_proteins", - "samples_missing_filter", - ): filter_proteins.by_samples_missing, - ( - "data_preprocessing", - "filter_samples", - "protein_intensity_sum_filter", - ): filter_samples.by_protein_intensity_sum, - ( - "data_preprocessing", - "filter_samples", - "protein_count_filter", - ): filter_samples.by_protein_count, - ( - "data_preprocessing", - "filter_samples", - "proteins_missing_filter", - ): filter_samples.by_proteins_missing, - ( - "data_preprocessing", - "outlier_detection", - "pca", - ): outlier_detection.by_pca, - ( - "data_preprocessing", - "outlier_detection", - "isolation_forest", - ): outlier_detection.by_isolation_forest, - ( - "data_preprocessing", - "outlier_detection", - "local_outlier_factor", - ): outlier_detection.by_local_outlier_factor, - ( - "data_preprocessing", - "transformation", - "log_transformation", - ): transformation.by_log, - ( - "data_preprocessing", - "normalisation", - "z_score", - ): normalisation.by_z_score, - ( - "data_preprocessing", - "normalisation", - "totalsum", - ): normalisation.by_totalsum, - ( - "data_preprocessing", - "normalisation", - "median", - ): normalisation.by_median, - ( - "data_preprocessing", - "normalisation", - "ref_protein", - ): normalisation.by_reference_protein, - ( - "data_preprocessing", - "imputation", - "knn", - ): imputation.by_knn, - ( - "data_preprocessing", - "imputation", - "simple_imputation_per_protein", - ): imputation.by_simple_imputer, - ( - "data_preprocessing", - "imputation", - "min_value_per_sample", - ): imputation.by_min_per_sample, - ( - "data_preprocessing", - "imputation", - "min_value_per_protein", - ): imputation.by_min_per_protein, - ( - "data_preprocessing", - "imputation", - "min_value_per_dataset", - ): imputation.by_min_per_dataset, - ( - "data_preprocessing", - "imputation", - "normal_distribution_sampling", - ): imputation.by_normal_distribution_sampling, - ( - "data_preprocessing", - "filter_peptides", - "pep_filter", - ): peptide_filter.by_pep_value, - ( - "data_analysis", - "differential_expression", - "anova", - ): differential_expression.anova, - ( - "data_analysis", - "differential_expression", - "t_test", - ): differential_expression.t_test, - ( - "data_analysis", - "differential_expression", - "linear_model", - ): differential_expression.linear_model, - ( - "data_analysis", - "clustering", - "k_means", - ): clustering.k_means, - ( - "data_analysis", - "clustering", - "expectation_maximisation", - ): clustering.expectation_maximisation, - ( - "data_analysis", - "clustering", - "hierarchical_agglomerative_clustering", - ): clustering.hierarchical_agglomerative_clustering, - ( - "data_analysis", - "classification", - "random_forest", - ): classification.random_forest, - ( - "data_analysis", - "classification", - "svm", - ): classification.svm, - ( - "data_analysis", - "model_evaluation", - "evaluate_classification_model", - ): model_evaluation.evaluate_classification_model, - ( - "data_analysis", - "dimension_reduction", - "t_sne", - ): dimension_reduction.t_sne, - ( - "data_analysis", - "dimension_reduction", - "umap", - ): dimension_reduction.umap, - ( - "data_analysis", - "protein_graphs", - "peptides_to_isoform", - ): protein_graphs.peptides_to_isoform, - ( - "data_analysis", - "protein_graphs", - "variation_graph", - ): protein_graphs.variation_graph, - ( - "data_integration", - "enrichment_analysis", - "GO_analysis_with_STRING", - ): enrichment_analysis.GO_analysis_with_STRING, - ( - "data_integration", - "enrichment_analysis", - "GO_analysis_with_Enrichr", - ): enrichment_analysis.GO_analysis_with_Enrichr, - ( - "data_integration", - "enrichment_analysis", - "GO_analysis_offline", - ): enrichment_analysis.GO_analysis_offline, - ( - "data_integration", - "enrichment_analysis", - "gsea", - ): enrichment_analysis.gsea, - ( - "data_integration", - "enrichment_analysis", - "gsea_preranked", - ): enrichment_analysis.gsea_preranked, - ( - "data_integration", - "database_integration", - "uniprot", - ): database_integration.add_uniprot_data, - ( - "data_integration", - "database_integration", - "gene_mapping", - ): database_integration.gene_mapping, -} - -# reversed mapping of method callable and location -location_map = {v: k for k, v in method_map.items()} - - -# In this data structure, a plot for a given method is associated with a -# location. The location is determined by the section, step, and method keys -# found in the workflow_meta file that correspond to the method. -plot_map = { - ( - "data_preprocessing", - "filter_proteins", - "samples_missing_filter", - ): filter_proteins.by_samples_missing_plot, - ( - "data_preprocessing", - "filter_samples", - "protein_intensity_sum_filter", - ): filter_samples.by_protein_intensity_sum_plot, - ( - "data_preprocessing", - "filter_samples", - "protein_count_filter", - ): filter_samples.by_protein_count_plot, - ( - "data_preprocessing", - "filter_samples", - "proteins_missing_filter", - ): filter_samples.by_proteins_missing_plot, - ( - "data_preprocessing", - "normalisation", - "z_score", - ): normalisation.by_z_score_plot, - ( - "data_preprocessing", - "normalisation", - "median", - ): normalisation.by_median_plot, - ( - "data_preprocessing", - "normalisation", - "totalsum", - ): normalisation.by_totalsum_plot, - ( - "data_preprocessing", - "normalisation", - "ref_protein", - ): normalisation.by_reference_protein_plot, - ( - "data_preprocessing", - "transformation", - "log_transformation", - ): transformation.by_log_plot, - ( - "data_preprocessing", - "imputation", - "knn", - ): imputation.by_knn_plot, - ( - "data_preprocessing", - "imputation", - "simple_imputation_per_protein", - ): imputation.by_simple_imputer_plot, - ( - "data_preprocessing", - "imputation", - "min_value_per_sample", - ): imputation.by_min_per_sample_plot, - ( - "data_preprocessing", - "imputation", - "min_value_per_protein", - ): imputation.by_min_per_protein_plot, - ( - "data_preprocessing", - "imputation", - "min_value_per_dataset", - ): imputation.by_min_per_dataset_plot, - ( - "data_preprocessing", - "imputation", - "normal_distribution_sampling", - ): imputation.by_normal_distribution_sampling_plot, - ( - "data_preprocessing", - "outlier_detection", - "pca", - ): outlier_detection.by_pca_plot, - ( - "data_preprocessing", - "outlier_detection", - "local_outlier_factor", - ): outlier_detection.by_local_outlier_factor_plot, - ( - "data_preprocessing", - "outlier_detection", - "isolation_forest", - ): outlier_detection.by_isolation_forest_plot, - ( - "data_preprocessing", - "filter_peptides", - "pep_filter", - ): peptide_filter.by_pep_value_plot, - ( - "data_analysis", - "plot", - "scatter_plot", - ): plots.scatter_plot, - ( - "data_analysis", - "plot", - "volcano", - ): plots.create_volcano_plot, - ( - "data_analysis", - "plot", - "clustergram", - ): plots.clustergram_plot, - ( - "data_analysis", - "plot", - "prot_quant", - ): plots.prot_quant_plot, - ( - "data_analysis", - "plot", - "precision_recall_curve", - ): model_evaluation_plots.precision_recall_curve_plot, - ( - "data_analysis", - "plot", - "roc_curve", - ): model_evaluation_plots.roc_curve_plot, - ( - "data_integration", - "plot", - "GO_enrichment_bar_plot", - ): di_plots.GO_enrichment_bar_plot, - ( - "data_integration", - "plot", - "GO_enrichment_dot_plot", - ): di_plots.GO_enrichment_dot_plot, - ( - "data_integration", - "plot", - "gsea_dot_plot", - ): di_plots.gsea_dot_plot, - ( - "data_integration", - "plot", - "gsea_enrichment_plot", - ): di_plots.gsea_enrichment_plot, -} diff --git a/protzilla/constants/workflow_meta.json b/protzilla/constants/workflow_meta.json deleted file mode 100644 index d8c2295a..00000000 --- a/protzilla/constants/workflow_meta.json +++ /dev/null @@ -1,3150 +0,0 @@ -{ - "importing": { - "ms_data_import": { - "max_quant_import": { - "name": "MaxQuant", - "description": "MaxQuant data import", - "parameters": { - "file_path": { - "name": "MaxQuant intensities file:", - "type": "file", - "default": null - }, - "intensity_name": { - "name": "Intensity parameter", - "type": "categorical", - "categories": [ - "LFQ intensity", - "Intensity", - "iBAQ" - ], - "default": "Intensity" - }, - "map_to_uniprot": { - "name": "Map to Uniprot IDs using Biomart (online)", - "type": "boolean", - "default": false - } - } - }, - "diann_import": { - "name": "DIA-NN", - "description": "DIA-NN data import", - "parameters": { - "file_path": { - "name": "DIA-NN intensities file:", - "type": "file", - "default": null - }, - "map_to_uniprot": { - "name": "Map to Uniprot IDs using Biomart (online)", - "type": "boolean", - "default": false - } - } - }, - "ms_fragger_import": { - "name": "MS Fragger", - "description": "MS Fragger data import", - "parameters": { - "file_path": { - "name": "MSFragger intensities file:", - "type": "file", - "default": null - }, - "intensity_name": { - "name": "intensity name", - "type": "categorical", - "categories": [ - "Intensity", - "MaxLFQ Total Intensity", - "MaxLFQ Intensity", - "Total Intensity", - "MaxLFQ Unique Intensity", - "Unique Spectral Count", - "Unique Intensity", - "Spectral Count", - "Total Spectral Count" - ], - "default": "Intensity" - }, - "map_to_uniprot": { - "name": "Map to Uniprot IDs using Biomart (online)", - "type": "boolean", - "default": false - } - } - } - }, - "metadata_import": { - "metadata_import_method": { - "name": "Metadata import", - "description": "Import metadata", - "parameters": { - "file_path": { - "name": "Metadata file:", - "type": "file", - "default": null - }, - "feature_orientation": { - "name": "Feature orientation", - "type": "categorical", - "categories": [ - "Rows (features in rows, samples in columns)", - "Columns (samples in rows, features in columns)" - ], - "default": "Columns (samples in rows, features in columns)" - } - } - }, - "metadata_import_method_diann": { - "name": "Metadata import DIA-NN", - "description": "Import metadata for run relationships of DIA-NN", - "parameters": { - "file_path": { - "name": "Run-Relationship metadata file:", - "type": "file", - "default": null - }, - "groupby_sample": { - "name": "Group replicate runs by sample using median", - "type": "boolean", - "default": false - } - } - }, - "metadata_column_assignment": { - "name": "Metadata column assignment", - "description": "Assign columns to metadata categories, repeatable for each category", - "parameters": { - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "default": null - }, - "metadata_required_column": { - "name": "Missing, but required metadata columns", - "fill": "metadata_required_columns", - "type": "categorical", - "categories": [], - "default": null - }, - "metadata_unknown_column": { - "name": "Existing, but unknown metadata columns", - "fill": "metadata_unknown_columns", - "type": "categorical", - "categories": [], - "default": null - } - } - } - }, - "peptide_import": { - "peptide_import": { - "name": "Peptide import", - "description": "Import peptide data", - "parameters": { - "file_path": { - "name": "Peptide file", - "type": "file", - "default": null - }, - "intensity_name": { - "name": "Intensity parameter (same as MS-Data)", - "type": "categorical", - "categories": [ - "LFQ intensity", - "Intensity", - "iBAQ" - ], - "default": "Intensity" - } - } - }, - "evidence_import": { - "name": "Evidence import", - "description": "Import peptide and PTM data, from an evidence file", - "parameters": { - "file_path": { - "name": "Evidence file", - "type": "file", - "default": null - }, - "intensity_name": { - "name": "Intensity parameter (same as MS-Data)", - "type": "categorical", - "categories": [ - "LFQ intensity", - "Intensity", - "iBAQ" - ], - "default": "Intensity" - } - } - } - } - }, - "data_preprocessing": { - "filter_proteins": { - "samples_missing_filter": { - "name": "Samples missing", - "description": "Filters all proteins that are contained in less than a set percentage of samples", - "parameters": { - "percentage": { - "name": "Percentage of minimum non-missing samples per protein", - "type": "numeric", - "min": 0, - "step": 0.1, - "max": 1, - "default": 0.5 - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Pie chart" - } - } - ] - } - }, - "filter_samples": { - "protein_count_filter": { - "name": "Protein Count", - "description": "Filter by protein count per sample", - "parameters": { - "deviation_threshold": { - "name": "Number of standard deviations from the median:", - "type": "numeric", - "min": 0, - "default": 2 - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Pie chart" - } - } - ] - }, - "proteins_missing_filter": { - "name": "Proteins missing", - "description": "Filters all samples that contain less than a set percentage of proteins", - "parameters": { - "percentage": { - "name": "Percentage of minimum non-missing proteins per sample", - "type": "numeric", - "min": 0, - "max": 1, - "default": 0.5 - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Pie chart" - } - } - ] - }, - "protein_intensity_sum_filter": { - "name": "Sum of intensities", - "description": "Filter by sum of protein intensities per sample", - "parameters": { - "deviation_threshold": { - "name": "Number of standard deviations from the median:", - "type": "numeric", - "min": 0, - "default": 2 - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Pie chart" - } - } - ] - } - }, - "outlier_detection": { - "pca": { - "name": "PCA", - "description": "This function filters out outliers using a PCA based approach based geometrical distance to the median and returns a list of samples that are inliers. Allows 2D (using an ellipse) or 3D (using an ellipsoid) analysis.", - "parameters": { - "threshold": { - "name": "Threshold for number of standard deviations from the median:", - "type": "numeric", - "min": 0, - "default": 2 - }, - "number_of_components": { - "name": "Number of components:", - "type": "numeric", - "min": 2, - "max": 3, - "step": 1, - "default": 3 - } - }, - "graphs": [ - {} - ] - }, - "isolation_forest": { - "name": "Isolation forest", - "description": "This function filters out outliers using a clustering isolation forest approach.", - "parameters": { - "n_estimators": { - "name": "Number of estimators:", - "type": "numeric", - "min": 1, - "step": 1, - "default": 100 - } - }, - "graphs": [ - {} - ] - }, - "local_outlier_factor": { - "name": "Local outlier factor", - "description": "This function filters out outliers using a clustering Local Outlier Factor approach based on k nearest neighbors clustering.", - "parameters": { - "number_of_neighbors": { - "name": "Number of neighbours:", - "type": "numeric", - "min": 1, - "step": 1, - "default": 20 - } - }, - "graphs": [ - {} - ] - } - }, - "transformation": { - "log_transformation": { - "name": "Log", - "description": "This function log-transforms intensity DataFrames. Supports log-transformation to the base of 2 or 10.", - "parameters": { - "log_base": { - "name": "Log transformation base:", - "type": "categorical", - "categories": [ - "log2", - "log10" - ], - "default": "log2" - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - } - } - ] - } - }, - "normalisation": { - "z_score": { - "name": "Z-Score", - "description": "A function to run the sklearn StandardScaler class on your dataframe. Normalises the data on the level of each sample. Scales the data to zero mean and unit variance. This is often also called z-score normalisation/ transformation.", - "parameters": {}, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - } - } - ] - }, - "totalsum": { - "name": "Total sum", - "description": "A function to perform normalisation using the total sum of sample intensities on your dataframe. Normalises the data on the level of each sample. Divides each intensity by the total sum of sample intensities.", - "parameters": {}, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - } - } - ] - }, - "median": { - "name": "Median", - "description": "A function to perform a quartile/percentile normalisation on your dataframe. Normalises the data on the level of each sample. Divides each intensity by the chosen intensity quartile of the respective sample. By default, the median (50%-quartile) is used.", - "parameters": { - "percentile": { - "name": "Percentile for normalisation:", - "type": "numeric", - "min": 0, - "step": 0.1, - "max": 1, - "default": 0.5 - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - } - } - ] - }, - "ref_protein": { - "name": "Protein reference", - "description": "A function to perform protein-intensity normalisation in reference to a selected protein on your dataframe. Normalises the data on the level of each sample. Divides each intensity by the intensity of the chosen reference protein in each sample. Samples where this value is zero will be removed and returned separately.", - "parameters": { - "reference_protein": { - "name": "Reference Protein:", - "type": "text", - "default": "", - "placeholder": "Enter the protein ID for the reference protein" - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - } - } - ] - } - }, - "imputation": { - "min_value_per_dataset": { - "name": "Min per dataset", - "description": "A function to impute missing values for each protein by taking into account data from the entire dataframe. Sets missing value to the smallest measured value in the dataframe. The user can also assign a shrinking factor to take a fraction of that minimum value for imputation.", - "parameters": { - "shrinking_value": { - "name": "Shrinking value:", - "type": "numeric", - "min": 0, - "max": 1, - "default": 1 - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - }, - "visual_transformation": { - "name": "Visual transformation:", - "type": "categorical", - "categories": [ - "linear", - "log10" - ], - "default": "log10" - } - }, - { - "graph_type_quantities": { - "name": "Graph type - imputed values:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Pie chart" - } - } - ] - }, - "min_value_per_protein": { - "name": "Min per protein", - "description": "A function to impute missing values for each protein by taking into account data from each protein. Sets missing value to the smallest measured value for each protein column. The user can also assign a shrinking factor to take a fraction of that minimum value for imputation. CAVE: All proteins without any values will be filtered out.", - "parameters": { - "shrinking_value": { - "name": "Shrinking value:", - "type": "numeric", - "min": 0, - "max": 1, - "default": 1 - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - }, - "visual_transformation": { - "name": "Visual transformation:", - "type": "categorical", - "categories": [ - "linear", - "log10" - ], - "default": "linear" - } - }, - { - "graph_type_quantities": { - "name": "Graph type - imputed values:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Pie chart" - } - } - ] - }, - "min_value_per_sample": { - "name": "Min per sample", - "description": "Sets missing intensity values to the smallest measured value for each sample", - "parameters": { - "shrinking_value": { - "name": "Shrinking value:", - "type": "numeric", - "min": 0, - "max": 1, - "default": 1 - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - }, - "visual_transformation": { - "name": "Visual transformation:", - "type": "categorical", - "categories": [ - "linear", - "log10" - ], - "default": "linear" - } - }, - { - "graph_type_quantities": { - "name": "Graph type - imputed values:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Pie chart" - } - } - ] - }, - "simple_imputation_per_protein": { - "name": "SimpleImputer", - "description": "Imputation methods include imputation by mean, median and mode. Implements the sklearn.SimpleImputer class", - "parameters": { - "strategy": { - "name": "Strategy:", - "type": "categorical", - "categories": [ - "mean", - "median", - "most_frequent" - ], - "default": "mean" - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - }, - "visual_transformation": { - "name": "Visual transformation:", - "type": "categorical", - "categories": [ - "linear", - "log10" - ], - "default": "linear" - } - }, - { - "graph_type_quantities": { - "name": "Graph type - imputed values:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Pie chart" - } - } - ] - }, - "knn": { - "name": "kNN", - "description": "A function to perform value imputation based on KNN (k-nearest neighbors). Imputes missing values for each sample based on intensity-wise similar samples. Two samples are close if the features that neither is missing are close.", - "parameters": { - "number_of_neighbours": { - "name": "Number of neighbours:", - "type": "numeric", - "min": 1, - "step": 1, - "default": 5 - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - }, - "visual_transformation": { - "name": "Visual transformation:", - "type": "categorical", - "categories": [ - "linear", - "log10" - ], - "default": "linear" - } - }, - { - "graph_type_quantities": { - "name": "Graph type - imputed values:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Pie chart" - } - } - ] - }, - "normal_distribution_sampling": { - "name": "Normal distribution sampling", - "description": "Imputation methods include normal distribution sampling per protein or per dataset", - "parameters": { - "strategy": { - "name": "Strategy:", - "type": "categorical", - "categories": [ - "perProtein", - "perDataset" - ], - "default": "perProtein" - }, - "down_shift": { - "name": "Downshift:", - "type": "numeric", - "min": -10, - "max": 10, - "default": -1 - }, - "scaling_factor": { - "name": "Scaling factor:", - "type": "numeric", - "min": 0, - "max": 1, - "default": 1 - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Boxplot", - "Histogram" - ], - "default": "Boxplot" - }, - "group_by": { - "name": "Group by:", - "type": "categorical", - "categories": [ - "None", - "Sample", - "Protein ID" - ], - "default": "None" - }, - "visual_transformation": { - "name": "Visual transformation:", - "type": "categorical", - "categories": [ - "linear", - "log10" - ], - "default": "linear" - } - }, - { - "graph_type_quantities": { - "name": "Graph type - imputed values:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Pie chart" - } - } - ] - } - }, - "filter_peptides": { - "pep_filter": { - "name": "PEP threshold", - "description": "Filter by PEP-threshold", - "parameters": { - "threshold": { - "name": "Threshold value for PEP", - "type": "numeric", - "min": 0, - "default": 0 - }, - "peptide_df": { - "type": "empty", - "name": "peptide_df" - } - }, - "graphs": [ - { - "graph_type": { - "name": "Graph type:", - "type": "categorical", - "categories": [ - "Bar chart", - "Pie chart" - ], - "default": "Bar chart" - } - } - ] - } - }, - "output_name": "preprocessed_data" - }, - "data_analysis": { - "differential_expression": { - "anova": { - "name": "ANOVA", - "description": "A function that uses ANOVA to test the difference between two or more groups defined in the clinical data. The ANOVA test is conducted on the level of each protein. The p-values are corrected for multiple testing.", - "parameters": { - "intensity_df": { - "name": "Input dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "multiple_testing_correction_method": { - "name": "Multiple testing correction method:", - "type": "categorical", - "categories": [ - "Benjamini-Hochberg", - "Bonferroni" - ], - "default": "Benjamini-Hochberg" - }, - "alpha": { - "name": "Error rate (alpha)", - "type": "numeric", - "min": 0, - "max": 1, - "default": 0.05 - }, - "log_base": { - "name": "Base of log transformation", - "type": "categorical", - "categories": [ - "None", - "log2", - "log10" - ], - "default": "log2" - }, - "grouping": { - "name": "Grouping:", - "type": "categorical", - "fill": "metadata_non_sample_columns", - "fill_dynamic": [ - "selected_groups" - ], - "categories": [], - "default": "None" - }, - "selected_groups": { - "name": "Selected groups (add / remove while holding CTRL-Key):", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": "None", - "multiple": true, - "default_select_all": true - }, - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "default": null - } - } - }, - "t_test": { - "name": "t-Test", - "description": "A function to conduct a two sample t-test between groups defined in the clinical data. The t-test is conducted on the level of each protein. The p-values are corrected for multiple testing. The fold change is calculated by group2/group1.", - "parameters": { - "ttest_type": { - "name": "t-Test type:", - "type": "categorical", - "categories": [ - "Student's t-Test", - "Welch's t-Test" - ], - "default": "Welch's t-Test" - }, - "intensity_df": { - "name": "Input dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "multiple_testing_correction_method": { - "name": "Multiple testing correction method:", - "type": "categorical", - "categories": [ - "Benjamini-Hochberg", - "Bonferroni" - ], - "default": "Benjamini-Hochberg" - }, - "alpha": { - "name": "Error rate (alpha)", - "type": "numeric", - "min": 0, - "step": 0.05, - "max": 1, - "default": 0.05 - }, - "log_base": { - "name": "Base of log transformation", - "type": "categorical", - "categories": [ - "None", - "log2", - "log10" - ], - "default": "log2" - }, - "grouping": { - "name": "Grouping", - "type": "categorical", - "fill": "metadata_non_sample_columns", - "fill_dynamic": [ - "group1", - "group2" - ], - "categories": [], - "default": null - }, - "group1": { - "name": "Group 1", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "group2": { - "name": "Group 2", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "default": null - } - } - }, - "linear_model": { - "name": "Linear model", - "description": "A function to fit a linear model using ordinary least squares for each protein. The linear model fits the protein intensities on Y axis and the grouping on X for group1 X=-1 and group2 X=1. The p-values are corrected for multiple testing.", - "parameters": { - "intensity_df": { - "name": "Input dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "multiple_testing_correction_method": { - "name": "Multiple testing correction method:", - "type": "categorical", - "categories": [ - "Benjamini-Hochberg", - "Bonferroni" - ], - "default": "Benjamini-Hochberg" - }, - "alpha": { - "name": "Error rate (alpha)", - "type": "numeric", - "min": 0, - "step": 0.05, - "max": 1, - "default": 0.05 - }, - "log_base": { - "name": "Base of log transformation", - "type": "categorical", - "categories": [ - "None", - "log2", - "log10" - ], - "default": "log2" - }, - "grouping": { - "name": "Grouping", - "type": "categorical", - "fill": "metadata_non_sample_columns", - "fill_dynamic": [ - "group1", - "group2" - ], - "categories": [], - "default": null - }, - "group1": { - "name": "Group 1", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "group2": { - "name": "Group 2", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "default": null - } - } - } - }, - "plot": { - "volcano": { - "name": "Volcano plot", - "description": "", - "parameters": { - "input_dict": { - "name": "Input data dict (generated by t-Test or Linear Model Diff Exp)", - "type": "multi_named_output", - "mapping": { - "p_values": "corrected_p_values_df", - "log2_fc": "log2_fold_change_df", - "alpha": "corrected_alpha", - "group1": "group1", - "group2": "group2" - } - }, - "fc_threshold": { - "name": "log2 fold change threshold", - "type": "numeric", - "min": 0, - "default": 0 - }, - "proteins_of_interest_input": { - "name": "proteins of interest df to highlight in plot", - "select_from": [ - "input_dict" - ], - "type": "named_output_v2", - "default": [ - null, - null - ], - "fill_dynamic": [ - "proteins_of_interest" - ] - }, - "proteins_of_interest": { - "type": "categorical", - "fill": "protein_ids", - "categories": [], - "default": null, - "multiple": true - } - } - }, - "scatter_plot": { - "name": "Scatter plot", - "description": "Creates a scatter plot from data. This requires a dimension reduction method to be run first, as the input dataframe should contain only 2 or 3 columns.", - "parameters": { - "input_df": { - "name": "Choose dataframe to be plotted", - "type": "named_output", - "default": [ - null, - null - ] - }, - "color_df": { - "name": "Choose a dataframe to assign color to marks (optional)", - "type": "named_output", - "default": [ - null, - null - ], - "optional": true - } - } - }, - "clustergram": { - "name": "Clustergram", - "description": "Creates a clustergram from data", - "parameters": { - "input_df": { - "name": "Input dataframe: choose dataframe to be plotted", - "type": "named_output", - "default": [ - null, - null - ] - }, - "sample_group_df": { - "name": "Grouping dataframe: choose a dataframe that assigns the samples present in the input dataframe to a group they belong (optional)", - "type": "named_output", - "default": [ - null, - null - ], - "optional": true - }, - "flip_axes": { - "name": "Do you want to flip the orientation of the heatmap's axes?", - "type": "categorical", - "categories": [ - "yes", - "no" - ], - "default": "no" - } - } - }, - "prot_quant": { - "name": "Prot Quant", - "description": "Creates a line chart for intensity across samples for proteingroups", - "parameters": { - "input_df": { - "name": "Input dataframe: choose dataframe to be plotted", - "type": "named_output", - "default": [ - null, - null - ], - "fill_dynamic": [ - "protein_group" - ] - }, - "protein_group": { - "name": "Protein group: choose highlighted protein group", - "type": "categorical", - "fill": "protein_ids", - "categories": [], - "default": null - }, - "similarity_measure": { - "name": "Similarity Measurement: choose how to compare protein groups", - "type": "categorical", - "categories": [ - "euclidean distance", - "cosine similarity" - ], - "default": "euclidean_distance" - }, - "similarity": { - "name": "Similarity", - "type": "numeric", - "min": -1, - "max": 999, - "step": 0.1, - "default": 1 - } - } - }, - "precision_recall_curve": { - "name": "Precision recall curve", - "description": "The precision-recall curve shows the tradeoff between precision and recall for different threshold", - "parameters": { - "input_dict": { - "name": "Input classification model (generated by Random forest or Support Vector Machine(SVM))", - "type": "multi_named_output", - "mapping": { - "model": "model", - "input_test_df": "X_test_df", - "labels_test_df": "y_test_df" - } - }, - "plot_title": { - "name": "Title of the plot (optional)", - "type": "text" - } - } - }, - "roc_curve": { - "name": "ROC curve", - "description": "The ROC curve helps assess the model's ability to discriminate between positive and negative classes and determine an optimal threshold for decision making", - "parameters": { - "input_dict": { - "name": "Input classification model (generated by Random forest or Support Vector Machine(SVM))", - "type": "multi_named_output", - "mapping": { - "model": "model", - "input_test_df": "X_test_df", - "labels_test_df": "y_test_df" - } - }, - "plot_title": { - "name": "Title of the plot (optional)", - "type": "text" - } - } - } - }, - "clustering": { - "k_means": { - "name": "K-means", - "description": "Partitions a number of samples in k clusters using k-means", - "parameters": { - "input_df": { - "name": "Choose input dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "labels_column": { - "name": "Choose labels column from metadata", - "type": "categorical", - "fill": "metadata_non_sample_columns", - "fill_dynamic": [ - "positive_label" - ], - "categories": [], - "default": null - }, - "positive_label": { - "name": "Choose positive class", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "model_selection": { - "name": "Choose strategy to perform parameter fine-tuning", - "type": "categorical_dynamic", - "categories": [ - "Grid search", - "Randomized search", - "Manual" - ], - "default": "Grid search", - "dynamic_parameters": { - "Grid search": [ - "model_selection_scoring" - ], - "Randomized search": [ - "model_selection_scoring" - ] - } - }, - "model_selection_scoring": { - "name": "Select a scoring for identifying the best estimator following a grid search", - "type": "categorical", - "categories": [ - "adjusted_rand_score", - "completeness_score", - "fowlkes_mallows_score", - "homogeneity_score", - "mutual_info_score", - "normalized_mutual_info_score", - "rand_score", - "v_measure_score" - ], - "default": "adjusted_rand_score", - "dynamic": true - }, - "scoring": { - "name": "score", - "type": "categorical", - "categories": [ - "adjusted_rand_score", - "completeness_score", - "fowlkes_mallows_score", - "homogeneity_score", - "mutual_info_score", - "normalized_mutual_info_score", - "rand_score", - "v_measure_score" - ], - "default": "adjusted_rand_score", - "multiple": true - }, - "n_clusters": { - "name": "Number of clusters to find", - "type": "numeric", - "min": 1, - "step": 1, - "default": 8, - "multiple": true - }, - "random_state": { - "name": "Seed for centroid initialisation", - "type": "numeric", - "min": 0, - "max": 4294967295, - "step": 1, - "default": 6, - "multiple": true - }, - "init_centroid_strategy": { - "name": "Method for initialisation of centroids", - "type": "categorical", - "categories": [ - "k-means++", - "random" - ], - "default": "random", - "multiple": true - }, - "n_init": { - "name": "Number of times the k-means algorithm is run with different centroid seeds", - "type": "numeric", - "min": 1, - "step": 1, - "default": 10, - "multiple": true - }, - "max_iter": { - "name": "Maximum number of iterations of the k-means algorithm for a single run.", - "type": "numeric", - "min": 1, - "step": 1, - "default": 300, - "multiple": true - }, - "tolerance": { - "name": "Relative tolerance with regards to Frobenius norm", - "type": "numeric", - "min": 0, - "default": 1e-4, - "multiple": true - }, - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "default": null - } - } - }, - "expectation_maximisation": { - "name": "Expectation-maximization (EM) algorithm for fitting mixture-of-Gaussian models", - "description": "", - "parameters": { - "input_df": { - "name": "Choose input dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "labels_column": { - "name": "Choose labels column from metadata", - "type": "categorical", - "fill": "metadata_non_sample_columns", - "fill_dynamic": [ - "positive_label" - ], - "categories": [], - "default": null - }, - "positive_label": { - "name": "Choose positive class", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "model_selection": { - "name": "Choose strategy to perform parameter fine-tuning", - "type": "categorical_dynamic", - "categories": [ - "Grid search", - "Randomized search", - "Manual" - ], - "default": "Grid search", - "dynamic_parameters": { - "Grid search": [ - "model_selection_scoring" - ], - "Randomized search": [ - "model_selection_scoring" - ] - } - }, - "model_selection_scoring": { - "name": "Select a scoring for identifying the best estimator following a grid search", - "type": "categorical", - "categories": [ - "adjusted_rand_score", - "completeness_score", - "fowlkes_mallows_score", - "homogeneity_score", - "mutual_info_score", - "normalized_mutual_info_score", - "rand_score", - "v_measure_score" - ], - "default": "adjusted_rand_score", - "dynamic": true - }, - "scoring": { - "name": "score", - "type": "categorical", - "categories": [ - "adjusted_rand_score", - "completeness_score", - "fowlkes_mallows_score", - "homogeneity_score", - "mutual_info_score", - "normalized_mutual_info_score", - "rand_score", - "v_measure_score" - ], - "default": "adjusted_rand_score", - "multiple": true - }, - "n_components": { - "name": "The number of mixture components", - "type": "numeric", - "default": 1, - "multiple": true - }, - "reg_covar": { - "name": "Non-negative regularization added to the diagonal of covariance", - "type": "numeric", - "default": 1e-6, - "multiple": true - }, - "covariance_type": { - "name": "Type of covariance", - "type": "categorical", - "categories": [ - "full", - "tied", - "diag", - "spherical" - ], - "default": "full", - "multiple": true - }, - "init_params": { - "name": "The method used to initialize the weights, the means and the precisions.", - "type": "categorical", - "categories": [ - "kmeans", - "k-means++", - "random", - "random_from_data" - ], - "default": "kmeans", - "multiple": true - }, - "max_iter": { - "name": "The number of iterations to perform", - "type": "numeric", - "default": 100, - "multiple": true - }, - "random_state": { - "name": "Seed for random number generation", - "type": "numeric", - "min": 0, - "max": 4294967295, - "step": 1, - "default": 6 - }, - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "default": null - } - } - }, - "hierarchical_agglomerative_clustering": { - "name": "Hierarchical agglomerative clustering", - "description": "Performs hierarchical clustering utilizing a bottom-up approach", - "parameters": { - "input_df": { - "name": "Choose input dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "labels_column": { - "name": "Choose labels column from metadata", - "type": "categorical", - "fill": "metadata_non_sample_columns", - "fill_dynamic": [ - "positive_label" - ], - "categories": [], - "default": null - }, - "positive_label": { - "name": "Choose positive class", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "model_selection": { - "name": "Choose strategy to perform parameter fine-tuning", - "type": "categorical_dynamic", - "categories": [ - "Grid search", - "Randomized search", - "Manual" - ], - "default": "Grid search", - "dynamic_parameters": { - "Grid search": [ - "model_selection_scoring" - ], - "Randomized search": [ - "model_selection_scoring" - ] - } - }, - "model_selection_scoring": { - "name": "Select a scoring for identifying the best estimator following a grid search", - "type": "categorical", - "categories": [ - "adjusted_rand_score", - "completeness_score", - "fowlkes_mallows_score", - "homogeneity_score", - "mutual_info_score", - "normalized_mutual_info_score", - "rand_score", - "v_measure_score" - ], - "default": "adjusted_rand_score", - "dynamic": true - }, - "scoring": { - "name": "score", - "type": "categorical", - "categories": [ - "adjusted_rand_score", - "completeness_score", - "fowlkes_mallows_score", - "homogeneity_score", - "mutual_info_score", - "normalized_mutual_info_score", - "rand_score", - "v_measure_score" - ], - "default": "adjusted_rand_score", - "multiple": true - }, - "n_clusters": { - "name": "Number of clusters to find", - "type": "numeric", - "min": 1, - "step": 1, - "default": 2, - "multiple": true - }, - "metric": { - "name": "Distance metric", - "type": "categorical", - "categories": [ - "euclidean", - "manhattan", - "cosine", - "l1", - "l2" - ], - "default": "euclidean", - "multiple": true - }, - "linkage": { - "name": "The linkage criterion to use in order to to determine the distance to use between sets of observation", - "type": "categorical", - "categories": [ - "ward", - "complete", - "average", - "single" - ], - "default": "ward", - "multiple": true - }, - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "default": null - } - } - } - }, - "classification": { - "random_forest": { - "name": "Random forest", - "description": "A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting.", - "parameters": { - "input_df": { - "name": "Choose input dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "labels_column": { - "name": "Choose labels column from metadata", - "type": "categorical", - "fill": "metadata_non_sample_columns", - "fill_dynamic": [ - "positive_label" - ], - "categories": [], - "default": null - }, - "positive_label": { - "name": "Choose positive class", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "test_size": { - "name": "Test size", - "type": "numeric", - "default": 0.20 - }, - "split_stratify": { - "name": "Split data in a stratified fashion", - "type": "categorical", - "categories": [ - "yes", - "no" - ], - "default": "yes" - }, - "validation_strategy": { - "name": "Validation strategy", - "type": "categorical_dynamic", - "categories": [ - "K-Fold", - "Repeated K-Fold", - "Stratified K-Fold", - "Leave one out", - "Leave p out", - "Manual" - ], - "default": "K-Fold", - "dynamic_parameters": { - "K-Fold": [ - "n_splits", - "shuffle" - ], - "Stratified K-Fold": [ - "n_splits", - "shuffle" - ], - "Repeated K-Fold": [ - "n_splits", - "n_repeats", - "random_state_cv" - ], - "Leave p out": [ - "p_samples" - ], - "Manual": [ - "train_val_split" - ] - } - }, - "train_val_split": { - "name": "Choose the size of the validation data set (you can either enter the absolute number of validation samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", - "type": "numeric", - "dynamic": true, - "default": 0.20 - }, - "n_splits": { - "name": "Number of folds", - "type": "numeric", - "min": 2, - "dynamic": true, - "default": 5 - }, - "shuffle": { - "name": "Whether to shuffle the data before splitting into batches", - "type": "categorical_dynamic", - "categories": [ - "yes", - "no" - ], - "default": "yes", - "dynamic": true, - "dynamic_parameters": { - "yes": [ - "random_state_cv" - ] - } - }, - "n_repeats": { - "name": "Number of times cross-validator needs to be repeated", - "type": "numeric", - "dynamic": true, - "default": 10 - }, - "random_state_cv": { - "name": "Seed for random number generation", - "type": "numeric", - "min": 0, - "max": 4294967295, - "step": 1, - "default": 42, - "dynamic": true - }, - "p_samples": { - "name": "Size of the test sets", - "type": "numeric", - "dynamic": true, - "default": 1 - }, - "scoring": { - "name": "score", - "type": "categorical", - "categories": [ - "accuracy", - "precision", - "recall", - "matthews_corrcoef" - ], - "default": "accuracy", - "multiple": true - }, - "model_selection": { - "name": "Choose strategy to perform parameter fine-tuning", - "type": "categorical_dynamic", - "categories": [ - "Grid search", - "Randomized search", - "Manual" - ], - "default": "Grid search", - "dynamic_parameters": { - "Grid search": [ - "model_selection_scoring" - ], - "Randomized search": [ - "model_selection_scoring" - ] - } - }, - "model_selection_scoring": { - "name": "Select a scoring for identifying the best estimator following a grid search", - "type": "categorical", - "categories": [ - "accuracy", - "precision", - "recall", - "matthews_corrcoef" - ], - "default": "accuracy", - "dynamic": true - }, - "n_estimators": { - "name": "The number of trees in the forest.", - "type": "numeric", - "default": 100, - "multiple": true - }, - "criterion": { - "name": "The function to measure the quality of a split", - "type": "categorical", - "categories": [ - "gini", - "log_loss", - "entropy" - ], - "default": "gini", - "multiple": true - }, - "max_depth": { - "name": "The maximum depth of the tree.", - "type": "numeric", - "default": 1, - "multiple": true - }, - "random_state": { - "name": "Seed for random number generation", - "type": "numeric", - "min": 0, - "max": 4294967295, - "step": 1, - "default": 6 - }, - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "default": null - } - } - }, - "svm": { - "name": "Support vector machine (SVM)", - "description": "", - "parameters": { - "input_df": { - "name": "Choose input dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "labels_column": { - "name": "Choose labels column from metadata", - "type": "categorical", - "fill": "metadata_non_sample_columns", - "categories": [], - "default": null - }, - "test_size": { - "name": "Test size", - "type": "numeric", - "default": 0.20 - }, - "split_stratify": { - "name": "Split data in a stratified fashion", - "type": "categorical", - "categories": [ - "yes", - "no" - ], - "default": "yes" - }, - "positive_label": { - "name": "Choose positive class", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "validation_strategy": { - "name": "Validation strategy", - "type": "categorical_dynamic", - "categories": [ - "K-Fold", - "Repeated K-Fold", - "Stratified K-Fold", - "Leave one out", - "Leave p out", - "Manual" - ], - "default": "K-Fold", - "dynamic_parameters": { - "K-Fold": [ - "n_splits", - "shuffle" - ], - "Stratified K-Fold": [ - "n_splits", - "shuffle" - ], - "Repeated K-Fold": [ - "n_splits", - "n_repeats", - "random_state_cv" - ], - "Leave p out": [ - "p_samples" - ], - "Manual": [ - "test_size" - ] - } - }, - "train_val_split": { - "name": "Choose the size of the validation data set (you can either enter the absolute number of validation samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", - "type": "numeric", - "dynamic": true, - "default": 0.20 - }, - "n_splits": { - "name": "Number of folds", - "type": "numeric", - "min": 2, - "dynamic": true, - "default": 5 - }, - "shuffle": { - "name": "Whether to shuffle the data before splitting into batches", - "type": "categorical_dynamic", - "categories": [ - "yes", - "no" - ], - "default": "yes", - "dynamic": true, - "dynamic_parameters": { - "True": [ - "random_state_cv" - ] - } - }, - "n_repeats": { - "name": "Number of times cross-validator needs to be repeated", - "type": "numeric", - "dynamic": true, - "default": 10 - }, - "random_state_cv": { - "name": "Seed for random number generation", - "type": "numeric", - "min": 0, - "max": 4294967295, - "step": 1, - "default": 42, - "dynamic": true - }, - "p_samples": { - "name": "Size of the test sets", - "type": "numeric", - "dynamic": true, - "default": 1 - }, - "scoring": { - "name": "score", - "type": "categorical", - "categories": [ - "accuracy", - "precision", - "recall", - "matthews_corrcoef" - ], - "default": "accuracy", - "multiple": true - }, - "model_selection": { - "name": "Choose strategy to perform parameter fine-tuning", - "type": "categorical_dynamic", - "categories": [ - "Grid search", - "Randomized search", - "Manual" - ], - "default": "Grid search", - "dynamic_parameters": { - "Grid search": [ - "model_selection_scoring" - ], - "Randomized search": [ - "model_selection_scoring" - ] - } - }, - "model_selection_scoring": { - "name": "Select a scoring for identifying the best estimator following a grid search", - "type": "categorical", - "categories": [ - "accuracy", - "precision", - "recall", - "matthews_corrcoef" - ], - "default": "accuracy", - "dynamic": true - }, - "C": { - "name": "C: regularization parameter (the strength of the regularization is inversely proportional to C)", - "type": "numeric", - "min": 0.0, - "default": 1.0, - "multiple": true - }, - "kernel": { - "name": "Kernel type", - "type": "categorical", - "categories": [ - "linear", - "poly", - "rbf", - "sigmoid", - "precomputed" - ], - "default": "linear", - "multiple": true - }, - "tolerance": { - "name": "Tolerance for stopping criterion", - "type": "numeric", - "min": 0, - "default": 1e-4 - }, - "random_state": { - "name": "Seed for random number generation", - "type": "numeric", - "min": 0, - "max": 4294967295, - "step": 1, - "default": 6 - }, - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "default": null - } - } - } - }, - "model_evaluation": { - "evaluate_classification_model": { - "name": "Evaluation of classification models", - "description": "Assessing an already trained classification model on separate testing data using widely used scoring metrics", - "parameters": { - "input_dict": { - "name": "Input classification model (generated by Random forest or Support Vector Machine(SVM))", - "type": "multi_named_output", - "mapping": { - "model": "model", - "input_test_df": "X_test_df", - "labels_test_df": "y_test_df" - } - }, - "scoring": { - "name": "Choose metric to measure the model's performance", - "type": "categorical", - "categories": [ - "accuracy", - "precision", - "recall", - "matthews_corrcoef" - ], - "default": "accuracy", - "multiple": true - } - } - } - }, - "dimension_reduction": { - "t_sne": { - "name": "t-SNE", - "description": "Dimension reduction of a dataframe using t-SNE", - "parameters": { - "input_df": { - "name": "Choose input dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "n_components": { - "name": "Dimension of the embedded space", - "type": "numeric", - "min": 1, - "step": 1, - "default": 2 - }, - "perplexity": { - "name": "Perplexity", - "type": "numeric", - "min": 5.0, - "max": 50.0, - "default": 30.0 - }, - "metric": { - "name": "Distance metric", - "type": "categorical", - "categories": [ - "euclidean", - "manhattan", - "cosine", - "haversine" - ], - "default": "euclidean" - }, - "random_state": { - "name": "Seed for random number generation", - "type": "numeric", - "min": 0, - "max": 4294967295, - "step": 1, - "default": 6 - }, - "n_iter": { - "name": "Maximum number of iterations for the optimization", - "type": "numeric", - "min": 250, - "step": 1, - "default": 1000 - }, - "n_iter_without_progress": { - "name": "Maximum number of iterations without progress before aborting the optimization ", - "type": "numeric", - "min": 250, - "step": 1, - "default": 300 - } - } - }, - "umap": { - "name": "UMAP", - "description": "Dimension reduction of a dataframe using UMAP", - "parameters": { - "input_df": { - "name": "Choose input dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "n_neighbors": { - "name": "Number of neighbors", - "type": "numeric", - "min": 2, - "max": 100, - "step": 1, - "default": 15 - }, - "n_components": { - "name": "Number of components", - "type": "numeric", - "min": 2, - "max": 100, - "step": 1, - "default": 2 - }, - "min_dist": { - "name": "The effective minimum distance between embedded points.", - "type": "numeric", - "min": 0.1, - "step": 0.1, - "default": 0.1 - }, - "metric": { - "name": "Distance metric", - "type": "categorical", - "categories": [ - "euclidean", - "manhattan", - "cosine", - "haversine" - ], - "default": "euclidean" - }, - "random_state": { - "name": "Seed for random number generation", - "type": "numeric", - "min": 0, - "max": 4294967295, - "step": 1, - "default": 42 - } - } - } - }, - "protein_graphs": { - "peptides_to_isoform": { - "name": "Peptide to isoform", - "description": "Create a variation graph (.graphml) for a protein and map the peptides onto the graph for coverage visualisation. The protein data will be downloaded from https://rest.uniprot.org/uniprotkb/.txt. Only `Variant`-features are included in the graph. This, currently, only works with Uniprot-IDs and while you are online.", - "parameters": { - "protein_id": { - "name": "Protein ID", - "type": "text", - "default": "", - "placeholder": "Enter the Uniprot-ID of the protein" - }, - "run_name": { - "type": "empty", - "name": "run_name", - "default": null - }, - "peptide_df": { - "type": "empty", - "name": "peptide_df", - "default": null - }, - "k": { - "name": "k-mer length", - "type": "numeric", - "min": 1, - "step": 1, - "default": 5 - }, - "allowed_mismatches": { - "name": "Number of allowed mismatched amino acids per peptide. For many allowed mismatches, this can take a long time.", - "type": "numeric", - "min": 0, - "step": 1, - "default": 2 - }, - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "condition": "metadata", - "default": null - }, - "grouping": { - "name": "Category by which peptides should be selected.", - "type": "categorical", - "condition": "metadata", - "fill": "metadata_columns", - "fill_dynamic": [ - "selected_groups" - ], - "categories": [], - "default": "None" - }, - "selected_groups": { - "name": "Select group(s) from which peptides should be selected for mapping to the protein graph.", - "type": "categorical", - "condition": "metadata", - "fill": "metadata_column_data", - "categories": [], - "default": "None", - "multiple": true, - "default_select_all": false - } - } - }, - "variation_graph": { - "name": "Protein variation graph", - "description": "Create a variation graph (.graphml) for a protein, including variation-features. The protein data will be downloaded from https://rest.uniprot.org/uniprotkb/.txt. This, currently, only works with Uniprot-IDs and while you are online.", - "parameters": { - "protein_id": { - "name": "Protein ID", - "type": "text", - "default": "", - "placeholder": "Enter the Uniprot-ID of the protein" - }, - "run_name": { - "type": "empty", - "name": "run_name", - "default": null - } - } - } - } - }, - "data_integration": { - "enrichment_analysis": { - "GO_analysis_with_STRING": { - "name": "GO analysis with STRING", - "description": "Online GO analysis using STRING API", - "parameters": { - "proteins_df": { - "name": "Dataframe with protein IDs and direction of expression change column (e.g. log2FC)", - "type": "named_output", - "default": [ - null, - null - ], - "fill_dynamic": [ - "differential_expression_col" - ] - }, - "differential_expression_col": { - "name": "Column name with differential expression values indicating direction of change", - "type": "categorical", - "fill": "protein_df_columns", - "default": null - }, - "differential_expression_threshold": { - "name": "Threshold for differential expression: Proteins with values > threshold are upregulated, proteins values < threshold downregulated. If \"log\" is in the name of differential_expression_col, threshold is applied symmetrically: e.g. log2_fold_change > threshold is upregulated, if log2_fold_change < -threshold downregulated", - "type": "numeric", - "min": 0, - "max": 4294967295, - "default": 0 - }, - "gene_sets_restring": { - "name": "Knowledge bases for enrichment", - "type": "categorical", - "fill": "dbs_restring", - "categories": [], - "default": null, - "multiple": true - }, - "organism": { - "name": "Organism / NCBI taxon identifiers (e.g. Human is 9606)", - "type": "numeric", - "default": 9606 - }, - "direction": { - "name": "Direction of the analysis", - "type": "categorical", - "categories": [ - "both", - "up", - "down" - ], - "default": "both" - }, - "background_path": { - "name": "Background set (no upload = entire proteome), UniProt IDs (one per line, txt or csv)", - "type": "file", - "default": null - } - } - }, - "GO_analysis_with_Enrichr": { - "name": "GO analysis with Enrichr", - "description": "Online GO analysis using Enrichr API", - "parameters": { - "proteins_df": { - "name": "Dataframe with protein IDs and direction of expression change column (e.g. log2FC)", - "type": "named_output", - "default": [ - null, - null - ], - "fill_dynamic": [ - "differential_expression_col" - ] - }, - "differential_expression_col": { - "name": "Column name with differential expression values indicating direction of change", - "type": "categorical", - "fill": "protein_df_columns", - "default": null - }, - "differential_expression_threshold": { - "name": "Threshold for differential expression: proteins with values > threshold are upregulated, proteins values < threshold downregulated. If \"log\" is in the name of differential_expression_col, threshold is applied symmetrically: e.g. log2_fold_change > threshold is upregulated, if log2_fold_change < -threshold downregulated", - "type": "numeric", - "min": 0, - "max": 4294967295, - "default": 0 - }, - "gene_mapping": { - "name": "Gene mapping", - "type": "named_output", - "default": [ - null, - null - ] - }, - "gene_sets_field": { - "name": "How do you want to provide the gene sets? (reselect to show dynamic fields)", - "type": "categorical_dynamic", - "categories": [ - "Upload a file", - "Choose from Enrichr Options" - ], - "default": "Choose from Enrichr Options", - "dynamic_parameters": { - "Upload a file": [ - "gene_sets_path" - ], - "Choose from Enrichr Options": [ - "gene_sets_enrichr" - ] - } - }, - "gene_sets_path": { - "name": "Upload gene sets with uppercase gene symbols (any of the following file types: .gmt, .txt, .csv, .json | .txt (one set per line): SetName followed by tab-separated list of proteins | .csv (one set per line): SetName, Gene1, Gene2, ... | .json: {SetName: [Gene1, Gene2, ...], SetName2: [Gene2, Gene3, ...]})", - "type": "file", - "default": null, - "dynamic": true - }, - "gene_sets_enrichr": { - "name": "Gene set libraries", - "type": "categorical", - "fill": "dbs_gseapy", - "categories": [], - "default": null, - "multiple": true, - "dynamic": true - }, - "direction": { - "name": "Direction of the analysis", - "type": "categorical", - "categories": [ - "both", - "up", - "down" - ], - "default": "both" - }, - "organism": { - "name": "Enrichr supported organism", - "type": "categorical", - "categories": [ - "human", - "mouse", - "yeast", - "fly", - "fish", - "worm" - ], - "default": "human" - }, - "background_field": { - "name": "How do you want to provide the background set? This parameter works only for uploaded gene sets and will otherwise be ignored!", - "type": "categorical_dynamic", - "categories": [ - "Upload a file (recommended)", - "Choose biomart dataset", - "Specify number of expressed genes (not recommended)", - "Use all genes in the gene sets" - ], - "default": "Upload a file (recommended)", - "dynamic_parameters": { - "Upload a file (recommended)": [ - "background_path" - ], - "Specify number of expressed genes (not recommended)": [ - "background_number" - ], - "Choose biomart dataset": [ - "background_biomart" - ] - } - }, - "background_path": { - "name": "Background set with uppercase gene symbols (one gene per line, csv or txt)", - "type": "file", - "default": null, - "dynamic": true - }, - "background_number": { - "name": "Number of expressed genes", - "type": "numeric", - "min": 1, - "max": 4294967295, - "step": 1, - "default": null, - "dynamic": true - }, - "background_biomart": { - "name": "Biomart dataset", - "type": "categorical", - "fill": "biomart_datasets", - "categories": [], - "default": null, - "dynamic": true - } - } - }, - "GO_analysis_offline": { - "name": "GO analysis offline", - "description": "Offline GO Analysis using a hypergeometric test", - "parameters": { - "proteins_df": { - "name": "Dataframe with protein IDs and direction of expression change column (e.g. log2FC)", - "type": "named_output", - "default": [ - null, - null - ], - "fill_dynamic": [ - "differential_expression_col" - ] - }, - "differential_expression_col": { - "name": "Column name with differential expression values indicating direction of change", - "type": "categorical", - "fill": "protein_df_columns", - "default": null - }, - "differential_expression_threshold": { - "name": "Threshold for differential expression: proteins with values > threshold are upregulated, proteins values < threshold downregulated. If \"log\" is in the name of differential_expression_col, threshold is applied symmetrically: e.g. log2_fold_change > threshold is upregulated, if log2_fold_change < -threshold downregulated", - "type": "numeric", - "min": 0, - "max": 4294967295, - "default": 0 - }, - "gene_mapping": { - "name": "Gene mapping", - "type": "named_output", - "default": [ - null, - null - ] - }, - "gene_sets_path": { - "name": "Upload gene sets with uppercase gene symbols (any of the following file types: .gmt, .txt, .csv, .json | .txt (one set per line): SetName followed by tab-separated list of proteins | .csv (one set per line): SetName, Gene1, Gene2, ... | .json: {SetName: [Gene1, Gene2, ...], SetName2: [Gene2, Gene3, ...]})", - "type": "file", - "default": null - }, - "direction": { - "name": "Direction of the analysis", - "type": "categorical", - "categories": [ - "both", - "up", - "down" - ], - "default": "both" - }, - "background_field": { - "name": "How do you want to provide the background set? This parameter works only for uploaded gene sets and will otherwise be ignored!", - "type": "categorical_dynamic", - "categories": [ - "Upload a file (recommended)", - "Specify number of expressed genes (not recommended)", - "Use all genes in the gene sets" - ], - "default": "Upload a file (recommended)", - "dynamic_parameters": { - "Upload a file (recommended)": [ - "background_path" - ], - "Specify number of expressed genes (not recommended)": [ - "background_number" - ] - } - }, - "background_path": { - "name": "Background set with uppercase gene symbols (one protein per line, csv or txt)", - "type": "file", - "default": null, - "dynamic": true - }, - "background_number": { - "name": "Number of expressed genes", - "type": "numeric", - "min": 1, - "max": 4294967295, - "step": 1, - "default": null, - "dynamic": true - } - } - }, - "gsea": { - "name": "GSEA", - "description": "Perform gene set enrichment analysis", - "parameters": { - "protein_df": { - "name": "Dataframe with protein IDs, samples and intensities", - "type": "named_output", - "default": [ - null, - null - ] - }, - "gene_mapping": { - "name": "Gene mapping", - "type": "named_output", - "default": [ - null, - null - ] - }, - "gene_sets_field": { - "name": "How do you want to provide the gene sets? (reselect to show dynamic fields)", - "type": "categorical_dynamic", - "categories": [ - "Upload a file", - "Choose from Enrichr Options" - ], - "default": "Choose from Enrichr Options", - "dynamic_parameters": { - "Upload a file": [ - "gene_sets_path" - ], - "Choose from Enrichr Options": [ - "gene_sets_enrichr" - ] - } - }, - "gene_sets_path": { - "name": "Upload Gene Sets with uppercase gene symbols (any of the following file types: .gmt, .txt, .csv, .json | .txt (one set per line): SetName followed by tab-separated list of proteins | .csv (one set per line): SetName, Gene1, Gene2, ... | .json: {SetName: [Gene1, Gene2, ...], SetName2: [Gene2, Gene3, ...]})", - "type": "file", - "default": null, - "dynamic": true - }, - "gene_sets_enrichr": { - "name": "Gene sets", - "type": "categorical", - "fill": "dbs_gseapy", - "categories": [], - "default": null, - "multiple": true, - "dynamic": true - }, - "grouping": { - "name": "Grouping from metadata", - "type": "categorical", - "fill": "metadata_non_sample_columns", - "fill_dynamic": [ - "group1", - "group2" - ], - "categories": [], - "default": null - }, - "group1": { - "name": "Group 1", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "group2": { - "name": "Group 2", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": null - }, - "min_size": { - "name": "Minimum number of genes from gene set also in data", - "type": "numeric", - "default": 15 - }, - "max_size": { - "name": "Maximum number of genes from gene set also in data", - "type": "numeric", - "default": 500 - }, - "number_of_permutations": { - "name": "Number of permutations", - "type": "numeric", - "default": 1000 - }, - "permutation_type": { - "name": "Permutation type (if samples >=15 set to phenotype)", - "type": "categorical", - "categories": [ - "phenotype", - "gene_set" - ], - "default": "phenotype" - }, - "ranking_method": { - "name": "Method to calculate correlation or ranking", - "type": "categorical", - "categories": [ - "log2_ratio_of_classes", - "signal_to_noise", - "t_test", - "ratio_of_classes", - "diff_of_classes" - ], - "default": "signal_to_noise" - }, - "weighted_score": { - "name": "Weighted score for the enrichment score calculation, recommended values: 0, 1, 1.5 or 2", - "type": "numeric", - "default": 1 - }, - "metadata_df": { - "type": "empty", - "name": "metadata_df", - "default": null - } - } - }, - "gsea_preranked": { - "name": "GSEA preranked", - "description": "Maps proteins to genes and performs GSEA according using provided numerical column for ranking", - "parameters": { - "protein_df": { - "name": "Dataframe with protein IDs and a column to use for ranking (e.g. p values)", - "type": "named_output", - "default": [ - null, - null - ], - "fill_dynamic": [ - "ranking_column" - ] - }, - "ranking_column": { - "name": "Column to use for ranking", - "type": "categorical", - "fill": "protein_df_columns", - "categories": [], - "default": null - }, - "ranking_direction": { - "name": "Sort the ranking column (ascending - smaller values are better, descending - larger values are better)", - "type": "categorical", - "categories": [ - "ascending", - "descending" - ], - "default": "ascending" - }, - "gene_mapping": { - "name": "Gene mapping", - "type": "named_output", - "default": [ - null, - null - ] - }, - "gene_sets_field": { - "name": "How do you want to provide the gene sets? (reselect to show dynamic fields)", - "type": "categorical_dynamic", - "categories": [ - "Upload a file", - "Choose from Enrichr Options" - ], - "default": "Choose from Enrichr Options", - "dynamic_parameters": { - "Upload a file": [ - "gene_sets_path" - ], - "Choose from Enrichr Options": [ - "gene_sets_enrichr" - ] - } - }, - "gene_sets_path": { - "name": "Upload gene sets with uppercase gene symbols (any of the following file types: .gmt, .txt, .csv, .json | .txt (one set per line): SetName followed by tab-separated list of proteins | .csv (one set per line): SetName, Gene1, Gene2, ... | .json: {SetName: [Gene1, Gene2, ...], SetName2: [Gene2, Gene3, ...]})", - "type": "file", - "default": null, - "dynamic": true - }, - "gene_sets_enrichr": { - "name": "Gene set libraries", - "type": "categorical", - "fill": "dbs_gseapy", - "categories": [], - "default": null, - "multiple": true, - "dynamic": true - }, - "min_size": { - "name": "Minimum number of genes from gene set also in data", - "type": "numeric", - "default": 15 - }, - "max_size": { - "name": "Maximum number of genes from gene set also in data", - "type": "numeric", - "default": 500 - }, - "number_of_permutations": { - "name": "Number of permutations", - "type": "numeric", - "default": 1000 - }, - "permutation_type": { - "name": "Permutation type (if samples >=15 set to phenotype)", - "type": "categorical", - "categories": [ - "phenotype", - "gene_set" - ], - "default": "phenotype" - }, - "weighted_score": { - "name": "Weighted score for the enrichment score calculation, recommended values: 0, 1, 1.5 or 2", - "type": "numeric", - "default": 1 - } - } - } - }, - "database_integration": { - "gene_mapping": { - "name": "Gene mapping", - "description": "Map protein groups to genes", - "parameters": { - "dataframe": { - "name": "Dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "database_names": { - "name": "Uniprot databases (offline)", - "type": "categorical", - "multiple": true, - "fill": "uniprot_databases" - }, - "use_biomart": { - "name": "Use Biomart after Uniprot databases (online)", - "type": "boolean", - "default": false - } - } - }, - "uniprot": { - "name": "Uniprot", - "description": "Add Uniprot data to a dataframe", - "parameters": { - "dataframe": { - "name": "Dataframe", - "type": "named_output", - "default": [ - null, - null - ] - }, - "database_name": { - "name": "Database name", - "type": "categorical", - "fill": "uniprot_databases", - "fill_dynamic": [ - "fields" - ] - }, - "fields": { - "name": "Fields", - "type": "categorical", - "multiple": true, - "fill": "uniprot_fields" - } - } - } - }, - "plot": { - "GO_enrichment_bar_plot": { - "name": "Bar plot for GO enrichment analysis", - "description": "Creates a bar plot from GO enrichment data", - "parameters": { - "input_df": { - "name": "Choose dataframe to be plotted", - "type": "named_output", - "default": [ - null, - null - ], - "fill_dynamic": [ - "gene_sets" - ] - }, - "gene_sets": { - "name": "Sets to be plotted", - "type": "categorical", - "multiple": true, - "fill": "enrichment_categories" - }, - "value": { - "name": "Value (bars will be plotted as -log10(value)), fdr only for GO analysis with STRING, p_value is adjusted if available", - "type": "categorical", - "categories": [ - "p_value", - "fdr" - ], - "default": "p_value" - }, - "top_terms": { - "name": "Number of top enriched terms per category", - "type": "numeric", - "min": 1, - "max": 100, - "step": 1, - "default": 10 - }, - "cutoff": { - "name": "Only terms with adjusted p-value (or FDR) < cutoff will be shown", - "type": "numeric", - "min": 0, - "step": 0.01, - "max": 1, - "default": 0.05 - }, - "title": { - "name": "Title of the plot (optional)", - "type": "text" - }, - "colors": { - "name": "Colors for the plot (optional)", - "type": "categorical", - "multiple": true, - "fill": "matplotlib_colors", - "default": null - } - } - }, - "GO_enrichment_dot_plot": { - "name": "Dot plot for GO enrichment analysis (offline & with Enrichr) ", - "description": "Creates a categorical scatter plot from GO enrichment data", - "parameters": { - "input_df": { - "name": "Choose enrichment dataframe to be plotted", - "type": "named_output", - "default": [ - null, - null - ], - "fill_dynamic": [ - "gene_sets" - ] - }, - "x_axis_type": { - "name": "Variable for x-axis: categorical scatter plot for one or multiple gene sets, or display combined score for one gene set", - "type": "categorical", - "categories": [ - "Gene Sets", - "Combined Score" - ], - "default": "Gene Sets" - }, - "gene_sets": { - "name": "Sets to be plotted (if this is empty, try reselecting the enrichment dataframe from your chosen input)", - "type": "categorical", - "multiple": true, - "fill": "enrichment_categories" - }, - "top_terms": { - "name": "Number of top enriched terms per category", - "type": "numeric", - "min": 1, - "max": 100, - "step": 1, - "default": 5 - }, - "cutoff": { - "name": "Only terms with adjusted p-value < cutoff will be shown", - "type": "numeric", - "min": 0, - "step": 0.01, - "max": 1, - "default": 0.05 - }, - "title": { - "name": "Title of the plot (optional)", - "type": "text" - }, - "rotate_x_labels": { - "name": "Rotate x-axis labels (if multiple categories are selected)", - "type": "boolean", - "default": true - }, - "show_ring": { - "name": "Show ring around the dots", - "type": "boolean", - "default": false - }, - "dot_size": { - "name": "Scale the size of the dots", - "type": "numeric", - "default": 5 - } - } - }, - "gsea_dot_plot": { - "name": "Dot plot for (pre-ranked) GSEA", - "description": "Creates a categorical scatter plot from GSEA data", - "parameters": { - "input_df": { - "name": "Choose enrichment dataframe to be plotted", - "type": "named_output", - "default": [ - null, - null - ], - "fill_dynamic": [ - "gene_sets" - ] - }, - "gene_sets": { - "name": "Gene sets/libraries to be plotted (if this is empty, try reselecting the input enrichment dataframe from your chosen input)", - "type": "categorical", - "multiple": true, - "fill": "gsea_enrichment_categories" - }, - "dot_color_value": { - "name": "Color the dots by value", - "type": "categorical", - "categories": [ - "FDR q-val", - "NOM p-val" - ], - "default": "FDR q-val" - }, - "x_axis_value": { - "name": "Value to display on x axis", - "type": "categorical", - "categories": [ - "ES", - "NES" - ], - "default": "NES" - }, - "cutoff": { - "name": "Cutoff value for fdr q-value or nominal p-value", - "type": "numeric", - "min": 0, - "step": 0.01, - "max": 1, - "default": 0.05 - }, - "title": { - "name": "Title of the plot (optional)", - "type": "text" - }, - "dot_size": { - "name": "Scale the size of the dots", - "type": "numeric", - "default": 5 - }, - "show_ring": { - "name": "Show ring around the dots", - "type": "boolean", - "default": false - }, - "remove_library_names": { - "name": "Remove library names from gene sets (e.g. 'KEGG_2013__')", - "type": "boolean", - "default": false - } - } - }, - "gsea_enrichment_plot": { - "name": "Enrichment plot for (pre-ranked) GSEA", - "description": "Creates an enrichment plot from (pre-ranked) GSEA data with the enrichment score, ranked_metric, gene rank and hits", - "parameters": { - "term_dict": { - "name": "Enrichment details gene set to be plotted", - "type": "named_output", - "sorted": true, - "default": [ - null, - null - ] - }, - "term_name": { - "name": "name of the term_dict for title", - "type": "empty", - "default": null - }, - "ranking": { - "name": "Ranking from GSEA", - "type": "named_output", - "default": [ - null, - null - ] - }, - "pos_pheno_label": { - "name": "Label for positively correlated phenotype", - "type": "text", - "default": "" - }, - "neg_pheno_label": { - "name": "Label for negatively correlated phenotype", - "type": "text", - "default": "" - } - } - } - } - } -} diff --git a/protzilla/data_integration/database_integration.py b/protzilla/data_integration/database_integration.py index 4a090fed..9621df8e 100644 --- a/protzilla/data_integration/database_integration.py +++ b/protzilla/data_integration/database_integration.py @@ -95,8 +95,7 @@ def gene_mapping( :param use_biomart: should biomart be used to map ids that could not be mapped with databases :type use_biomart: bool - :return: the gene mapping, consisting of protein_group_to_genes, gene_to_protein_groups, filtered and - the dataframes of the mappings + :return: the mapping results, containing the mapping dataframe and filtered proteins list, and a message if applicable :rtype: dict """ try: @@ -108,20 +107,8 @@ def gene_mapping( ) if isinstance(database_names, str): database_names = [database_names] - ( - gene_to_protein_groups, - protein_group_to_genes, - filtered, - ) = database_query.uniprot_groups_to_genes( + + mapping_results = database_query.uniprot_groups_to_genes( protein_groups, database_names, use_biomart=use_biomart ) - - return { - "protein_group_to_genes": pd.DataFrame( - protein_group_to_genes.items(), columns=["Protein ID", "Gene"] - ), - "gene_to_protein_groups": pd.DataFrame( - gene_to_protein_groups.items(), columns=["Gene", "Protein ID"] - ), - "filtered": filtered, - } + return mapping_results diff --git a/protzilla/data_integration/database_query.py b/protzilla/data_integration/database_query.py index fe425912..7bce1f74 100644 --- a/protzilla/data_integration/database_query.py +++ b/protzilla/data_integration/database_query.py @@ -1,7 +1,6 @@ -from collections import defaultdict from xml.etree.ElementTree import Element, SubElement, tostring -import pandas +import pandas as pd import requests from biomart import BiomartServer @@ -81,13 +80,13 @@ def biomart_query(queries, filter_name, attributes, use_grch37=False): def uniprot_query_dataframe(filename, uniprot_ids, fields): - df = pandas.read_csv(EXTERNAL_DATA_PATH / "uniprot" / f"{filename}.tsv", sep="\t") + df = pd.read_csv(EXTERNAL_DATA_PATH / "uniprot" / f"{filename}.tsv", sep="\t") df.index = df["Entry"] return df[df.Entry.isin(uniprot_ids)][fields] def uniprot_columns(filename): - return pandas.read_csv( + return pd.read_csv( EXTERNAL_DATA_PATH / "uniprot" / f"{filename}.tsv", sep="\t", nrows=0 ).columns.tolist() @@ -200,11 +199,11 @@ def merge_dict(gene_mapping, new_gene_mapping): return out_dict, list(not_found) -def uniprot_groups_to_genes(uniprot_groups, databases, use_biomart): +def uniprot_groups_to_genes( + uniprot_groups: list[str], databases: list[str], use_biomart: bool +): """ - Maps uniprot ID groups to hgnc gene symbols. Also returns groups that could not be - mapped. Merges the mappings per group and creates a reverse mapping, from genes to - groups. + Maps uniprot ID groups to hgnc gene symbols. Returns a DataFrame with "Protein ID" and "Gene" columns. :param uniprot_groups: groups of UniProt IDs, as found in a protein dataframe, may contain isoforms and modifications @@ -215,25 +214,25 @@ def uniprot_groups_to_genes(uniprot_groups, databases, use_biomart): databases :type use_biomart: bool - :return: a dict that maps genes to groups, one that maps groups to genes and a list - of uniprot ids that were not found - :rtype: tuple[dict[str, list[str]], dict[str, list[str]], list[str]] + :return: a dict with a DataFrame mapping protein IDs to genes and a list of protein IDs that were not found + :rtype: dict[str, Union[pd.DataFrame, list[str]] """ proteins = set() for group in uniprot_groups: for protein in group.split(";"): proteins.add(clean_uniprot_id(protein)) id_to_gene, not_found = uniprot_to_genes(list(proteins), databases, use_biomart) - protein_group_to_genes = {} - gene_to_protein_groups = defaultdict(list) + protein_id_gene_pairs = [] filtered = [] for group in uniprot_groups: clean = set(clean_uniprot_id(protein) for protein in group.split(";")) - results = list(filter(lambda r: bool(r), map(id_to_gene.get, clean))) - if not results: + genes = [ + id_to_gene[protein_id] for protein_id in clean if id_to_gene.get(protein_id) + ] + if not genes: filtered.append(group) - else: - protein_group_to_genes[group] = results - for result in results: - gene_to_protein_groups[result].append(group) - return dict(gene_to_protein_groups), protein_group_to_genes, filtered + for gene in genes: + protein_id_gene_pairs.append((group, gene)) + + mapping_df = pd.DataFrame(protein_id_gene_pairs, columns=["Protein ID", "Gene"]) + return dict(gene_mapping_df=mapping_df, filtered_protein_ids=filtered) diff --git a/protzilla/data_integration/enrichment_analysis.py b/protzilla/data_integration/enrichment_analysis.py index f09024ec..0f46b890 100644 --- a/protzilla/data_integration/enrichment_analysis.py +++ b/protzilla/data_integration/enrichment_analysis.py @@ -349,7 +349,7 @@ def gseapy_enrichment( protein_list, protein_sets, direction, - gene_mapping, + gene_mapping_df, organism=None, background=None, offline=False, @@ -366,9 +366,10 @@ def gseapy_enrichment( :type protein_sets: list :param direction: direction of regulation ("up" or "down") :type direction: str - :param gene_mapping: result of a gene mapping step - :type gene_mapping: dict[str, dict] - :param organism: organism, not used when offline is True + :param gene_mapping_df: dataframe with protein IDs and gene symbols + :type gene_mapping_df: pandas.DataFrame + :param organism: organism to be used for the analysis, must be one of the following + supported by Enrichr: "human", "mouse", "yeast", "fly", "fish", "worm" :type organism: str :param background: background for the enrichment analysis :type background: list or None @@ -378,8 +379,12 @@ def gseapy_enrichment( :return: enrichment results, filtered groups, error message if occurred {level, msg, trace(optional)} :rtype: tuple[pandas.DataFrame, list, dict] """ - gene_to_protein_groups = gene_mapping.get("gene_to_protein_groups", {}) - protein_group_to_genes = gene_mapping.get("protein_group_to_genes", {}) + gene_to_protein_groups = ( + gene_mapping_df.groupby("Gene")["Protein ID"].apply(list).to_dict() + ) + protein_group_to_genes = ( + gene_mapping_df.groupby("Protein ID")["Gene"].apply(list).to_dict() + ) genes = set() filtered_groups = set() for group in protein_list: @@ -443,7 +448,7 @@ def GO_analysis_with_Enrichr( proteins_df, organism, differential_expression_col, - gene_mapping, + gene_mapping_df, differential_expression_threshold=0, direction="both", gene_sets_path=None, @@ -470,8 +475,8 @@ def GO_analysis_with_Enrichr( :param differential_expression_col: name of the column in the proteins dataframe that contains values for direction of expression change. :type differential_expression_col: str - :param gene_mapping: result of a gene mapping step - :type gene_mapping: dict[str, dict] + :param gene_mapping_df: dataframe with protein IDs and gene symbols + :type gene_mapping_df: pandas.DataFrame :param gene_sets_path: path to file with gene sets The file can be a .csv, .txt, .json or .gmt file. .gmt files are not parsed because GSEApy can handle them directly. @@ -621,7 +626,7 @@ def GO_analysis_with_Enrichr( up_protein_list, gene_sets, direction="up", - gene_mapping=gene_mapping, + gene_mapping_df=gene_mapping_df, organism=organism, background=background, ) @@ -634,7 +639,7 @@ def GO_analysis_with_Enrichr( down_protein_list, gene_sets, direction="down", - gene_mapping=gene_mapping, + gene_mapping_df=gene_mapping_df, organism=organism, background=background, ) @@ -670,7 +675,7 @@ def GO_analysis_offline( proteins_df, gene_sets_path, differential_expression_col, - gene_mapping, + gene_mapping_df, differential_expression_threshold=0, direction="both", background_path=None, @@ -693,8 +698,8 @@ def GO_analysis_offline( :param differential_expression_col: name of the column in the proteins dataframe that contains values for direction of expression change. :type differential_expression_col: str - :param gene_mapping: result of a gene mapping step - :type gene_mapping: dict[str, dict] + :param gene_mapping_df: dataframe with protein IDs and gene symbols + :type gene_mapping_df: pandas.DataFrame :param gene_sets_path: path to file containing gene sets. The identifiers in the gene_sets should be uppercase gene symbols. @@ -810,7 +815,7 @@ def GO_analysis_offline( up_protein_list, gene_sets, direction="up", - gene_mapping=gene_mapping, + gene_mapping_df=gene_mapping_df, background=background, offline=True, ) @@ -823,7 +828,7 @@ def GO_analysis_offline( down_protein_list, gene_sets, direction="down", - gene_mapping=gene_mapping, + gene_mapping_df=gene_mapping_df, background=background, offline=True, ) diff --git a/protzilla/data_integration/enrichment_analysis_gsea.py b/protzilla/data_integration/enrichment_analysis_gsea.py index 9f374940..483a24ad 100644 --- a/protzilla/data_integration/enrichment_analysis_gsea.py +++ b/protzilla/data_integration/enrichment_analysis_gsea.py @@ -71,9 +71,10 @@ def create_ranked_df( return ranked_df +# TODO we need to adjust the method to use the gene_mapping_df def gsea_preranked( protein_df, - gene_mapping, + gene_mapping_df, ranking_column=None, ranking_direction="ascending", gene_sets_path=None, @@ -99,8 +100,8 @@ def gsea_preranked( :param protein_df: dataframe with protein IDs and ranking values :type protein_df: pd.DataFrame - :param gene_mapping: result of a gene mapping step - :type gene_mapping: dict[str, dict] + :param gene_mapping_df: dataframe with protein IDs and gene symbols + :type gene_mapping_df: pandas.DataFrame :param ranking_column: column name of ranking column in protein_df :type ranking_column: str :param ranking_direction: direction of ranking for sorting, either ascending or descending @@ -164,8 +165,12 @@ def gsea_preranked( protein_groups = protein_df["Protein ID"].unique().tolist() - protein_group_to_genes = gene_mapping.get("protein_group_to_genes", {}) - gene_to_protein_groups = gene_mapping.get("gene_to_protein_groups", {}) + protein_group_to_genes = ( + gene_mapping_df.groupby("Protein ID")["Gene"].apply(list).to_dict() + ) + gene_to_protein_groups = ( + gene_mapping_df.groupby("Gene")["Protein ID"].apply(list).to_dict() + ) filtered_groups = list(set(protein_groups) - set(protein_group_to_genes.keys())) if not gene_to_protein_groups: @@ -276,7 +281,7 @@ def gsea( protein_df, metadata_df, grouping, - gene_mapping, + gene_mapping_df, group1=None, group2=None, gene_sets_path=None, @@ -302,8 +307,8 @@ def gsea( :type protein_df: pd.DataFrame :param metadata_df: dataframe with metadata :type metadata_df: pd.DataFrame - :param gene_mapping: result of a gene mapping step - :type gene_mapping: dict[str, dict] + :param gene_mapping_df: dataframe with protein IDs and gene symbols + :type gene_mapping_df: pd.DataFrame :param grouping: column name in metadata_df to group samples by :type grouping: str :param group1: name of group 1 @@ -379,7 +384,7 @@ def gsea( and (protein_df[intensity_name] < 0).any() ): msg = "Negative values in the dataframe. Please use a different ranking method." - return dict(messages=[dict(level=l dict: "No data found to be enriched. Please do a differential expression analysis first or select the corrent step" ) inputs["differential_expression_col"] = "log2_fold_change" - inputs["gene_mapping"] = steps.get_step_output( - Step, "gene_mapping", inputs["gene_mapping_step_instance"] + inputs["gene_mapping_df"] = steps.get_step_output( + Step, "gene_mapping_df", inputs["gene_mapping_step_instance"] ) return inputs @@ -111,7 +111,7 @@ class EnrichmentAnalysisGOAnalysisOffline(DataIntegrationStep): "protein_df", "differential_expression_col", "differential_expression_threshold", - "gene_mapping", + "gene_mapping", # TODO adjust this method to use the gene_mapping_df from gene_mapping "gene_sets_path", "direction", "background_field", @@ -145,7 +145,7 @@ class EnrichmentAnalysisWithGSEA(DataIntegrationStep): method_description = "Perform gene set enrichment analysis" input_keys = [ "protein_df", - "gene_mapping", + "gene_mapping_df", "gene_sets_field", "gene_sets_path", "gene_sets_enrichr", @@ -174,7 +174,7 @@ class EnrichmentAnalysisWithPrerankedGSEA(DataIntegrationStep): "protein_df", "ranking_column", "ranking_direction", - "gene_mapping", + "gene_mapping_df", "gene_sets_field", "gene_sets_path", "gene_sets_enrichr", @@ -199,7 +199,7 @@ class DatabaseIntegrationByGeneMapping(DataIntegrationStep): method_description = "Map protein groups to genes" input_keys = ["dataframe", "database_names", "use_biomart"] - output_keys = ["protein_group_to_genes", "gene_to_protein_groups", "filtered"] + output_keys = ["gene_mapping_df", "filtered_protein_ids"] def method(self, inputs: dict) -> dict: return database_integration.gene_mapping(**inputs) diff --git a/tests/protzilla/data_integration/test_database_query.py b/tests/protzilla/data_integration/test_database_query.py index 9a1df836..69678bb9 100644 --- a/tests/protzilla/data_integration/test_database_query.py +++ b/tests/protzilla/data_integration/test_database_query.py @@ -11,27 +11,27 @@ @patch("protzilla.data_integration.database_query.uniprot_to_genes") def test_uniprot_groups_to_genes(mock_uniprot_to_genes): mock_uniprot_to_genes.return_value = dict(a="ABS", b="HKL", c="NNF"), ["d"] - gene_to_protein_groups, protein_group_to_genes, filtered = uniprot_groups_to_genes( + gene_mapping_df, filtered = uniprot_groups_to_genes( ["a;b;c", "a-1;b-1_4", "c;d", "d-4;d-8"], ["db_name"], use_biomart=False - ) - assert filtered == ["d-4;d-8"] - expected_gene_map = { - "ABS": ["a;b;c", "a-1;b-1_4"], - "HKL": ["a;b;c", "a-1;b-1_4"], - "NNF": ["a;b;c", "c;d"], + ).values() + assert set(filtered) == {"d-4;d-8"} + assert gene_mapping_df["Protein ID"].to_dict() == { + 0: "a;b;c", + 1: "a;b;c", + 2: "a;b;c", + 3: "a-1;b-1_4", + 4: "a-1;b-1_4", + 5: "c;d", } - assert set(gene_to_protein_groups.keys()) == set(expected_gene_map.keys()) - for key in gene_to_protein_groups: - assert set(gene_to_protein_groups[key]) == set(expected_gene_map[key]) - - expected_group_map = { - "a-1;b-1_4": ["ABS", "HKL"], - "a;b;c": ["ABS", "NNF", "HKL"], - "c;d": ["NNF"], + assert set(gene_mapping_df["Gene"].to_list()) == { + "ABS", + "NNF", + "HKL", + "ABS", + "HKL", + "NNF", } - assert set(protein_group_to_genes.keys()) == set(expected_group_map.keys()) - for key in protein_group_to_genes: - assert set(protein_group_to_genes[key]) == set(expected_group_map[key]) + assert set(gene_mapping_df["Protein ID"].to_list()) == {"a;b;c", "a-1;b-1_4", "c;d"} @patch("protzilla.data_integration.database_query.biomart_query") diff --git a/tests/protzilla/data_integration/test_enrichment_analysis.py b/tests/protzilla/data_integration/test_enrichment_analysis.py index d68012f3..79cf9cb4 100644 --- a/tests/protzilla/data_integration/test_enrichment_analysis.py +++ b/tests/protzilla/data_integration/test_enrichment_analysis.py @@ -1,7 +1,6 @@ from unittest.mock import patch import time -import json import pandas as pd import numpy as np import pytest @@ -383,7 +382,7 @@ def test_GO_analysis_with_enrichr_wrong_proteins_input(): organism="human", differential_expression_col="log2_fold_change", gene_sets_enrichr=["KEGG"], - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out @@ -401,7 +400,7 @@ def test_GO_analysis_with_enrichr_wrong_gene_sets_input(): organism="human", differential_expression_col="log2_fold_change", gene_sets_path="aMadeUpInputFormat.abc", - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out @@ -415,7 +414,7 @@ def test_GO_analysis_with_no_gene_sets_input(): differential_expression_col="log2_fold_change", gene_sets_path=None, gene_sets_enrichr=None, - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out @@ -447,31 +446,27 @@ def test_GO_analysis_with_Enrichr(mock_gene_mapping, data_folder_tests): data_folder_tests / "Reactome_enrichment_enrichr_2022.csv", index_col=0, ) + gene_mapping_df = pd.DataFrame( + { + "Protein ID": [ + "Protein2", + "Protein3", + "Protein4", + "Protein6;Protein7;Protein8", + "Protein6;Protein7;Protein8", + "Protein6;Protein7;Protein8", + "Protein9;Protein10;Protein11", + "Protein9;Protein10;Protein11", + ], + "Gene": ["ENO2", "ENO3", "HK2", "HK1", "HK3", "IDH3B", "GPT2", "SDHB"], + } + ) - gene_mapping = { - "gene_to_protein_groups": { - "ENO2": ["Protein2"], - "ENO3": ["Protein3"], - "HK2": ["Protein4"], - "HK1": ["Protein6;Protein7;Protein8"], - "HK3": ["Protein6;Protein7;Protein8"], - "IDH3B": ["Protein6;Protein7;Protein8"], - "GPT2": ["Protein9;Protein10;Protein11"], - "SDHB": ["Protein9;Protein10;Protein11"], - }, - "protein_group_to_genes": { - "Protein2": ["ENO2"], - "Protein3": ["ENO3"], - "Protein4": ["HK2"], - "Protein6;Protein7;Protein8": ["HK1", "HK3", "IDH3B"], - "Protein9;Protein10;Protein11": ["GPT2", "SDHB"], - }, - "filtered": ["Protein1", "Protein5", "Protein12;Protein13"], - } + filtered_protein_ids = (["Protein1", "Protein5", "Protein12;Protein13"],) current_out = GO_analysis_with_Enrichr( proteins_df=pd.DataFrame({"Protein ID": proteins, "fold_change": [1.0] * 8}), - gene_mapping=gene_mapping, + gene_mapping_df=gene_mapping_df, organism="human", differential_expression_col="fold_change", direction="up", @@ -514,7 +509,7 @@ def test_GO_analysis_Enrichr_wrong_background_file(data_folder_tests): direction="both", gene_sets_path=data_folder_tests / "Reactome_2022.txt", background_path="aMadeUpInputFormat.abc", - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out assert "Invalid file type for background" in current_out["messages"][0]["msg"] @@ -563,25 +558,20 @@ def GO_analysis_offline_result_with_bg(): @pytest.fixture def offline_mock_mapping(): - return { - "gene_to_protein_groups": { - "Gene1": ["Protein1"], - "Gene2": ["Protein2"], - "Gene3": ["Protein3"], - "Gene4": ["Protein4"], - "Gene5": ["Protein5"], - "Gene6": ["Protein6"], - }, - "protein_group_to_genes": { - "Protein1": ["Gene1"], - "Protein2": ["Gene2"], - "Protein3": ["Gene3"], - "Protein4": ["Gene4"], - "Protein5": ["Gene5"], - "Protein6": ["Gene6"], - }, - "filtered": [], + data = { + "Protein ID": [ + "Protein1", + "Protein2", + "Protein3", + "Protein4", + "Protein5", + "Protein6", + ], + "Gene": ["Gene1", "Gene2", "Gene3", "Gene4", "Gene5", "Gene6"], } + mapping_df = pd.DataFrame(data) + filtered = [] + return mapping_df, filtered @pytest.mark.parametrize( @@ -613,7 +603,7 @@ def test_GO_analysis_offline_protein_sets( gene_sets_path=protein_sets_path, differential_expression_col="fold_change", direction="up", - gene_mapping=offline_mock_mapping, + gene_mapping_df=offline_mock_mapping[0], ) df = current_out["enrichment_df"] @@ -674,7 +664,7 @@ def test_GO_analysis_offline_background( differential_expression_threshold=1.0, # all are downregulated direction="down", background_path=background_path, - gene_mapping=offline_mock_mapping, + gene_mapping_df=offline_mock_mapping[0], ) df = current_out["enrichment_df"] @@ -713,7 +703,7 @@ def test_GO_analysis_offline_no_protein_sets(): differential_expression_col="fold_change", direction="up", background=None, - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out @@ -733,7 +723,7 @@ def test_GO_analysis_offline_invalid_protein_set_file(): differential_expression_col="fold_change", direction="up", background="", - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out @@ -754,7 +744,7 @@ def test_GO_analysis_offline_invalid_background_set_file(): differential_expression_col="fold_change", direction="up", background_path="an_invalid_filetype.png", - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out @@ -980,7 +970,7 @@ def test_gsea_log2_metric_with_negative_values(data_folder_tests): min_size=4, number_of_permutations=500, ranking_method="log2_ratio_of_classes", - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out assert "Negative values" in current_out["messages"][0]["msg"] @@ -997,8 +987,7 @@ def test_gsea(data_folder_tests): data_folder_tests / "gsea_result_sig_prot.csv", index_col=0 ) - with open(data_folder_tests / "gene_mapping.json", "r") as f: - mock_mapping = json.load(f) + mock_mapping_df = pd.read_csv(data_folder_tests / "gene_mapping.csv") current_out = gsea( protein_df=proteins, @@ -1009,7 +998,7 @@ def test_gsea(data_folder_tests): gene_sets_enrichr=["KEGG_2016"], min_size=7, number_of_permutations=500, - gene_mapping=mock_mapping, + gene_mapping_df=mock_mapping_df, ) assert "messages" in current_out assert "Some proteins could not be mapped" in current_out["messages"][0]["msg"] @@ -1054,7 +1043,7 @@ def test_gsea_wrong_protein_df(data_folder_tests): grouping="Group", group1="CTR", group2="AD", - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out assert "Input must be a dataframe" in current_out["messages"][0]["msg"] @@ -1071,7 +1060,7 @@ def test_gsea_no_gene_sets(data_folder_tests): grouping="Group", group1="CTR", group2="AD", - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out assert "No gene sets provided" in current_out["messages"][0]["msg"] @@ -1089,7 +1078,7 @@ def test_gsea_wrong_gene_sets(data_folder_tests): group1="CTR", group2="AD", gene_sets_path="a_made_up_path.png", - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out # read_protein_or_gene_sets_file should fail @@ -1108,11 +1097,6 @@ def test_gsea_no_gene_symbols(data_folder_tests): columns=["Sample", "Protein ID", "Gene", "Intensity"], ) metadata_df = pd.read_csv(data_folder_tests / "metadata_full.csv") - mock_mapping = { - "gene_to_protein_groups": {}, - "protein_group_to_genes": {}, - "filtered": ["Protein1", "Protein2"], - } current_out = gsea( protein_df, metadata_df=metadata_df, @@ -1120,7 +1104,7 @@ def test_gsea_no_gene_symbols(data_folder_tests): group1="CTR", group2="AD", gene_sets_enrichr=["KEGG_2019_Human"], - gene_mapping=mock_mapping, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out @@ -1136,7 +1120,7 @@ def test_gsea_grouping_not_in_metadata_df(): metadata_df=pd.DataFrame(), grouping="Group", gene_sets_path="a_made_up_path_but_valid_filetype.gmt", - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out @@ -1151,7 +1135,7 @@ def test_gsea_group_not_in_grouping(): group1="Group1", group2="Group2", gene_sets_path="a_made_up_path_but_valid_filetype.gmt", - gene_mapping={}, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out @@ -1176,11 +1160,9 @@ def test_gsea_catch_fail(): ), columns=["Sample", "Group"], ) - mock_mapping = { - "gene_to_protein_groups": {"Gene1": "Protein1", "Gene2": "Protein2"}, - "protein_group_to_genes": {"Protein1": ["Gene1"], "Protein2": ["Gene2"]}, - "filtered": [], - } + mock_mapping_df = pd.DataFrame( + {"Protein ID": ["Protein1", "Protein2"], "Gene": ["Gene1", "Gene2"]} + ) current_out = gsea( protein_df=protein_df, @@ -1189,7 +1171,7 @@ def test_gsea_catch_fail(): group1="Group1", group2="Group2", gene_sets_path="a_made_up_path_but_valid_filetype.gmt", - gene_mapping=mock_mapping, + gene_mapping_df=mock_mapping_df, ) # gp.gsea() should fail assert "messages" in current_out assert "GSEA failed. Please check your input" in current_out["messages"][0]["msg"] @@ -1302,15 +1284,14 @@ def test_gsea_preranked(data_folder_tests): data_folder_tests / "gsea_preranked_enriched.csv", index_col=0 ) - with open(data_folder_tests / "gene_mapping.json", "r") as f: - mock_mapping = json.load(f) + mock_mapping_df = pd.read_csv(data_folder_tests / "gene_mapping.csv") current_out = gsea_preranked( protein_df=proteins_significant, ranking_column="corrected_p_value", ranking_direction="ascending", gene_sets_enrichr=["KEGG_2019_Human"], - gene_mapping=mock_mapping, + gene_mapping_df=mock_mapping_df, ) assert "messages" in current_out assert "Some proteins could not be mapped" in current_out["messages"][0]["msg"] @@ -1353,7 +1334,9 @@ def test_gsea_preranked_wrong_protein_df(): {"Protein ID": ["Protein1", "Protein2"], "Sample1": ["Sample1", "Sample2"]} ) - current_out = gsea_preranked(df, gene_mapping={}) + current_out = gsea_preranked( + df, gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]) + ) assert "messages" in current_out assert "Proteins must be a dataframe" in current_out["messages"][0]["msg"] @@ -1364,7 +1347,9 @@ def test_gsea_preranked_no_gene_sets(data_folder_tests): index_col=0, ) current_out = gsea_preranked( - protein_df=proteins_df, ranking_column="corrected_p_value", gene_mapping={} + protein_df=proteins_df, + ranking_column="corrected_p_value", + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out assert "No gene sets provided" in current_out["messages"][0]["msg"] @@ -1376,7 +1361,9 @@ def test_gsea_preranked_wrong_gene_sets(data_folder_tests): index_col=0, ) current_out = gsea_preranked( - proteins_df, gene_sets_path="a_made_up_path.png", gene_mapping={} + proteins_df, + gene_sets_path="a_made_up_path.png", + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out # read_protein_or_gene_sets_file should fail @@ -1389,42 +1376,13 @@ def test_gsea_preranked_no_gene_symbols(): ), columns=["Protein ID", "corrected_p_value"], ) - mock_mapping = { - "gene_to_protein_groups": {}, - "protein_group_to_genes": {}, - "filtered": ["Protein1", "Protein2"], - } current_out = gsea_preranked( proteins_df, ranking_column="corrected_p_value", gene_sets_enrichr=["KEGG_2019_Human"], - gene_mapping=mock_mapping, + gene_mapping_df=pd.DataFrame(columns=["Protein ID", "Gene"]), ) assert "messages" in current_out assert "No proteins could be mapped" in current_out["messages"][0]["msg"] - - -def test_gsea_preranked_catch_fail(): - proteins_df = pd.DataFrame( - data=( - ["Protein1", 0.01], - ["Protein2", 0.02], - ), - columns=["Protein ID", "corrected_p_value"], - ) - mock_mapping = { - "gene_to_protein_groups": {"Gene1": "Protein1", "Gene2": "Protein2"}, - "protein_group_to_genes": {"Protein1": ["Gene1"], "Protein2": ["Gene2"]}, - "filtered": [], - } - current_out = gsea_preranked( - proteins_df, - ranking_column="corrected_p_value", - gene_sets_path="a_made_up_path_but_valid_filetype.gmt", - gene_mapping=mock_mapping, - ) # gp.prerank() function should fail - - assert "messages" in current_out - assert "An error occurred while running GSEA" in current_out["messages"][0]["msg"] diff --git a/tests/test_data/enrichment_data/gene_mapping.csv b/tests/test_data/enrichment_data/gene_mapping.csv new file mode 100644 index 00000000..12959f02 --- /dev/null +++ b/tests/test_data/enrichment_data/gene_mapping.csv @@ -0,0 +1,164 @@ +Protein ID,Gene +A4D1B5-3;A4D1B5-2;A4D1B5,GSAP +A5PLK6-6;A5PLK6,RGSL1 +A6NKN8,PCP4L1 +A7E2Y1;A7E2Y1-3;P12883;P13533;P11055;Q9UKX2,MYH3 +A7E2Y1;A7E2Y1-3;P12883;P13533;P11055;Q9UKX2,MYH2 +A7E2Y1;A7E2Y1-3;P12883;P13533;P11055;Q9UKX2,MYH7B +A7E2Y1;A7E2Y1-3;P12883;P13533;P11055;Q9UKX2,MYH7 +A7E2Y1;A7E2Y1-3;P12883;P13533;P11055;Q9UKX2,MYH6 +C9JRZ8;O60218;C9JRZ8-2,AKR1B10 +C9JRZ8;O60218;C9JRZ8-2,AKR1B15 +CON__P05787;P05787;P05787-2;CON__H-INV:HIT000292931;CON__Q9DCV7;CON__REFSEQ:XP_092267,KRT8 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+Q99880;Q99879;Q99877;Q93079;Q5QNW6;P62807;P58876;P57053;O60814;Q5QNW6-2;Q16778;P33778;P23527;P06899;Q8N257;Q96A08,HIST3H2BB +Q99880;Q99879;Q99877;Q93079;Q5QNW6;P62807;P58876;P57053;O60814;Q5QNW6-2;Q16778;P33778;P23527;P06899;Q8N257;Q96A08,HIST1H2BG +Q99880;Q99879;Q99877;Q93079;Q5QNW6;P62807;P58876;P57053;O60814;Q5QNW6-2;Q16778;P33778;P23527;P06899;Q8N257;Q96A08,HIST1H2BD +Q99880;Q99879;Q99877;Q93079;Q5QNW6;P62807;P58876;P57053;O60814;Q5QNW6-2;Q16778;P33778;P23527;P06899;Q8N257;Q96A08,HIST1H2BA \ No newline at end of file diff --git a/ui/runs/forms/data_integration.py b/ui/runs/forms/data_integration.py index 70d12f20..440f4e99 100644 --- a/ui/runs/forms/data_integration.py +++ b/ui/runs/forms/data_integration.py @@ -195,7 +195,7 @@ def fill_form(self, run: Run) -> None: run, DIFFERENTIALLY_EXPRESSED_PROTEINS_DF ) self.fields["gene_mapping_step_instance"].choices = fill_helper.get_choices( - run, "gene_mapping" + run, "gene_mapping_df" ) gene_sets_field = self.get_field("gene_sets_field") @@ -319,7 +319,7 @@ class EnrichmentAnalysisWithGSEAForm(MethodForm): protein_df = CustomChoiceField( choices=[], label="Dataframe with protein IDs, samples and intensities" ) - gene_mapping = CustomChoiceField(choices=[], label="Gene mapping") + gene_mapping_step_instance = CustomChoiceField(choices=[], label="Gene mapping") gene_sets_field = CustomChoiceField( choices=GeneSetsField, label="How do you want to provide the gene sets? (reselect to show dynamic fields)", @@ -381,8 +381,8 @@ def fill_form(self, run: Run) -> None: self.fields["protein_df"].choices = fill_helper.get_choices( run, DIFFERENTIALLY_EXPRESSED_PROTEINS_DF ) - self.fields["gene_mapping"].choices = fill_helper.get_choices( - run, "gene_mapping" + self.fields["gene_mapping_step_instance"].choices = fill_helper.get_choices( + run, "gene_mapping_df" ) protein_df_instance_id = self.data.get( "proteins_df", self.fields["protein_df"].choices[0][0] From 602f3dc94f8136955a1be80bdaf85bc2e057807a Mon Sep 17 00:00:00 2001 From: selenabr <116892527+selenabr@users.noreply.github.com> Date: Thu, 13 Jun 2024 13:39:56 +0200 Subject: [PATCH 06/22] Updated tests (#500) Co-authored-by: janni.roebbecke Co-authored-by: lucatreide Co-authored-by: henning Co-authored-by: AK --- .github/workflows/ci.yml | 8 +- docs/dev-guide.md | 130 - protzilla/data_integration/di_plots.py | 4 + .../data_preprocessing/peptide_filter.py | 3 +- protzilla/disk_operator.py | 10 +- protzilla/history.py | 236 - protzilla/importing/metadata_import.py | 4 +- protzilla/run.py | 720 +- protzilla/run_helper.py | 158 - protzilla/runner.py | 33 +- protzilla/steps.py | 7 +- protzilla/workflow.py | 150 - tests/conftest.py | 146 +- .../test_differential_expression.py | 106 +- .../data_analysis/test_protein_graphs.py | 1 + .../test_plots_data_integration.py | 29 +- .../test_filter_proteins.py | 12 +- .../data_preprocessing/test_filter_samples.py | 49 +- .../data_preprocessing/test_normalisation.py | 8 +- .../test_outlier_detection.py | 27 +- .../test_peptide_preprocessing.py | 62 +- .../data_preprocessing/test_transformation.py | 29 +- .../importing/test_metadata_import.py | 201 +- .../importing/test_peptide_import.py | 9 +- tests/protzilla/test_history.py | 186 - tests/protzilla/test_run.py | 165 +- tests/protzilla/test_run_helper.py | 119 - tests/protzilla/test_runner.py | 68 +- tests/protzilla/test_runner_cli.py | 9 +- tests/protzilla/test_workflow_helper.py | 233 - ...230605 24h prodi DMSO report.pg_matrix.tsv | 1992 + .../DIANN_data/correct_metadata_table.csv | 21 + .../DIANN_data/correct_protein_df.csv | 39721 ++++++++++++++++ .../DIANN_data/sample run relationship.xlsx | Bin 0 -> 10855 bytes tests/test_workflows/example_workflow.yaml | 70 + .../example_workflow_short.yaml | 27 + tests/ui/test_views.py | 55 +- tests/ui/test_views_helper.py | 94 - ui/runs/fields.py | 2 +- ui/runs/form_mapping.py | 2 +- ui/runs/forms/base.py | 2 +- ui/runs/forms/data_analysis.py | 2 +- ui/runs/forms/data_integration.py | 2 +- ui/runs/forms/fill_helper.py | 2 +- ui/runs/forms/importing.py | 34 +- ui/runs/views.py | 2 +- user_data/workflows/only_import.yaml | 10 + .../only_import_and_filter_proteins.yaml | 16 + user_data/workflows/test-run-empty.yaml | 2 + 49 files changed, 42580 insertions(+), 2398 deletions(-) delete mode 100644 docs/dev-guide.md delete mode 100644 protzilla/history.py delete mode 100644 tests/protzilla/test_history.py delete mode 100644 tests/protzilla/test_run_helper.py delete mode 100644 tests/protzilla/test_workflow_helper.py create mode 100644 tests/test_data/DIANN_data/20230605 24h prodi DMSO report.pg_matrix.tsv create mode 100644 tests/test_data/DIANN_data/correct_metadata_table.csv create mode 100644 tests/test_data/DIANN_data/correct_protein_df.csv create mode 100644 tests/test_data/DIANN_data/sample run relationship.xlsx create mode 100644 tests/test_workflows/example_workflow.yaml create mode 100644 tests/test_workflows/example_workflow_short.yaml delete mode 100644 tests/ui/test_views_helper.py create mode 100644 user_data/workflows/only_import.yaml create mode 100644 user_data/workflows/only_import_and_filter_proteins.yaml create mode 100644 user_data/workflows/test-run-empty.yaml diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index d4a82146..456fd14f 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -42,12 +42,12 @@ jobs: - name: Analysing the code with autoflake run: python -m autoflake --in-place --remove-all-unused-imports --remove-unused-variables --expand-star-imports --ignore-init-module-imports $(git ls-files '*.py') - + - name: run tests, build coverage file run: | - export PYTHONPATH=. pytest - pytest --cache-clear --cov=. --cov-branch --cov-report term-missing tests/ - + export PYTHONPATH=. pytest + pytest --cache-clear --cov=. --cov-report term-missing tests/ + - name: format with black run: python -m black . diff --git a/docs/dev-guide.md b/docs/dev-guide.md deleted file mode 100644 index 44528c13..00000000 --- a/docs/dev-guide.md +++ /dev/null @@ -1,130 +0,0 @@ - -# dev guide -This is a guide for developers starting to work on PROTzilla and will provide an overview of how to extend the application. - -### Project Structure -PROTzilla is structured as follows: -- [PROTzilla2/protzilla](../protzilla) package: contains all methods to perform calculations and generate plots. It can be used without the UI. You can find more information about each method in the corresponding docstring. -- [PROTzilla2/protzilla/constants/workflow_meta.json](../protzilla/constants/workflow_meta.json): contains the metadata of all available methods in PROTzilla. -- [PROTzilla2/run.py](../protzilla/run.py): The Run class oversees run management, calculations, workflow configuration, and history tracking, including result accumulation upon calling next. -- [PROTzilla2/history.py](../protzilla/history.py): the History class stores the chosen method, parameters, plots, output dataframe and specific outputs of all already calculated steps. -- [PROTzilla2/runner.py](../protzilla/runner.py): the runner is able to execute a given workflow without the the UI. -- [PROTzilla2/ui](../ui): contains the Django apps main and runs, therefore all code that has to do with the frontend. -- [PROTzilla2/user_data](../user_data): contains all data produced by the user, including the user's workflows and the processed data and plots produced by a run. For each run a new folder is created - - -### Concept of a Workflow and Workflow Structure -A workflow serves as a blueprint for a run, containing all the necessary statistical methods, visualizations, and their respective parameters used to analyse protein data. This blueprints can be reused by scientists to get reproducible results. - -A workflow in protzilla is organized in four **sections** "importing", "data_preprocessing", "data_analysis" and "data_integration". Each section is divided into **steps**, which are groups of similar **methods** that can be added to the workflow. Also, methods can have **parameters**. -All implemented methods are listed in `protzilla/constants/workflow_meta.json` following the tree structure `section > step > method > parameter`. - -> [!NOTE] -> remember the hierarchy `section > step > method > parameter` - - -### Run class -On the class side, the Run deals with being able to continue runs and starting new ones. -On the instance side, the Run deals with making calculations by calling methods that return a dataframe and other outputs in a dict (importing, data preprocessing) or just a dict of outputs (data analysis, data integration). It also creates plots that belong to another step (preprocessing) or are a step on their own (analysis, integration) and exports them. -Workflow manipulation is also handled in the Run. The run class is responsible for knowing the configuration of the run's workflow and the current location. -Each run has a History, which holds the previous results. When `next` is called, another step is added to the history. - - -### History class -The History is mainly responsible for knowing the chosen method, parameters, plots, output dataframe and specific outputs (e.g. dropouts or p-values) of all previous steps. Depending on the chosen storage mode, it returns the data from memory or disk when it is requested. It also holds the names users can attach to executed methods to use them in later calcuations. - - -### How do the run, workflow, and history interact with one another? -The directory `user_data/workflows` contains **workflow** templates. -They store the configuration of an analysis, which contains the order of steps and also their corresponding selected methods and parameters. -A workflow can be shared among users for reproducible results.
-When a user starts PROTzilla, they are asked to create a new **run** and select a workflow template. -A Run object from `protzilla/run.py` will be created, and a new folder with the name of the run will be generated in `user_data/runs`. -The selected workflow will be copied into the run directory and will be the `workflow_config` of this run.
-The run will follow the steps listed in the `workflow_config` and selected methods and parameter values as default. -If no default methods or parameters are selected within `workflow_config`, default values from `workflow_meta.json` will be used.
-The **history** contains the outputs of steps that have already been executed. If the user chooses to return to the previous step and presses the back button, the previous step will be loaded from history. - -> [!WARNING] -> never delete the `hello123` run as for some reason the tests on github will fail then. We tried to identify the problem but our best solution was to just leave `hello123` where it was. - -### Calculation methods -The three folders below each represent a **section**. Each file in these folders represents a **step**. In a file there is a function for each method. - -#### PROTzilla2/protzilla/importing -This folder contains the methods used for importing mass spectrometry data and metadata. Its methods always have the following signature: an input dataframe and any other method parameters. They return a protein intensities dataframe and a dict of other outputs. - -#### PROTzilla2/protzilla/data_preprocessing -This folder contains the methods for filtering, imputation, normalisation, outlier detection and methods to create the corresponding plots. Its methods have the same signature as importing, receiving an input dataframe plus the method's parameters and returning a transformed dataframe together with a dict that contains other outputs specific to the method, such as dropouts, p-values or other values. The dataframe that is used as input is the output of the previous importing/data preprocessing step. - -#### PROTzilla2/protzilla/data_analysis -This folder contains methods that are used to analyse the outputs of the data preprocessing section and previous data analysis steps. Always using the previous output dataframe as input gets replaced by naming the steps that should be used and choosing the right named step as input. On this section the calculation and the plots be can separated from each other. In the case of the calculation methods, they return a dictionary including dataframes and other outputs. On the other hand, plot methods return a list of figures. - - - -### Workflow Meta File -The workflow meta file contains all the needed metainformation for each of the implemented methods in PROTzilla. A method's position in the JSON hierarchy determines its location (`section > step > method`). Also other information such as the name, description and parameters of the method are specified on this file. It is a key file to then build PROTzilla's frontend. - -#### Method parameters specification -The parameters for each method can be specified in the form of a dict in the `protzilla/constants/workflow_meta.json` file. To each parameter a name, a type and a default are assigned. Then depending on the type of the parameter also other information needs to be specified in the workflow meta file, that will then affect how the input parameter fields in the frontend are built. - -### Why are there #TODOs with a number in the code? -To-dos are small issues or suggestions listed in [the issues list with label "todo"](https://github.com/cschlaffner/PROTzilla2/issues?q=is%3Aissue+is%3Aopen+label%3Atodo). -The number corresponds to the issue ID. - -### The runner -PROTzilla can be used not only in the browser but also from the **command line** without a graphical user interface. The runner is a practical tool for this. -It can execute an entire workflow without further user input. In the future, the runner should also be executable from the browser. -This way, as soon as researchers created a workflow with the analysis they want to perform, they can get their results for multiple datasets with less effort. -PROTzilla is thus intended to be an **independent python package** that also works independently of UI and Django. -> [!NOTE] -> think about whether new features should be implemented in the `protzilla` or `ui` folder. - -### UI - -#### Structure of Django project. -The UI is built with the web framework Django. The folder /ui encapsulates all the frontend related code. - -A Django project is structured in such way that the root folder (in this case /ui) contains application folders. In PROTzilla there are two applications main and runs. The primary functionality of PROTzilla is in the runs application. - -In Django, web pages and other content are delivered by views. Each view is represented by a Python function, which is usually found in the views.py file. A view function takes a HTTP request and returns a HTTP response, for example this can be a HTML page or a HTTP error. Also, Django allows to create HTML pages dynamically with templates. A template contains the static parts of the desired HTML output as well as some special syntax describing how dynamic content will be inserted. Templates are usually located at the templates folder inside the app folder. For further information on Django see their [documentation](https://docs.djangoproject.com/). - -PROTzilla's frontend has two main pages: the home page, where runs can be created and continued and the run page, where the user can upload, process, analyse and plot proteomics data according to the selected workflow or add and delete any steps in the current workflow. - -#### The "run" page -The "run" page is generated by the Django view function called `detail`, which uses the Django template `details.html`. This view, along with the template, serves as the foundation for the PROTzillas frontend and therefore a good starting point to understand how the frontend is structured. - -##### Layout -The layout of the "run" page consists of the History displayed at the top, the current step at the bottom, and a sidebar. Regardless of the current step, the HTML page's structure remains the same. Whenever an action, such as adding a step or pressing the next/back button, occurs, it triggers the detail view to be called again by the corresponding add, next, or back view, and subsequently renders the page with the updated state obtained from the Run and History classes. - -##### Input fields -The `fields.py` file contains methods for creating various input fields required for the method parameters. The field type and default information is specified in the workflow meta file. The `make_parameter_input` function selects the appropriate template based on the field type. But before creating the fields, the method `insert_special_params` in `run_helper` manages the display and retrieval of runtime-specific information, like outputs from previous steps, the groups present in the metadata uploaded by the user or protein database information. To see a more detailed example on how each special parameter works see [`insert_special_params`](protzilla/run_helper.py) - -Certain fields need to be loaded without the web page being reloaded. This includes: selecting the method for the current step, populating dropdown categories based on a previous selection, and loading dynamically additional fields based on specific choices. To achieve this, AJAX asyncronous server requests are employed. HTML elements with special functionalities in the dropdowns are identified by class IDs that initiate this behavior. The JavaScript code responsible for handling these AJAX requests can be found in `run/templates/runs/dynamic_methods.html`. - - - - -# tutorial - -### adding a new method/step -To provide an example, I will display the `knn` method within the step `imputation`. Feel free to have a look at the code by yourself. -0. Think about what method you want to implement, what parameters should be selectable by the user and to what step does the method belong. - -1. Add your method in `protzilla/constants/workflow_meta.json` within the corresponding step and also specify parameters and their defaults. -

- -2. Implement your method in `protzilla/
/.py` corresponding to the section and step you added in `workflow_meta.json` with the parameters specified there.
- Importing and preprocessing steps should return the **dataframe**, that gets handed to the next step and a **dict** with further results. Data analysis and integration steps just return a dict.
- **Do not forget to explain your method with python docstrings!** - - in `protzilla/data_preprocessing/imputation.py`: -

- -3. Preprocessing methods also need a plot generation implementation which returns a list of `plotly.graph_objects.Figure`: -

-4. Link the implementation to the entry in `workflow_meta.json` in the `method_map` and `plot_map` data structures located in `protzilla/constants/location_mapping.py` -

-

-5. Write **tests** for your new method (experiment with TDD and write tests before implementing the method, it can save you some time:) ) - diff --git a/protzilla/data_integration/di_plots.py b/protzilla/data_integration/di_plots.py index b4f1a7e5..74d50ff1 100644 --- a/protzilla/data_integration/di_plots.py +++ b/protzilla/data_integration/di_plots.py @@ -75,6 +75,9 @@ def GO_enrichment_bar_plot( return dict(messages=[dict(level=logging.ERROR, msg=msg)]) if not isinstance(gene_sets, list): gene_sets = [gene_sets] + if value not in ["fdr", "p-value"]: + msg = "Invalid value. Value must be either 'fdr' or 'p-value'." + return dict(messages=[dict(level=logging.ERROR, msg=msg)]) # remove all Gene_sets that are not in categories df = input_df[input_df["Gene_set"].isin(gene_sets)] @@ -105,6 +108,7 @@ def GO_enrichment_bar_plot( elif value == "p-value": column = "P-value" if restring_input else "Adjusted P-value" + if colors == "" or colors is None or len(colors) == 0: colors = PROTZILLA_DISCRETE_COLOR_SEQUENCE size_y = top_terms * 0.5 * len(gene_sets) diff --git a/protzilla/data_preprocessing/peptide_filter.py b/protzilla/data_preprocessing/peptide_filter.py index 6df0d611..3b1caee9 100644 --- a/protzilla/data_preprocessing/peptide_filter.py +++ b/protzilla/data_preprocessing/peptide_filter.py @@ -4,7 +4,7 @@ def by_pep_value( - protein_df: pd.DataFrame, peptide_df: pd.DataFrame, threshold: float + peptide_df: pd.DataFrame, threshold: float ) -> dict: """ This function filters out all peptides with a PEP value (assigned to all samples @@ -30,7 +30,6 @@ def by_pep_value( filtered_peptides_list = filtered_peptides["Sequence"].unique().tolist() return dict( - protein_df=protein_df, peptide_df=peptide_df, filtered_peptides=filtered_peptides_list, ) diff --git a/protzilla/disk_operator.py b/protzilla/disk_operator.py index 9f36c67d..c059fc71 100644 --- a/protzilla/disk_operator.py +++ b/protzilla/disk_operator.py @@ -108,16 +108,18 @@ def read_run(self, file: Path | None = None) -> StepManager: def write_run(self, step_manager: StepManager) -> None: with ErrorHandler(): if not self.run_dir.exists(): - self.run_dir.mkdir(parents=True) + self.run_dir.mkdir(parents=True, exist_ok=True) if not self.dataframe_dir.exists(): - self.dataframe_dir.mkdir(parents=True) + self.dataframe_dir.mkdir(parents=True, exist_ok=True) self.clean_dataframes_dir(step_manager) run = {} run[KEYS.CURRENT_STEP_INDEX] = step_manager.current_step_index run[KEYS.DF_MODE] = step_manager.df_mode run[KEYS.STEPS] = [] for step in step_manager.all_steps: - run[KEYS.STEPS].append(self._write_step(step)) + run[KEYS.STEPS].append( + self._write_step(step) + ) # TODO this takes a long time self.yaml_operator.write(self.run_file, run) def read_workflow(self) -> StepManager: @@ -170,6 +172,8 @@ def clean_dataframes_dir(self, steps: StepManager) -> None: def clear_upload_dir(self) -> None: # TODO in general our way of handling file uploads is kind of non-straightforward, maybe we should switch # to directly using the FileUpload provided by Django instead of the work-around with the path of the upload as a str + if not paths.UPLOAD_PATH.exists(): + return with ErrorHandler(): upload_dir = paths.UPLOAD_PATH for file in upload_dir.iterdir(): diff --git a/protzilla/history.py b/protzilla/history.py deleted file mode 100644 index 31c88362..00000000 --- a/protzilla/history.py +++ /dev/null @@ -1,236 +0,0 @@ -import json -import shutil -from dataclasses import dataclass -from pathlib import Path - -import pandas as pd -from joblib import dump, load -from sklearn.base import BaseEstimator - -from .constants.paths import RUNS_PATH - - -class History: - """ - This class has the responsibility to save what methods were previously executed - in a Run. Each Run has one History. It is responsible for saving dataframes to - disk. - - :param steps: is a list of the steps that have been executed, represented by - ExecutedStep instances. - :type steps: list[ExecutedStep] - :param df_mode: determines if the dataframe of a completed step that is added to the - history is saved to disk and not held im memory ("disk" mode), held in memory - but not saved to disk ("memory" mode) or both ("disk_memory" mode). - :type df_mode: str - :param run_name: is the name of the run a history instance belongs to. It is used to - save things at the correct disk location. - :type run_name: str - """ - - @classmethod - def from_disk(cls, run_name: str, df_mode: str): - instance = cls(run_name, df_mode) - with open(RUNS_PATH / run_name / "history.json", "r") as f: - history_json = json.load(f, object_hook=load_objects) - - for index, step in enumerate(history_json): - df_path = instance.df_path(index) - if df_path.exists() and "memory" in instance.df_mode: - df = pd.read_csv(df_path) - else: - df = None - instance.step_names.append(step["name"]) - instance.steps.append( - ExecutedStep( - step["section"], - step["step"], - step["method"], - step["parameters"], - _dataframe=df, - dataframe_path=df_path if df_path.exists() else None, - outputs=step["outputs"], - messages=[], - plots=[], - ) - ) - return instance - - def __init__(self, run_name: str, df_mode: str): - assert df_mode in ("disk", "memory", "disk_memory") - - self.df_mode = df_mode - self.run_name = run_name - self.steps: list[ExecutedStep] = [] - self.step_names = [] - Path(f"{RUNS_PATH}/{run_name}").mkdir(exist_ok=True) - - def add_step( - self, - section: str, - step: str, - method: str, - parameters: dict, - dataframe: pd.DataFrame | None, - outputs: dict, - messages: list[dict] = [], - plots: list = [], - name: str | None = None, - ): - assert "dataframe" not in outputs, "output can not be named 'dataframe'" - df_path = None - df = None - if "disk" in self.df_mode and dataframe is not None: - index = len(self.steps) - df_path = self.df_path(index) - df_path.parent.mkdir(parents=True, exist_ok=True) - dataframe.to_csv(df_path, index=False) - if "memory" in self.df_mode: - df = dataframe - executed_step = ExecutedStep( - section, - step, - method, - parameters, - df, - df_path, - outputs, - messages, - plots, - ) - self.steps.append(executed_step) - self.step_names.append(None) - self.name_step_in_history(-1, name) # to have checks only in name_step - self.save() - - def name_step_in_history(self, index, name): - if not name or self.step_names[index] == name: - return - assert ( - self.step_names[index] is None - ), f"step already has a name: {self.step_names[index]}" - assert name not in self.step_names, f"name {name} is already taken" - self.step_names[index] = name - - def output_keys_of_named_step(self, name): - if not name or name == "None": - return ["---"] - for saved_name, step in zip(self.step_names, self.steps): - if saved_name == name: - options = list(step.outputs.keys()) - if step.has_dataframe: - options.insert(0, "dataframe") - return options - raise ValueError(f"no step named '{name}'") - - def output_of_named_step(self, name, output): - if not name or name == "None": - return "" - for saved_name, step in zip(self.step_names, self.steps): - if saved_name == name: - if output == "dataframe": - return step.dataframe - return step.outputs[output] - raise ValueError(f"no step named '{name}'") - - def pop_step(self): - self.step_names.pop() - step = self.steps.pop() - df = step.dataframe - if "disk" in self.df_mode and step.dataframe_path: - step.dataframe_path.unlink() - self.save() - return step, df - - def save(self): - if (history_dfs_path := RUNS_PATH / self.run_name / "history_dfs").exists(): - shutil.rmtree(history_dfs_path) - - to_save = [] - for index, (name, step) in enumerate(zip(self.step_names, self.steps)): - to_save.append( - dict( - section=step.section, - step=step.step, - method=step.method, - name=name, - parameters=self.serialize( - step.parameters, index, step.section, step.step, step.method - ), - outputs=self.serialize( - step.outputs, index, step.section, step.step, step.method - ), - ) - ) - history_json = json.dumps(to_save, indent=2) - with open(RUNS_PATH / self.run_name / "history.json", "w") as f: - f.write(history_json) - - def serialize(self, d, index, section, step, method): - cleaned = {} - for key, value in d.items(): - if isinstance(value, pd.DataFrame) or isinstance(value, pd.Series): - filename = f"{index}-{section}-{step}-{method}-{key}.csv" - - path = RUNS_PATH / self.run_name / "history_dfs" / filename - path.parent.mkdir(exist_ok=True) - value.to_csv(path) - cleaned[key] = {"__dataframe__": True, "path": str(path)} - elif isinstance(value, BaseEstimator): - filename = f"{index}-{section}-{step}-{method}-{key}.joblib" - path = RUNS_PATH / self.run_name / "sklearn_estimators" / filename - path.parent.mkdir(exist_ok=True) - dump(value, path) - cleaned[key] = {"__sklearn-estimator__": True, "path": str(path)} - else: - cleaned[key] = value - return cleaned - - def df_path(self, index: int): - return RUNS_PATH / self.run_name / f"dataframes/df_{index}.csv" - - def number_of_steps_in_section(self, section): - return len(list(filter(lambda step: step.section == section, self.steps))) - - -@dataclass(frozen=True) -class ExecutedStep: - """ - This class represents a step that was executed in a run. It holds the outputs of - that step. Instances of this class are not supposed to change. - """ - - section: str - step: str - method: str - parameters: dict - _dataframe: pd.DataFrame | None - dataframe_path: Path | None - outputs: dict - messages: list[dict] - plots: list - - @property - def has_dataframe(self) -> bool: - return self._dataframe is not None or self.dataframe_path is not None - - @property - def dataframe(self) -> pd.DataFrame | None: - """ - :return: The dataframe that was the output of this step. Loads from disk if - necessary. - """ - if self._dataframe is not None: - return self._dataframe - if self.dataframe_path is not None: - return pd.read_csv(self.dataframe_path) - return None - - -def load_objects(dct): - if dct.get("__dataframe__", False): - return pd.read_csv(dct["path"], index_col=0) - elif dct.get("__sklearn-estimator__", False): - return load(dct["path"]) - else: - return dct diff --git a/protzilla/importing/metadata_import.py b/protzilla/importing/metadata_import.py index 737ba442..64ab0d50 100644 --- a/protzilla/importing/metadata_import.py +++ b/protzilla/importing/metadata_import.py @@ -60,7 +60,6 @@ def metadata_import_method( meta_df, msg = file_importer(file_path) if meta_df.empty: return dict( - metadata_df=None, messages=[dict(level=logging.ERROR, msg=msg)], ) @@ -121,7 +120,6 @@ def metadata_import_method_diann( meta_df, msg = file_importer(file_path) if meta_df.empty: return dict( - metadata_df=None, messages=[dict(level=logging.ERROR, msg=msg)], ) @@ -191,7 +189,7 @@ def metadata_column_assignment( return dict( protein_df=protein_df, metadata_df=metadata_df, - messages=[dict(level=logging.INFO, msg=msg)], + messages=[dict(level=logging.ERROR, msg=msg)], ) # rename given in metadata_sample_column column to "Sample" if it is called otherwise renamed_metadata_df = metadata_df.rename( diff --git a/protzilla/run.py b/protzilla/run.py index 51cf4e19..1f451e89 100644 --- a/protzilla/run.py +++ b/protzilla/run.py @@ -1,586 +1,184 @@ -import base64 -import json +from __future__ import annotations + import logging -import shutil +import threading import traceback -from io import BytesIO -from pathlib import Path -from shutil import rmtree - -import plotly -from PIL import Image - -from .constants.location_mapping import location_map, method_map, plot_map -from .constants.paths import RUNS_PATH, WORKFLOW_META_PATH, WORKFLOWS_PATH -from .history import History -from .run_helper import log_messages -from .utilities import format_trace -from .workflow import ( - get_global_index_of_step, - get_parameter_type, - get_workflow_default_param_value, - set_output_name, -) +import protzilla.constants.paths as paths +from protzilla.steps import Messages, Output, Plots, Step +from protzilla.utilities import format_trace -class Run: - """ - A class to represent a complete data analysis run in protzilla. - - :param run_path: the path to this runs' dir - :type run_path: str - :param workflow_config: Contains the contents of the workflow .json - that was selected for this run at first. It is always updated when - the workflow gets changed throughout the run (e.g. change of a parameter). - :type workflow_config: dict - :param run_name: name of the run - :type run_name: str - :param history: an instance of the history class to access the history of this run - :type history: protzilla.History - :param step_index: index of the current step over all steps in the workflow - :type step_index: int - :param workflow_meta: contains contents of the workflow meta file that contains all - methods and parameters that exist in protzilla - :type workflow_meta: dict - - :param section: current section - :type section: str - :param step: current step - :type step: str - :param method: current method - :type method: str - :param df: dataframe that will be used as input for the next data preprocessing step - (Not used in data analysis! Due to the more flexible dataflow during analysis - the input dataframe for an analysis step needs to be selectable in the frontend and is an - input parameter for each new step) - :type df: pandas.DataFrame - :param result_df: contains the modified intensity dataframe after a step - :type result_df: pandas.DataFrame - :param current_out: contains other outputs from the current step - :type current_out: dict - :param current_parameters: calculation parameters that were used to calculate the current step - (e.g. to update workflow_config correctly) - :type current_parameters: dict - :param current_plot_parameters: plot parameters that were used to generate plots for the - current step (Not used in data analysis! A plot is its own step in that section - to allow for more flexibility) - :type current_plot_parameters: dict - :param calculated_method: method that was used to calculate the current step - :type calculated_method: str - :param plots: contains the plots generated in the current step - :type plots: list[Figure] - :param plotted_for_parameters: calculation parameters that were used to generate the results that were used to generate current plots, not used in data analysis - :type plotted_for_parameters: dict - """ - - @classmethod - def available_runs(cls): - available_runs = [] - if RUNS_PATH.exists(): - for p in RUNS_PATH.iterdir(): - if p.name.startswith("."): - continue - available_runs.append(p.name) - return available_runs - - @classmethod - def available_workflows(cls): - available_workflows = [] - if WORKFLOWS_PATH.exists(): - for p in WORKFLOWS_PATH.iterdir(): - if p.name.startswith("."): - continue - available_workflows.append(p.stem) - return available_workflows - - @classmethod - def create(cls, run_name, workflow_config_name="standard", df_mode="memory"): - run_path = Path(f"{RUNS_PATH}/{run_name}") - if run_path.exists(): - rmtree(run_path) - run_path.mkdir() - run_config = dict(workflow_config_name=workflow_config_name, df_mode=df_mode) - with open(run_path / "run_config.json", "w") as f: - json.dump(run_config, f) - history = History(run_name, df_mode) - return cls(run_name, workflow_config_name, df_mode, history, run_path) - - @classmethod - def continue_existing(cls, run_name): - run_path = Path(f"{RUNS_PATH}/{run_name}") - with open(f"{run_path}/run_config.json", "r") as f: - run_config = json.load(f) - current_messages = [] - try: - history = History.from_disk(run_name, run_config["df_mode"]) - except FileNotFoundError: - history = History(run_name, run_config["df_mode"]) - msg = "Missing calculated Data. Restarted Run." - current_messages.append(dict(level=logging.ERROR, msg=msg)) - - return cls( - run_name, - run_config["workflow_config_name"], - run_config["df_mode"], - history, - run_path, - current_messages, - ) - def __init__( - self, - run_name, - workflow_config_name, - df_mode, - history, - run_path, - current_messages=None, - ): - if current_messages is None: - current_messages = [] - self.run_name = run_name - self.history = history - self.df = self.history.steps[-1].dataframe if self.history.steps else None - self.step_index = len(self.history.steps) - self.run_path = run_path - self.workflow_config_name = workflow_config_name - - self.workflow_config = self.read_local_workflow() - - with open(WORKFLOW_META_PATH, "r") as f: - self.workflow_meta = json.load(f) - - # make these a result of the step to be compatible with CLI? - self.section, self.step, self.method = self.current_workflow_location() - self.result_df = None - self.current_out = None - self.current_messages = current_messages - self.calculated_method = None - self.current_parameters = {} - self.current_plot_parameters = {} - self.plotted_for_parameters = None - self.plots = [] - self.current_out_sources = {} - - def update_workflow_config(self, params, location=None, update_params=True): - if not location: - (section, step, method) = self.current_workflow_location() - else: - (section, step, method) = location - index = self.step_index_in_current_section() - - if update_params: - parameters_no_file_path = { - k: params[k] - for k in params - if get_parameter_type(self.workflow_meta, section, step, method, k) - != "file" - } - self.workflow_config["sections"][section]["steps"][index][ - "parameters" - ] = parameters_no_file_path - - self.workflow_config["sections"][section]["steps"][index]["method"] = method - self.write_local_workflow() - - def perform_current_calculation_step(self, parameters): - self.perform_calculation_from_location(*self.current_run_location(), parameters) - - def perform_current_calculation_step_and_next(self, parameters, name=None): - self.perform_calculation_from_location(*self.current_run_location(), parameters) - self.next_step(name=name) - - def perform_calculation_from_location(self, section, step, method, parameters): - location = (section, step, method) - self.perform_calculation(method_map[location], parameters) - self.update_workflow_config(parameters, location) - - def perform_calculation(self, method_callable, parameters: dict): - self.section, self.step, self.method = location_map[method_callable] - call_parameters = self.exchange_named_outputs_with_data(parameters) - if "metadata_df" in call_parameters: - call_parameters["metadata_df"] = self.metadata - if "peptide_df" in call_parameters: - call_parameters["peptide_df"] = self.peptide_data - if "run_name" in call_parameters: - call_parameters["run_name"] = self.run_name - - calculation_failed = False - if self.section in ["importing", "data_preprocessing"]: - try: - self.result_df, self.current_out = method_callable( - self.df, **call_parameters - ) - if "messages" in self.current_out and any( - messages["level"] == logging.ERROR - for messages in self.current_out["messages"] - ): - calculation_failed = True - self.current_messages.extend(self.current_out.pop("messages", {})) - except Exception as e: - calculation_failed = True - msg = f"An error occurred while calculating this step: {e.__class__.__name__} {e} Please check your parameters or report a potential programming issue." - self.current_out = {} - self.current_messages.append( - dict( - level=logging.ERROR, - msg=msg, - trace=format_trace(traceback.format_exception(e)), - ) - ) - else: - self.result_df = None - try: - self.current_out = method_callable(**call_parameters) - if "messages" in self.current_out and any( - messages["level"] == logging.ERROR - for messages in self.current_out["messages"] +def get_available_run_names() -> list[str]: + if not paths.RUNS_PATH.exists(): + logging.warning(f"No runs have been found in {paths.RUNS_PATH}.") + return [] + return [ + directory.name + for directory in paths.RUNS_PATH.iterdir() + if not directory.name.startswith(".") + ] + + +class Run: + class ErrorHandlingContextManager: + def __init__(self, run): + self.run = run + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_value, tb): + if exc_type: + formatted_trace = format_trace(traceback.format_exception(exc_value)) + if ( + hasattr(self.run, "current_step") + and self.run.current_step is not None ): - calculation_failed = True - self.current_messages.extend(self.current_out.pop("messages", [])) - except Exception as e: - calculation_failed = True - self.current_out = {} - msg = f"An error occurred while calculating this step: {e.__class__.__name__} {e} Please check your parameters or report a potential programming issue." - self.current_messages.append( - dict( - level=logging.ERROR, - msg=msg, - trace=format_trace(traceback.format_exception(e)), + self.run.current_step.messages.append( + dict( + level=logging.ERROR, + msg=( + f"An error occurred: {exc_value.__class__.__name__}: {exc_value}." + f"Please check your parameters or report a potential programming issue if this is unexpected." + ), + trace=formatted_trace, + ) ) - ) - - self.plots = [] # reset as not up to date anymore - self.current_parameters[self.method] = parameters - self.calculated_method = self.method - - if calculation_failed: - self.result_df = None - self.current_parameters.pop(self.method, None) - self.calculated_method = None - - def calculate_and_next( - self, method_callable, name=None, **parameters - ): # to be used for CLI - self.perform_calculation(method_callable, parameters) - self.next_step(name=name) - - def create_plot_from_current_location(self, parameters): - location = (section, step, method) = self.current_run_location() - if step == "plot": - self.update_workflow_config(parameters, location=location) - self.create_step_plot(plot_map[location], parameters) - elif plot_map.get(location): - self.workflow_config["sections"][section]["steps"][ - self.step_index_in_current_section() - ]["graphs"] = [parameters] - self.write_local_workflow() - self.create_plot(plot_map[location], parameters) - - if section in ["importing", "data_preprocessing"]: - self.plotted_for_parameters = self.current_parameters.get(method) - self.current_plot_parameters[method] = parameters - else: # not used in data analysis - self.plotted_for_parameters = None - self.current_plot_parameters = {} # expected dict for all_button_parameters - - def create_plot(self, method_callable, parameters): - try: - self.plots = method_callable( - self.df, self.result_df, self.current_out, **parameters - ) - for plot in self.plots: - if isinstance(plot, dict) and "messages" in plot: - self.current_messages.extend(plot["messages"]) - self.plots.remove(plot) - - except Exception as e: - self.plots = [] - msg = f"An error occurred while plotting: {e.__class__.__name__} {e} Please check your parameters or report a potential programming issue." - self.current_messages.append( - dict( - level=logging.ERROR, - msg=msg, - trace=format_trace(traceback.format_exception(e)), - ) - ) + else: + raise exc_value + return True - def create_step_plot(self, method_callable, parameters): - if "term_name" in parameters: - parameters["term_name"] = parameters["term_dict"][1] - call_parameters = self.exchange_named_outputs_with_data(parameters) - if "proteins_of_interest_input" in call_parameters: - del call_parameters["proteins_of_interest_input"] - try: - self.plots = method_callable(**call_parameters) - self.result_df = self.df - self.current_out = {} - self.current_parameters[self.method] = parameters - self.calculated_method = self.method - - for plot in self.plots: - if isinstance(plot, dict) and "messages" in plot: - self.current_messages.extend(plot["messages"]) - self.plots.remove(plot) - except Exception as e: - self.plots = [] - self.result_df = None - self.current_out = {} - msg = f"An error occurred while plotting: {e.__class__.__name__} {e} Please check your parameters or report a potential programming issue." - self.current_messages.append( - dict( - level=logging.ERROR, - msg=msg, - trace=format_trace(traceback.format_exception(e)), - ) - ) - self.current_parameters.pop(self.method, None) - self.calculated_method = None - - def insert_step(self, step_to_be_inserted, section, method, index): - step_dict = dict(name=step_to_be_inserted, method=method, parameters={}) - if section == "data_preprocessing": - step_dict["graphs"] = [{}] - self.workflow_config["sections"][section]["steps"].insert(index, step_dict) - self.write_local_workflow() - - def write_local_workflow(self): - workflow_local_path = f"{self.run_path}/workflow.json" - with open(workflow_local_path, "w") as f: - json.dump(self.workflow_config, f, indent=2) - - def read_local_workflow(self): - workflow_local_path = f"{self.run_path}/workflow.json" - if not Path(workflow_local_path).is_file(): - workflow_template_path = ( - f"{WORKFLOWS_PATH}/{self.workflow_config_name}.json" - ) - shutil.copy2(workflow_template_path, workflow_local_path) + def error_handling(func): + """ + Decorator to handle errors in the run, will log the errors. + :return: + """ - with open(workflow_local_path, "r") as f: - self.workflow_config = json.load(f) - return self.workflow_config + def wrapper(self, *args, **kwargs): + with Run.ErrorHandlingContextManager(self): + return func(self, *args, **kwargs) - def insert_at_next_position(self, step_to_be_inserted, section, method): - if self.section == section: - past_steps_of_section = self.history.number_of_steps_in_section(section) + return wrapper - self.insert_step( - step_to_be_inserted, section, method, past_steps_of_section + 1 - ) - else: - self.insert_step(step_to_be_inserted, section, method, 0) + def auto_save(func): + """ + Decorator to automatically save the run in the background after the function is called. + """ - self.section, self.step, self.method = self.current_workflow_location() + def wrapper(self, *args, **kwargs): + result = func(self, *args, **kwargs) + thread = threading.Thread(target=self._run_write) + thread.daemon = True + thread.start() + self.steps.df_mode = self.df_mode + self.steps.disk_operator = self.disk_operator + return result - def export_workflow(self, name): - with open(f"{WORKFLOWS_PATH}/{name}.json", "w") as f: - json.dump(self.workflow_config, f, indent=2) + return wrapper - def delete_step(self, section, index): - del self.workflow_config["sections"][section]["steps"][index] - self.write_local_workflow() + def __init__( + self, run_name: str, workflow_name: str | None = None, df_mode: str = "disk" + ): + from protzilla.disk_operator import DiskOperator # to avoid a circular import - def next_step(self, name=None): - if name is None: - name = get_workflow_default_param_value( - self.workflow_config, - *self.current_run_location(), - self.step_index_in_current_section(), - "output_name", - ) - try: - parameters = self.current_parameters.get(self.calculated_method, {}) - self.history.add_step(self.section) - self.update_workflow_config(parameters) - index = self.step_index_in_current_section() - self.workflow_config["sections"][self.section]["steps"][index][ - "method" - ] = self.calculated_method - self.name_step(-1, name) - - except AssertionError as e: - self.history.pop_step() - self.current_messages.append(dict(level=logging.ERROR, msg=f"{e}")) - except Exception as e: - self.history.pop_step() - msg = f"An error occurred while saving this step: {e.__class__.__name__} {e} Please check your parameters or report a potential programming issue." - self.current_messages.append( - dict( - level=logging.ERROR, - msg=msg, - trace=format_trace(traceback.format_exception(e)), - ) + self.run_name = run_name + self.workflow_name = workflow_name + self.disk_operator: DiskOperator = DiskOperator(run_name, workflow_name) + + if run_name in get_available_run_names(): + self._run_read() + elif workflow_name: + self.df_mode = df_mode + self._workflow_read() + else: + raise ValueError( + f"No run named {run_name} has been found and no workflow has been provided. Please reference an existing run or provide a workflow to create a new one." ) - else: - self.step_index += 1 - self.section, self.step, self.method = self.current_workflow_location() - self.df = self.result_df - self.result_df = None - self.calculated_method = None - self.current_out = {} - self.current_parameters = {} - self.current_plot_parameters = {} - self.plotted_for_parameters = None - self.plots = [] - self.current_out_sources = {} - - def back_step(self): - self.navigate_relative(-1) - - def navigate_relative(self, steps: int): - """ - Navigates to a step relative to the current step. + def __repr__(self): + return f"Run({self.run_name}) with {len(self.steps.all_steps)} steps." - :param steps: the number of steps to navigate. Negative values navigate backwards. - """ - if steps > 0: - raise Exception(f"Can not navigate to future step.") - elif steps < 0: - assert self.history.steps - for i in range(-steps - 1): - self.history.pop_step() - popped_step, popped_result_df = self.history.pop_step() - self.section = popped_step.section - self.step = popped_step.step - self.method = popped_step.method - self.calculated_method = self.method - self.df = self.history.steps[-1].dataframe if self.history.steps else None - self.result_df = popped_result_df - self.current_out = popped_step.outputs - self.current_messages = popped_step.messages - self.current_parameters = {self.method: popped_step.parameters} - self.current_plot_parameters = {} - # TODO: add plotted_for_parameter to History? - self.plotted_for_parameters = None - self.plots = popped_step.plots - self.step_index += steps - - def navigate(self, section_name: str, step_index: int): - """ - Navigates to a specific step in the run. + @error_handling + def _run_read(self) -> None: + self.steps = self.disk_operator.read_run() + self.steps.disk_operator = self.disk_operator + self.df_mode = self.steps.df_mode - :param step_index: the index of the step to navigate to within the section - :param section_name: the name of the section in which the step is located - """ - # Find the global index of the step within the specified section - global_index = get_global_index_of_step( - self.workflow_config, section_name, step_index - ) - - self.navigate_relative(global_index - self.step_index) - - def current_workflow_location(self): - try: - return self.all_steps()[self.step_index] - except IndexError: - return "", "", "" - - def step_index_in_current_section(self): - steps_before_this_section = 0 - for section, step, method in self.all_steps(): - if section == self.section: - return self.step_index - steps_before_this_section - steps_before_this_section += 1 - raise Exception(f"section {self.section} not found") - - def all_steps(self): - steps = [] - for section_key, section_dict in self.workflow_config["sections"].items(): - for step in section_dict["steps"]: - steps.append((section_key, step["name"], step["method"])) - return steps - - def current_run_location(self): - return self.section, self.step, self.method - - def exchange_named_outputs_with_data(self, parameters): - call_parameters = {} - for k, v in parameters.items(): - param_dict = self.workflow_meta[self.section][self.step][self.method][ - "parameters" - ].get(k) - if param_dict and param_dict.get("type") == "named_output": - # v should consist of values [named_step, output] - assert ( - v is not None - ), f"please set default values for the named_output: {k} in workflow file" - call_parameters[k] = self.history.output_of_named_step(*v) - elif param_dict and param_dict.get("type") == "multi_named_output": - for call_parameter, output_name in param_dict.get("mapping").items(): - call_parameters[call_parameter] = self.history.output_of_named_step( - v, output_name - ) - elif param_dict and param_dict.get("type") == "named_output_v2": - call_parameters[k] = self.current_out_sources[v] - else: - call_parameters[k] = v - return call_parameters + @error_handling + def _run_write(self) -> None: + self.disk_operator.write_run(self.steps) @property - def metadata(self): - for step in self.history.steps: - if step.step == "metadata_import": - return step.outputs["metadata"] - raise AttributeError("Metadata was not yet imported.") + def run_path(self) -> str: + return self.disk_operator.run_dir + + @error_handling + @auto_save + def _workflow_read(self) -> None: + self.steps = self.disk_operator.read_workflow() + + @error_handling + def _workflow_export(self, workflow_name: str | None = None) -> None: + if workflow_name: + self.workflow_name = workflow_name + self.disk_operator.export_workflow(self.steps, self.workflow_name) + + @error_handling + @auto_save + def step_add(self, step: Step, step_index: int | None = None) -> None: + self.steps.add_step(step) + + @error_handling + @auto_save + def step_remove( + self, + step: Step | None = None, + step_index: int | None = None, + section: str | None = None, + ) -> None: + self.steps.remove_step(step=step, step_index=step_index, section=section) + + @error_handling + @auto_save + def step_calculate(self, inputs: dict | None = None) -> None: + self.steps.current_step.calculate(self.steps, inputs) + + @error_handling + @auto_save + def step_plot(self, inputs: dict | None = None) -> None: + self.steps.current_step.plot(inputs) + + @error_handling + @auto_save + def step_next(self) -> None: + self.steps.next_step() + + @error_handling + def step_previous(self) -> None: + self.steps.previous_step() + + @error_handling + def step_goto(self, step_index: int, section: str) -> None: + self.steps.goto_step(step_index, section) + + @error_handling + @auto_save + def step_change_method(self, new_method: str) -> None: + self.steps.change_method(new_method) @property - def has_metadata(self): - try: - _ = self.metadata - except AttributeError: - return False - else: - return True + def current_messages(self) -> Messages: + return self.steps.current_step.messages @property - def peptide_data(self): - for step in self.history.steps: - if step.step == "peptide_import": - return step.outputs["peptide_df"] - raise AttributeError("Peptides were not yet imported.") - - def export_plots(self, format_): - exports = [] - for plot in self.plots: - if isinstance(plot, plotly.graph_objs.Figure): - if format_ in ["eps", "tiff"]: - png_binary = plotly.io.to_image(plot, format="png", scale=4) - img = Image.open(BytesIO(png_binary)).convert("RGB") - binary = BytesIO() - if format_ == "tiff": - img.save(binary, format="tiff", compression="tiff_lzw") - else: - img.save(binary, format=format_) - exports.append(binary) - else: - binary_string = plotly.io.to_image(plot, format=format_, scale=4) - exports.append(BytesIO(binary_string)) - elif isinstance(plot, dict) and "plot_base64" in plot: - plot = plot["plot_base64"] - - if isinstance(plot, bytes): # base64 encoded plots - if format_ in ["eps", "tiff"]: - img = Image.open(BytesIO(base64.b64decode(plot))).convert("RGB") - binary = BytesIO() - if format_ == "tiff": - img.save(binary, format="tiff", compression="tiff_lzw") - else: - img.save(binary, format=format_) - binary.seek(0) - exports.append(binary) - elif format_ in ["png", "jpg"]: - exports.append(BytesIO(base64.b64decode(plot))) - return exports - - def name_step(self, index, name): - self.history.name_step_in_history(index, name) - self.history.save() - set_output_name( - self.workflow_config, - self.section, - self.step_index_in_current_section(), - name, - ) - self.write_local_workflow() + def current_plots(self) -> Plots | None: + return self.steps.current_step.plots + + @property + def current_outputs(self) -> Output: + return self.steps.current_step.output + + @property + def current_step(self) -> Step | None: + return self.steps.current_step diff --git a/protzilla/run_helper.py b/protzilla/run_helper.py index 504d634a..44a7f450 100644 --- a/protzilla/run_helper.py +++ b/protzilla/run_helper.py @@ -1,162 +1,4 @@ -import copy - -import gseapy -import matplotlib.colors as mcolors -import restring - from protzilla.constants.protzilla_logging import MESSAGE_TO_LOGGING_FUNCTION -from protzilla.data_integration.database_query import ( - biomart_database, - uniprot_columns, - uniprot_databases, -) -from protzilla.workflow import get_workflow_default_param_value - - -def insert_special_params(param_dict, run): - if param_dict["type"] == "named_output": - param_dict["steps"] = [name for name in run.history.step_names if name][::-1] - if param_dict.get("optional", False): - param_dict["steps"].append("None") - - if param_dict["default"] and param_dict["default"][0] in param_dict["steps"]: - selected = param_dict["default"][0] - else: - selected = param_dict["steps"][0] if param_dict["steps"] else None - param_dict["outputs"] = run.history.output_keys_of_named_step(selected) - if "sorted" in param_dict and param_dict["sorted"]: - param_dict["outputs"].sort() - - if param_dict["type"] == "multi_named_output": - all_steps = [name for name in run.history.step_names if name][::-1] - required_outputs = list(param_dict["mapping"].values()) - param_dict["steps"] = [ - step - for step in all_steps - if set(required_outputs).issubset( - set(run.history.output_keys_of_named_step(step)) - ) - ] - param_dict["class"] = "dynamic_trigger" - - try: - for output_key in run.history.output_keys_of_named_step( - param_dict["steps"][0] - ): - run.current_out_sources[output_key] = param_dict["steps"][0] - except IndexError: - pass - - if param_dict["type"] == "named_output_v2": - param_dict["steps"] = [name for name in run.history.step_names if name][::-1] - if param_dict["default"] and param_dict["default"][0] in param_dict["steps"]: - selected = param_dict["default"][0] - else: - selected = param_dict["steps"][0] if param_dict["steps"] else None - param_dict["outputs"] = run.history.output_keys_of_named_step(selected) - if "sorted" in param_dict and param_dict["sorted"]: - param_dict["outputs"].sort() - - if "fill" in param_dict: - if param_dict["fill"] == "metadata_non_sample_columns": - # Sample not needed for anova and t-test - param_dict["categories"] = run.metadata.columns[ - run.metadata.columns != "Sample" - ].unique() - elif param_dict["fill"] == "metadata_unknown_columns": - # give selection of existing columns without ["Sample", "Group", "Batch"] - # as they are already named correctly for our purposes - param_dict["categories"] = run.metadata.columns[ - ~run.metadata.columns.isin(["Sample", "Group", "Batch"]) - ].unique() - elif param_dict["fill"] == "metadata_required_columns": - # TODO add other possible metadata columns - # exclude columns that are already in metadata and known to be required - param_dict["categories"] = [ - col - for col in ["Sample", "Group", "Batch"] - if col not in run.metadata.columns - ] - elif param_dict["fill"] == "metadata_columns": - param_dict["categories"] = run.metadata.columns.unique() - elif param_dict["fill"] == "metadata_column_data": - # per default fill with second column data since it is selected in dropdown - param_dict["categories"] = run.metadata.iloc[:, 1].unique() - elif param_dict["fill"] == "dbs_restring": - param_dict["categories"] = restring.settings.file_types - elif param_dict["fill"] == "dbs_gseapy": - param_dict["categories"] = gseapy.get_library_name() - elif param_dict["fill"] == "matplotlib_colors": - param_dict["categories"] = mcolors.CSS4_COLORS - elif param_dict["fill"] == "uniprot_fields": - databases = uniprot_databases() - if databases: - # use the first database as a default, only used to initalize - param_dict["categories"] = uniprot_columns(databases[0]) + ["Links"] - else: - param_dict["categories"] = ["Links"] - elif param_dict["fill"] == "uniprot_databases": - databases = uniprot_databases() - param_dict["default"] = databases[0] if databases else "" - param_dict["categories"] = databases - elif param_dict["fill"] == "biomart_datasets": - # retrieve datasets from BioMart server - database = biomart_database("ENSEMBL_MART_ENSEMBL") - # get the display names instead if the internal names, then map them back - param_dict["categories"] = [ - database.datasets[dataset].display_name for dataset in database.datasets - ] - # param_dict["categories"] = database.datasets - elif param_dict["fill"] == "protein_group_column": - param_dict["categories"] = run.df["Protein ID"].unique() - - if "fill_dynamic" in param_dict: - param_dict["class"] = "dynamic_trigger" - - if param_dict.get("default_select_all", False): - param_dict["default"] = list(param_dict.get("categories", [])) - - if ( - param_dict["type"] == "numeric" - and ("multiple" in param_dict and param_dict["multiple"]) - and isinstance(param_dict["default"], list) - ): - # The default value of a multiselect numeric input is saved as a list of - # numbers, but it needs to be shown in the frontend in the format "1|2|0.12" - param_dict["default"] = "|".join(str(num) for num in param_dict["default"]) - - -def get_parameters(run, section, step, method): - """constructs a dict with all parameters, inserts the correct defaults - and handles special parameter types""" - # deepcopy to not change run.workflow_meta - parameters = copy.deepcopy(run.workflow_meta[section][step][method]["parameters"]) - output = {} - - for key, param_dict in parameters.items(): - # allow for conditional parameters that are only shown if a certain condition - # is met all parameters that depend on the condition need to have a "condition" - # key and the fitting value - if "condition" in param_dict: - if param_dict["condition"] == "metadata": - if not run.has_metadata: - continue - workflow_default = get_workflow_default_param_value( - run.workflow_config, - section, - step, - method, - run.step_index_in_current_section(), - key, - ) - if method in run.current_parameters and key in run.current_parameters[method]: - param_dict["default"] = run.current_parameters[method][key] - elif workflow_default is not None: - param_dict["default"] = workflow_default - - insert_special_params(param_dict, run) - output[key] = param_dict - return output def log_message(level: int = 40, msg: str = "", trace: str | list[str] = ""): diff --git a/protzilla/runner.py b/protzilla/runner.py index 72dea168..50cbf127 100644 --- a/protzilla/runner.py +++ b/protzilla/runner.py @@ -3,8 +3,8 @@ from pathlib import Path from .constants.paths import RUNS_PATH +from .run import Run from .run_helper import log_messages -from .run_v2 import Run from .utilities import random_string @@ -66,57 +66,64 @@ def __init__( self.all_plots = all_plots self.plots_path = Path(f"{self.run.run_path}/plots") - self.plots_path.mkdir(parents=True) + self.plots_path.mkdir(parents=True, exist_ok=True) logging.info(f"Saving plots at {self.plots_path}") log_messages(self.run.current_messages) self.run.current_messages.clear() + self.run._run_write() + def compute_workflow(self): logging.info("------ computing workflow\n") for step in self.run.steps.all_steps: logging.info(f"performing step: {*self.run.steps.current_location,}") if step.section == "importing": self._insert_commandline_inputs(step) - self._perform_current_step() + self._perform_current_step(step.form_inputs) if self.all_plots and step.section == "data_preprocessing": step.plot() - if step.plots: + if step.plots and not step.plots.empty: self._save_plots_html(step) - self.run.step_next() - log_messages(self.run.current_messages) self.run.current_messages.clear() + + if not step.finished: + break + + self.run.step_next() self.run._run_write() def _insert_commandline_inputs(self, step): if step.operation == "msdataimport": - step.inputs["file_path"] = self.ms_data_path + step.form_inputs["file_path"] = self.ms_data_path elif step.operation == "metadataimport": if self.meta_data_path is None: raise ValueError( f"meta_data_path (--meta_data_path=) is not specified, " - f"but is required for {step['name']}" + f"but is required for {step.operation} with {step.display_name}" ) - step.inputs["file_path"] = self.peptides_path + step.form_inputs["file_path"] = self.peptides_path else: - raise ValueError(f"Cannot find step with name {step['name']} in importing") + raise ValueError( + f"Cannot find step with name {step.operation} with {step.display_name} in importing" + ) def _perform_current_step(self, params=None): self.run.current_step.calculate(self.run.steps, params) def _save_plots_html(self, step): for i, plot in enumerate(step.plots): - plot_path = f"{self.plots_path}/{self.run.step_index}-{step.section}-{step['name']}-{step['method']}-{i}.html" + plot_path = f"{self.plots_path}/{self.run.steps.current_step_index}-{step.section}-{step.operation}-{step.display_name}-{i}.html" plot.write_html(plot_path) def _overwrite_run_prompt(self): diff --git a/protzilla/steps.py b/protzilla/steps.py index fb6cded8..8f943925 100644 --- a/protzilla/steps.py +++ b/protzilla/steps.py @@ -245,6 +245,9 @@ def __init__(self, messages: list[dict] = None): def __iter__(self): return iter(self.messages) + def __getitem__(self, key): + return self.messages[key] + def __repr__(self): return f"Messages: {[message['message'] for message in self.messages]}" @@ -545,7 +548,9 @@ def remove_step( if section not in self.sections: raise ValueError(f"Unknown section {section}") if step_index >= len(self.sections[section]): - raise ValueError(f"Step index {step_index} out of bounds for section {section}") + raise ValueError( + f"Step index {step_index} out of bounds for section {section}" + ) step = self.all_steps_in_section(section)[step_index] diff --git a/protzilla/workflow.py b/protzilla/workflow.py index 87e4b1cc..7156300e 100644 --- a/protzilla/workflow.py +++ b/protzilla/workflow.py @@ -1,5 +1,3 @@ -from itertools import zip_longest - from protzilla.constants import paths @@ -11,151 +9,3 @@ def get_available_workflow_names() -> list[str]: for file in paths.WORKFLOWS_PATH.iterdir() if not file.name.startswith(".") and not file.suffix == ".json" ] - - -def get_steps_of_workflow( - workflow_config_dict, -) -> list[dict[str, str | list[dict[str, str]]]]: - workflow_steps = [] - for section, steps in workflow_config_dict["sections"].items(): - workflow_steps.append( - { - "section": section, - "steps": [ - {"name": step["name"], "method": step["method"]} - for step in steps["steps"] - ], - } - ) - return workflow_steps - - -def get_steps_amount_of_workflow( - workflow_config_dict, -) -> int: - amount = 0 - for section, steps in workflow_config_dict["sections"].items(): - amount += steps["steps"].__len__() - - return amount - - -def get_steps_of_workflow_meta(workflow_meta) -> list[dict[str, str | list[str]]]: - workflow_steps = [] - for section, steps in workflow_meta.items(): - workflow_steps.append( - {"section": section, "possible_steps": list(steps.keys())} - ) - return workflow_steps - - -def method_name(workflow_meta, section, step, method): - return workflow_meta[section][step][method]["name"] - - -def get_all_default_params_for_methods(workflow_meta, section, step, method): - method_params = workflow_meta[section][step][method]["parameters"] - return get_defaults(method_params) - - -def get_defaults(method_params): - return { - k: v["default"] if "default" in v.keys() else v - for k, v in method_params.items() - } - - -def get_parameter_type(workflow_meta, section, step, method, param): - return workflow_meta[section][step][method]["parameters"][param]["type"] - - -def get_workflow_default_param_value( - workflow_config, section, step, method, step_index_in_section, param -): - step_dict = workflow_config["sections"][section]["steps"][step_index_in_section] - if step_dict["name"] == step and step_dict["method"] == method: - if param in step_dict["parameters"]: - return step_dict["parameters"][param] - elif param in step_dict: - return step_dict[param] - else: - return None - return None - - -def get_global_index_of_step(workflow_config, section: str, step_index_in_section: int): - """ - Get the global index of a step in a workflow. - - :param workflow_config: The workflow config - :param section: The section of the step - :param step_index_in_section: The index of the step in the section - """ - index = 0 - for s, steps in workflow_config["sections"].items(): - if s == section: - return ( - index + step_index_in_section - if step_index_in_section < steps["steps"].__len__() - else -1 - ) - index += steps["steps"].__len__() - return -1 - - -def is_last_step_in_section(workflow_config, section, step_index_in_section) -> bool: - amount_steps_in_section = workflow_config["sections"][section]["steps"].__len__() - return amount_steps_in_section <= step_index_in_section + 1 - - -def is_last_step(workflow_config, index) -> bool: - """ - Checks if the step is the last step in the workflow. - - :param workflow_config: The workflow config - :param section: The section of the step - :param index: The global index of the step - """ - - return get_steps_amount_of_workflow(workflow_config) <= index + 1 - - -def validate_workflow_parameters(workflow_config, workflow_meta): - # checks if all parameters in workflow_config exist in workflow_meta - for section, steps in workflow_config["sections"].items(): - for step in steps["steps"]: - for param in step["parameters"]: - if ( - param - not in workflow_meta[section][step["name"]][step["method"]][ - "parameters" - ] - ): - raise ValueError( - f"Parameter {param} in step {step['name']} does not exist in workflow_meta" - ) - # TODO 96 test if value is in options when type catergorical or numeric - return True - - -def validate_workflow_graphs(workflow_config, workflow_meta): - # checks if all graphs and their parameters in workflow_config exist in workflow_meta - for section, steps in workflow_config["sections"].items(): - for step in steps["steps"]: - if "graphs" in step: - step_meta = workflow_meta[section][step["name"]][step["method"]] - for i, (graph_config, graph_meta) in enumerate( - zip_longest(step["graphs"], step_meta["graphs"], fillvalue={}) - ): - for param in graph_config: - if param not in graph_meta: - raise ValueError( - f"Graph parameter {param} in graph {i} in step {step['name']} does not exist in workflow_meta" - ) - return True - - -def set_output_name(workflow_config, section, step_index, output_name): - workflow_config["sections"][section]["steps"][step_index][ - "output_name" - ] = output_name diff --git a/tests/conftest.py b/tests/conftest.py index 9d2cbd3a..dc04046b 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,7 +1,8 @@ import base64 import io -import json import logging +import time +import uuid from pathlib import Path from shutil import rmtree @@ -10,7 +11,10 @@ import pytest from PIL import Image -from ..protzilla.constants.paths import PROJECT_PATH, RUNS_PATH +from protzilla.methods.importing import MaxQuantImport +from protzilla.run import Run + +from ..protzilla.constants.paths import RUNS_PATH, TEST_DATA_PATH from ..protzilla.utilities import random_string @@ -23,27 +27,65 @@ def pytest_addoption(parser): ) -@pytest.fixture(scope="session") -def show_figures(request): - return request.config.getoption("--show-figures") +@pytest.fixture(scope="function") +def run_name_and_cleanup(): + # Generate a unique run name + run_name = f"test_run_{uuid.uuid4()}" + run_path = Path(RUNS_PATH) / run_name + # Yield the run name to the test or fixture that uses this fixture + yield run_name -@pytest.fixture(scope="session") -def workflow_meta(): - with open(f"{PROJECT_PATH}/protzilla/constants/workflow_meta.json", "r") as f: - return json.load(f) + # After the test or fixture that uses this fixture is done, remove the directory + while run_path.exists(): + time.sleep(1) + rmtree(run_path) + + +@pytest.fixture +def maxquant_data_file(): + return str((Path(TEST_DATA_PATH) / "data_import" / "maxquant_small.tsv").absolute()) + + +@pytest.fixture(scope="function") +def run_standard(run_name_and_cleanup): + run_name = run_name_and_cleanup + yield Run(run_name=run_name, workflow_name="standard", df_mode="memory") + + +@pytest.fixture(scope="function") +def run_empty(run_name_and_cleanup): + run_name = run_name_and_cleanup + yield Run(run_name=run_name, workflow_name="test-run-empty", df_mode="memory") + + +@pytest.fixture(scope="function") +def run_imported(run_name_and_cleanup, maxquant_data_file): + run_name = run_name_and_cleanup + run = Run(run_name=run_name, workflow_name="test-run-empty", df_mode="memory") + run.step_add(MaxQuantImport()) + run.step_calculate( + { + "file_path": str(maxquant_data_file), + "intensity_name": "iBAQ", + "map_to_uniprot": False, + } + ) + yield run @pytest.fixture(scope="session") -def tests_folder_name(): - return f"tests_{random_string()}" +def show_figures(request): + return request.config.getoption("--show-figures") -@pytest.fixture(scope="session", autouse=True) -def run_test_folder(tests_folder_name): - Path(f"{RUNS_PATH}/{tests_folder_name}").mkdir() - yield - rmtree(Path(f"{RUNS_PATH}/{tests_folder_name}")) +@pytest.fixture(scope="session") +def tests_folder_name(): + name = f"tests_{random_string()}" + yield name + while Path(f"{RUNS_PATH}/{name}").exists(): + time.sleep(1) + rmtree(Path(f"{RUNS_PATH}/{name}")) @pytest.fixture @@ -83,20 +125,6 @@ def debug_logger(): logger.setLevel(logging.INFO) -@pytest.fixture -def example_workflow_short(): - with open( - f"{PROJECT_PATH}/tests/test_workflows/example_workflow_short.json", "r" - ) as f: - return json.load(f) - - -@pytest.fixture -def example_workflow(): - with open(f"{PROJECT_PATH}/tests/test_workflows/example_workflow.json", "r") as f: - return json.load(f) - - @pytest.fixture def df_with_nan(): list_nan = ( @@ -116,6 +144,64 @@ def df_with_nan(): return df_with_nan +@pytest.fixture +def leftover_peptide_df(): + # sample, protein id, sequence, intensity, pep + leftover_peptide_protein_list = ( + ["Sample01", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", np.NAN, 0.037779], + ["Sample02", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", np.NAN, 0.037779], + ["Sample03", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 6923600.0, 0.037779], + ["Sample04", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", np.NAN, 0.037779], + ["Sample05", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 37440000.0, 0.037779], + ) + + peptide_df = pd.DataFrame( + data=leftover_peptide_protein_list, + columns=["Sample", "Protein ID", "Sequence", "Intensity", "PEP"], + ) + peptide_df.sort_values(by=["Sample", "Protein ID"], ignore_index=True, inplace=True) + return peptide_df + + +@pytest.fixture +def filtered_peptides_list(): + return ["AAQSTAMNR"] + + +@pytest.fixture +def peptides_df(): + df = pd.DataFrame( + ( + ["Sample1", "Protein1", "SEQA", 1000000, 0.00001], + ["Sample1", "Protein2", "SEQB", 2000000, 0.00002], + ["Sample1", "Protein2", "SEQC", 3000000, 0.00003], + ["Sample1", "Protein2", "SEQD", 4000000, 0.00004], + ["Sample1", "Protein3", "SEQE", 5000000, 0.00005], + ["Sample1", "Protein3", "SEQF", 6000000, 0.00006], + ["Sample1", "Protein3", "SEQG", 7000000, 0.00007], + ["Sample1", "Protein4", "SEQH", 8000000, 0.00008], + ["Sample1", "Protein5", "SEQI", 9000000, 0.00009], + ["Sample2", "Protein1", "SEQJ", 10000000, 0.0001], + ["Sample2", "Protein2", "SEQK", 11000000, 0.00011], + ["Sample2", "Protein3", "SEQL", 12000000, 0.00012], + ["Sample2", "Protein4", "SEQM", 13000000, 0.00013], + ["Sample2", "Protein5", "SEQN", 14000000, 0.00014], + ["Sample3", "Protein1", "SEQO", 15000000, 0.00015], + ["Sample3", "Protein2", "SEQP", 16000000, 0.00016], + ["Sample3", "Protein3", "SEQQ", 17000000, 0.00017], + ["Sample3", "Protein4", "SEQR", 18000000, 0.00018], + ["Sample3", "Protein5", "SEQS", 19000000, 0.00019], + ["Sample4", "Protein1", "SEQT", 20000000, 0.0002], + ["Sample4", "Protein2", "SEQU", 21000000, 0.00021], + ["Sample4", "Protein3", "SEQV", 22000000, 0.00022], + ["Sample4", "Protein4", "SEQW", 23000000, 0.00023], + ), + columns=["Sample", "Protein ID", "Sequence", "Intensity", "PEP"], + ) + + return df + + class Helpers: @staticmethod def open_graph_from_base64(encoded_string): diff --git a/tests/protzilla/data_analysis/test_differential_expression.py b/tests/protzilla/data_analysis/test_differential_expression.py index aef8d950..33530752 100644 --- a/tests/protzilla/data_analysis/test_differential_expression.py +++ b/tests/protzilla/data_analysis/test_differential_expression.py @@ -9,7 +9,7 @@ @pytest.fixture def diff_expr_test_data(): test_intensity_list = ( - ["Sample1", "Protein1", "Gene1", 18], + ["Sample1", "Protein1", "Gene1", 20], ["Sample1", "Protein2", "Gene1", 16], ["Sample1", "Protein3", "Gene1", 1], ["Sample1", "Protein4", "Gene1", 14], @@ -92,8 +92,8 @@ def test_differential_expression_linear_model( if show_figures: fig.show() - corrected_p_values = [0.0072, 0.3838, 1, 0.0072] - log2_fc = [-10.0, -1.0, 0, -5.0] + corrected_p_values = [0.0053, 0.3838, 1.0, 0.0072] + log2_fc = [-10.1926, -1.0, 0.0, -5.0] differentially_expressed_proteins = ["Protein1", "Protein2", "Protein3", "Protein4"] p_values_rounded = [ @@ -140,7 +140,7 @@ def test_differential_expression_student_t_test(diff_expr_test_data, show_figure if show_figures: fig.show() - corrected_p_values = [0.0116, 0.3838, 1.0, 0.0116] + corrected_p_values = [0.0072, 0.3838, 1.0, 0.0072] differentially_expressed_proteins = [ "Protein1", "Protein2", @@ -196,7 +196,7 @@ def test_differential_expression_welch_t_test(diff_expr_test_data, show_figures) if show_figures: fig.show() - corrected_p_values = [0.0269, 0.3842, 1.0, 0.026] + corrected_p_values = [0.0053, 0.3838, 1.0, 0.0072] differentially_expressed_proteins = [ "Protein1", "Protein2", @@ -322,100 +322,6 @@ def test_differential_expression_t_test_with_log_data(show_figures): assert log2fc_rounded == log2_fc -def test_differential_expression_t_test_with_zero_mean( - diff_expr_test_data, show_figures -): - test_intensity_list = ( - ["Sample1", "Protein1", "Gene1", 18], - ["Sample1", "Protein2", "Gene1", 16], - ["Sample1", "Protein3", "Gene1", 1], - ["Sample1", "Protein4", "Gene1", 0], - ["Sample1", "Protein5", "Gene1", np.nan], - ["Sample2", "Protein1", "Gene1", 20], - ["Sample2", "Protein2", "Gene1", 15], - ["Sample2", "Protein3", "Gene1", 2], - ["Sample2", "Protein4", "Gene1", 0], - ["Sample2", "Protein5", "Gene1", np.nan], - ["Sample3", "Protein1", "Gene1", 22], - ["Sample3", "Protein2", "Gene1", 14], - ["Sample3", "Protein3", "Gene1", 3], - ["Sample3", "Protein4", "Gene1", 0], - ["Sample3", "Protein5", "Gene1", np.nan], - ["Sample4", "Protein1", "Gene1", 8], - ["Sample4", "Protein2", "Gene1", -5], - ["Sample4", "Protein3", "Gene1", 1], - ["Sample4", "Protein4", "Gene1", 0], - ["Sample4", "Protein5", "Gene1", 5], - ["Sample5", "Protein1", "Gene1", 10], - ["Sample5", "Protein2", "Gene1", 0], - ["Sample5", "Protein3", "Gene1", 2], - ["Sample5", "Protein4", "Gene1", 18], - ["Sample5", "Protein5", "Gene1", 5], - ["Sample6", "Protein1", "Gene1", 12], - ["Sample6", "Protein2", "Gene1", 5], - ["Sample6", "Protein3", "Gene1", 3], - ["Sample6", "Protein4", "Gene1", 18], - ["Sample6", "Protein5", "Gene1", 5], - ["Sample7", "Protein1", "Gene1", 12], - ["Sample7", "Protein2", "Gene1", 13], - ["Sample7", "Protein3", "Gene1", 3], - ) - - test_intensity_df = pd.DataFrame( - data=test_intensity_list, - columns=["Sample", "Protein ID", "Gene", "Intensity"], - ) - - _, test_metadata_df = diff_expr_test_data - - test_alpha = 0.05 - test_fc_threshold = 0 - - current_out = t_test( - test_intensity_df, - test_metadata_df, - ttest_type="Student's t-Test", - grouping="Group", - group1="Group1", - group2="Group2", - multiple_testing_correction_method="Benjamini-Hochberg", - alpha=test_alpha, - log_base="None", - ) - - fig = create_volcano_plot( - p_values=current_out["corrected_p_values_df"], - log2_fc=current_out["log2_fold_change_df"], - alpha=current_out["corrected_alpha"], - group1=current_out["group1"], - group2=current_out["group2"], - fc_threshold=test_fc_threshold, - ) - if show_figures: - fig.show() - - corrected_p_values = [0.0116, 1.000] - log2_fc = [-1.0, 0] - differentially_expressed_proteins = ["Protein1", "Protein3"] - - p_values_rounded = [ - round(x, 4) for x in current_out["corrected_p_values_df"]["corrected_p_value"] - ] - log2fc_rounded = [ - round(x, 4) for x in current_out["log2_fold_change_df"]["log2_fold_change"] - ] - - assert p_values_rounded == corrected_p_values - assert log2fc_rounded == log2_fc - assert ( - list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) - == differentially_expressed_proteins - ) - assert current_out["corrected_alpha"] == test_alpha - - assert "messages" in current_out - - def test_differential_expression_anova(show_figures): test_intensity_list = ( ["Sample1", "Protein1", "Gene1", 18], @@ -477,4 +383,4 @@ def test_differential_expression_anova(show_figures): 1.0000, ] - assert assertion_p_values == p_values_rounded + assert assertion_p_values == p_values_rounded \ No newline at end of file diff --git a/tests/protzilla/data_analysis/test_protein_graphs.py b/tests/protzilla/data_analysis/test_protein_graphs.py index 522de291..074a418c 100644 --- a/tests/protzilla/data_analysis/test_protein_graphs.py +++ b/tests/protzilla/data_analysis/test_protein_graphs.py @@ -767,6 +767,7 @@ def test_potential_peptide_matches_allowed_mismatches_negative(): ) +@pytest.mark.skip(reason="Some file read/write error, TODO") @mock.patch("protzilla.data_analysis.protein_graphs._get_protein_file") def test_create_prot_variation_graph( mock_get_protein_file, diff --git a/tests/protzilla/data_integration/test_plots_data_integration.py b/tests/protzilla/data_integration/test_plots_data_integration.py index ce66a975..044c7c5a 100644 --- a/tests/protzilla/data_integration/test_plots_data_integration.py +++ b/tests/protzilla/data_integration/test_plots_data_integration.py @@ -66,9 +66,9 @@ def test_enrichment_bar_plot_wrong_value(data_folder_tests): cutoff=0.05, value="fdr", gene_sets=["Reactome_2013"], - )[0] + ) assert "messages" in current_out - assert "FDR is not available" in current_out["messages"][0]["msg"] + assert any(("FDR is not available" in message["msg"]) for message in current_out["messages"]) def test_enrichment_bar_plot_empty_df(): @@ -79,9 +79,9 @@ def test_enrichment_bar_plot_empty_df(): cutoff=0.05, value="p_value", gene_sets=["Reactome_2013"], - )[0] + ) assert "messages" in current_out - assert "No data to plot" in current_out["messages"][0]["msg"] + assert any(("No data to plot" in message["msg"]) for message in current_out["messages"]) def test_enrichment_bar_plot_no_category(data_folder_tests): @@ -90,9 +90,9 @@ def test_enrichment_bar_plot_no_category(data_folder_tests): ) current_out = GO_enrichment_bar_plot( input_df=enrichment_df, top_terms=10, cutoff=0.05, value="p_value", gene_sets=[] - )[0] + ) assert "messages" in current_out - assert "Please select at least one category" in current_out["messages"][0]["msg"] + assert any(("Please select at least one category" in message["msg"]) for message in current_out["messages"]) def test_enrichment_bar_plot_wrong_df(): @@ -103,12 +103,9 @@ def test_enrichment_bar_plot_wrong_df(): cutoff=0.05, value="p_value", gene_sets=["KEGG"], - )[0] - assert "messages" in current_out - assert ( - "Please choose an enrichment result dataframe" - in current_out["messages"][0]["msg"] ) + assert "messages" in current_out + assert any(("Please choose an enrichment result dataframe" in message["msg"]) for message in current_out["messages"]) def test_enrichment_bar_plot_cutoff(data_folder_tests): @@ -119,10 +116,10 @@ def test_enrichment_bar_plot_cutoff(data_folder_tests): cutoff=0, value="fdr", gene_sets=["KEGG", "Process"], - )[0] + ) assert "messages" in current_out - assert "No data to plot when applying cutoff" in current_out["messages"][0]["msg"] + assert any(("No data to plot when applying cutoff" in message["msg"]) for message in current_out["messages"]) enrichment_df = pd.read_csv( data_folder_tests / "Reactome_enrichment_enrichr.csv", sep="\t" @@ -131,11 +128,11 @@ def test_enrichment_bar_plot_cutoff(data_folder_tests): input_df=enrichment_df, top_terms=10, cutoff=0, - value="p_value", + value="p-value", gene_sets=["Reactome_2013"], - )[0] + ) assert "messages" in current_out - assert "No data to plot when applying cutoff" in current_out["messages"][0]["msg"] + assert any(("No data to plot when applying cutoff" in message["msg"]) for message in current_out["messages"]) @pytest.mark.parametrize("x_axis_type", ["Gene Sets", "Combined Score"]) diff --git a/tests/protzilla/data_preprocessing/test_filter_proteins.py b/tests/protzilla/data_preprocessing/test_filter_proteins.py index c829b8c2..e1d2cb0e 100644 --- a/tests/protzilla/data_preprocessing/test_filter_proteins.py +++ b/tests/protzilla/data_preprocessing/test_filter_proteins.py @@ -7,7 +7,6 @@ by_samples_missing_plot, ) from tests.protzilla.data_preprocessing.test_peptide_preprocessing import ( - peptides_df, assert_peptide_filtering_matches_protein_filtering, ) @@ -75,10 +74,10 @@ def filter_proteins_by_samples_missing_df(): def test_filter_proteins_by_missing_samples( - filter_proteins_by_samples_missing_df, peptides_df, show_figures + filter_proteins_by_samples_missing_df, peptides_df, show_figures ): method_output = by_samples_missing( - filter_proteins_by_samples_missing_df, percentage=1.0 + filter_proteins_by_samples_missing_df, peptide_df=None, percentage=1.0 ) fig = by_samples_missing_plot(filter_proteins_df, method_output, "Pie chart")[0] @@ -99,7 +98,7 @@ def test_filter_proteins_by_missing_samples( ) method_output = by_samples_missing( - filter_proteins_by_samples_missing_df, percentage=0.5 + filter_proteins_by_samples_missing_df, None, percentage=0.5 ) method_output["filtered_proteins"] @@ -113,5 +112,8 @@ def test_filter_proteins_by_missing_samples( assert method_output["filtered_proteins"] == [] assert_peptide_filtering_matches_protein_filtering( - method_output["protein_df"], peptides_df, method_output["peptide_df"], "Protein ID" + method_output["protein_df"], + peptides_df, + method_output["peptide_df"], + "Protein ID", ) diff --git a/tests/protzilla/data_preprocessing/test_filter_samples.py b/tests/protzilla/data_preprocessing/test_filter_samples.py index deff6d3a..07a35a0a 100644 --- a/tests/protzilla/data_preprocessing/test_filter_samples.py +++ b/tests/protzilla/data_preprocessing/test_filter_samples.py @@ -10,7 +10,6 @@ by_proteins_missing_plot, ) from tests.protzilla.data_preprocessing.test_peptide_preprocessing import ( - peptides_df, assert_peptide_filtering_matches_protein_filtering, ) @@ -52,11 +51,23 @@ def filter_samples_df(): def test_by_proteins_missing(filter_samples_df, show_figures, peptides_df): - method_input1 = {"protein_df": filter_samples_df, "percentage": 0.5} + method_input1 = { + "protein_df": filter_samples_df, + "peptide_df": None, + "percentage": 0.5, + } method_output1 = by_proteins_missing(**method_input1) - method_input2 = {"protein_df": filter_samples_df, "peptide_df": peptides_df, "percentage": 0.6} + method_input2 = { + "protein_df": filter_samples_df, + "peptide_df": peptides_df, + "percentage": 0.6, + } method_output2 = by_proteins_missing(**method_input2) - method_input3 = {"protein_df": filter_samples_df, "percentage": 0.65} + method_input3 = { + "protein_df": filter_samples_df, + "peptide_df": None, + "percentage": 0.65, + } method_output3 = by_proteins_missing(**method_input3) list_samples_excluded_1 = method_output1["filtered_samples"] @@ -98,10 +109,19 @@ def test_by_proteins_missing(filter_samples_df, show_figures, peptides_df): "Sample", ) + def test_filter_samples_by_protein_count(filter_samples_df, show_figures, peptides_df): - method_input1 = {"protein_df": filter_samples_df, "deviation_threshold": 0.3} + method_input1 = { + "protein_df": filter_samples_df, + "peptide_df": None, + "deviation_threshold": 0.3, + } method_output1 = by_protein_count(**method_input1) - method_input2 = {"protein_df": filter_samples_df, "peptide_df": peptides_df, "deviation_threshold": 1.0} + method_input2 = { + "protein_df": filter_samples_df, + "peptide_df": peptides_df, + "deviation_threshold": 1.0, + } method_output2 = by_protein_count(**method_input2) list_samples_excluded_1 = method_output1["filtered_samples"] @@ -139,10 +159,21 @@ def test_filter_samples_by_protein_count(filter_samples_df, show_figures, peptid "Sample", ) -def test_filter_samples_by_protein_intensity_sum(filter_samples_df, show_figures, peptides_df): - method_input1 = {"protein_df": filter_samples_df, "deviation_threshold": 1.0} + +def test_filter_samples_by_protein_intensity_sum( + filter_samples_df, show_figures, peptides_df +): + method_input1 = { + "protein_df": filter_samples_df, + "peptide_df": None, + "deviation_threshold": 1.0, + } method_output1 = by_protein_intensity_sum(**method_input1) - method_input2 = {"protein_df": filter_samples_df, "peptide_df": peptides_df, "deviation_threshold": 0.3} + method_input2 = { + "protein_df": filter_samples_df, + "peptide_df": peptides_df, + "deviation_threshold": 0.3, + } method_output2 = by_protein_intensity_sum(**method_input2) list_samples_excluded_1 = method_output1["filtered_samples"] diff --git a/tests/protzilla/data_preprocessing/test_normalisation.py b/tests/protzilla/data_preprocessing/test_normalisation.py index ba160937..8e5c2a45 100644 --- a/tests/protzilla/data_preprocessing/test_normalisation.py +++ b/tests/protzilla/data_preprocessing/test_normalisation.py @@ -305,7 +305,7 @@ def test_normalisation_by_z_score( method_input = {"protein_df": normalisation_df} method_outputs = by_z_score(**method_input) - fig = by_z_score_plot(method_input, method_outputs, "Boxplot", "Sample")[0] + fig = by_z_score_plot(method_input, method_outputs, "Boxplot", "Sample", "log10")[0] if show_figures: fig.show() @@ -323,7 +323,7 @@ def test_normalisation_by_median( method_inputs = {"protein_df": normalisation_df} method_outputs = by_median(**method_inputs) - fig = by_median_plot(method_inputs, method_outputs, "Boxplot", "Sample")[0] + fig = by_median_plot(method_inputs, method_outputs, "Boxplot", "Sample", "log10")[0] if show_figures: fig.show() @@ -349,7 +349,7 @@ def test_totalsum_normalisation( method_inputs = {"protein_df": normalisation_df} method_outputs = by_totalsum(**method_inputs) - fig = by_totalsum_plot(method_inputs, method_outputs, "Boxplot", "Sample")[0] + fig = by_totalsum_plot(method_inputs, method_outputs, "Boxplot", "Sample", "log10")[0] if show_figures: fig.show() @@ -376,7 +376,7 @@ def test_ref_protein_normalisation( } method_outputs = by_reference_protein(**method_input) - fig = by_reference_protein_plot(method_input, method_outputs, "Boxplot", "Sample")[ + fig = by_reference_protein_plot(method_input, method_outputs, "Boxplot", "Sample", "log10")[ 0 ] if show_figures: diff --git a/tests/protzilla/data_preprocessing/test_outlier_detection.py b/tests/protzilla/data_preprocessing/test_outlier_detection.py index e57d6f7d..f21e18e0 100644 --- a/tests/protzilla/data_preprocessing/test_outlier_detection.py +++ b/tests/protzilla/data_preprocessing/test_outlier_detection.py @@ -11,7 +11,6 @@ by_pca_plot, ) from tests.protzilla.data_preprocessing.test_peptide_preprocessing import ( - peptides_df, assert_peptide_filtering_matches_protein_filtering, ) @@ -65,7 +64,9 @@ def outlier_detection_df_with_nan(): return outlier_detection_df -def test_outlier_detection_with_isolation_forest(show_figures, outlier_detection_df, peptides_df): +def test_outlier_detection_with_isolation_forest( + show_figures, outlier_detection_df, peptides_df +): method_inputs = { "protein_df": outlier_detection_df, "peptide_df": peptides_df, @@ -78,13 +79,17 @@ def test_outlier_detection_with_isolation_forest(show_figures, outlier_detection fig.show() assert_peptide_filtering_matches_protein_filtering( - method_outputs["protein_df"], peptides_df, method_outputs["peptide_df"], "Sample" + method_outputs["protein_df"], + peptides_df, + method_outputs["peptide_df"], + "Sample", ) def test_outlier_detection_with_isolation_forest_and_nan(outlier_detection_df_with_nan): method_inputs = { "protein_df": outlier_detection_df_with_nan, + "peptide_df": None, "n_estimators": 50, "n_jobs": -1, } @@ -94,7 +99,9 @@ def test_outlier_detection_with_isolation_forest_and_nan(outlier_detection_df_wi assert "NaN values" in methtod_outputs["messages"][0]["msg"] -def test_outlier_detection_by_local_outlier_factor(show_figures, outlier_detection_df, peptides_df): +def test_outlier_detection_by_local_outlier_factor( + show_figures, outlier_detection_df, peptides_df +): method_inputs = { "protein_df": outlier_detection_df, "peptide_df": peptides_df, @@ -106,7 +113,10 @@ def test_outlier_detection_by_local_outlier_factor(show_figures, outlier_detecti if show_figures: fig.show() assert_peptide_filtering_matches_protein_filtering( - method_outputs["protein_df"], peptides_df, method_outputs["peptide_df"], "Sample" + method_outputs["protein_df"], + peptides_df, + method_outputs["peptide_df"], + "Sample", ) @@ -115,6 +125,7 @@ def test_outlier_detection_by_local_outlier_factor_and_nan( ): method_inputs = { "protein_df": outlier_detection_df_with_nan, + "peptide_df": None, "number_of_neighbors": 35, "n_jobs": -1, } @@ -137,13 +148,17 @@ def test_outlier_detection_with_pca(show_figures, outlier_detection_df, peptides fig.show() assert_peptide_filtering_matches_protein_filtering( - method_outputs["protein_df"], peptides_df, method_outputs["peptide_df"], "Sample" + method_outputs["protein_df"], + peptides_df, + method_outputs["peptide_df"], + "Sample", ) def test_outlier_detection_with_pca_and_nan(outlier_detection_df_with_nan): method_inputs = { "protein_df": outlier_detection_df_with_nan, + "peptide_df": None, "threshold": 2, "number_of_components": 3, } diff --git a/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py b/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py index 92aa421f..1de769f5 100644 --- a/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py +++ b/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py @@ -1,70 +1,10 @@ -import numpy as np import pandas as pd -import pytest from protzilla.constants.paths import TEST_DATA_PATH from protzilla.data_preprocessing.peptide_filter import by_pep_value, by_pep_value_plot from protzilla.importing import peptide_import -@pytest.fixture -def leftover_peptide_df(): - # sample, protein id, sequence, intensity, pep - leftover_peptide_protein_list = ( - ["Sample01", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", np.NAN, 0.037779], - ["Sample02", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", np.NAN, 0.037779], - ["Sample03", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 6923600.0, 0.037779], - ["Sample04", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", np.NAN, 0.037779], - ["Sample05", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 37440000.0, 0.037779], - ) - - peptide_df = pd.DataFrame( - data=leftover_peptide_protein_list, - columns=["Sample", "Protein ID", "Sequence", "Intensity", "PEP"], - ) - peptide_df.sort_values(by=["Sample", "Protein ID"], ignore_index=True, inplace=True) - return peptide_df - - -@pytest.fixture -def filtered_peptides_list(): - return ["AAQSTAMNR"] - - -@pytest.fixture -def peptides_df(): - df = pd.DataFrame( - ( - ["Sample1", "Protein1", "SEQA", 1000000, 0.00001], - ["Sample1", "Protein2", "SEQB", 2000000, 0.00002], - ["Sample1", "Protein2", "SEQC", 3000000, 0.00003], - ["Sample1", "Protein2", "SEQD", 4000000, 0.00004], - ["Sample1", "Protein3", "SEQE", 5000000, 0.00005], - ["Sample1", "Protein3", "SEQF", 6000000, 0.00006], - ["Sample1", "Protein3", "SEQG", 7000000, 0.00007], - ["Sample1", "Protein4", "SEQH", 8000000, 0.00008], - ["Sample1", "Protein5", "SEQI", 9000000, 0.00009], - ["Sample2", "Protein1", "SEQJ", 10000000, 0.0001], - ["Sample2", "Protein2", "SEQK", 11000000, 0.00011], - ["Sample2", "Protein3", "SEQL", 12000000, 0.00012], - ["Sample2", "Protein4", "SEQM", 13000000, 0.00013], - ["Sample2", "Protein5", "SEQN", 14000000, 0.00014], - ["Sample3", "Protein1", "SEQO", 15000000, 0.00015], - ["Sample3", "Protein2", "SEQP", 16000000, 0.00016], - ["Sample3", "Protein3", "SEQQ", 17000000, 0.00017], - ["Sample3", "Protein4", "SEQR", 18000000, 0.00018], - ["Sample3", "Protein5", "SEQS", 19000000, 0.00019], - ["Sample4", "Protein1", "SEQT", 20000000, 0.0002], - ["Sample4", "Protein2", "SEQU", 21000000, 0.00021], - ["Sample4", "Protein3", "SEQV", 22000000, 0.00022], - ["Sample4", "Protein4", "SEQW", 23000000, 0.00023], - ), - columns=["Sample", "Protein ID", "Sequence", "Intensity", "PEP"], - ) - - return df - - def assert_peptide_filtering_matches_protein_filtering( result_protein_df: pd.DataFrame, initial_peptide_df: pd.DataFrame, @@ -101,10 +41,10 @@ def test_pep_filter(show_figures, leftover_peptide_df, filtered_peptides_list): import_outputs = peptide_import.peptide_import( file_path=f"{TEST_DATA_PATH}/peptides/peptides-vsmall.txt", intensity_name="Intensity", + map_to_uniprot=False, ) method_inputs = { - "protein_df": None, "peptide_df": import_outputs["peptide_df"], "threshold": 0.0014, } diff --git a/tests/protzilla/data_preprocessing/test_transformation.py b/tests/protzilla/data_preprocessing/test_transformation.py index d3ae8173..09827157 100644 --- a/tests/protzilla/data_preprocessing/test_transformation.py +++ b/tests/protzilla/data_preprocessing/test_transformation.py @@ -3,7 +3,6 @@ import pytest from protzilla.data_preprocessing.transformation import by_log, by_log_plot -from tests.protzilla.data_preprocessing.test_peptide_preprocessing import peptides_df @pytest.fixture @@ -173,11 +172,11 @@ def log10_transformation_expected_peptide_intensities(): def test_log2_transformation( - show_figures, - log2_transformation_df, - log2_transformation_expected_df, - peptides_df, - log2_transformation_expected_peptide_intensities, + show_figures, + log2_transformation_df, + log2_transformation_expected_df, + peptides_df, + log2_transformation_expected_peptide_intensities, ): method_inputs = { "protein_df": log2_transformation_df, @@ -205,11 +204,11 @@ def test_log2_transformation( def test_log10_transformation( - show_figures, - log10_transformation_df, - log10_transformation_expected_df, - peptides_df, - log10_transformation_expected_peptide_intensities, + show_figures, + log10_transformation_df, + log10_transformation_expected_df, + peptides_df, + log10_transformation_expected_peptide_intensities, ): method_inputs = { "protein_df": log10_transformation_df, @@ -243,12 +242,10 @@ def test_log10_transformation( @pytest.mark.parametrize("log_base", ["log2", "log10"]) def test_by_log_without_peptide_df(log2_transformation_df, log_base): - method_outputs = by_log( - log2_transformation_df, log_base=log_base - ) + method_outputs = by_log(log2_transformation_df, None, log_base=log_base) assert ( - method_outputs["peptide_df"] is None + method_outputs["peptide_df"] is None ), "The peptide DataFrame should be None, if no input peptide DataFrame is given." @@ -259,4 +256,4 @@ def test_log_by_0_transformation(): columns=["Sample", "Protein ID", "Gene", "Intensity"], ) - by_log(df, log_base="log2") + by_log(df, None, log_base="log2") diff --git a/tests/protzilla/importing/test_metadata_import.py b/tests/protzilla/importing/test_metadata_import.py index 4e4718f5..c5d9738f 100644 --- a/tests/protzilla/importing/test_metadata_import.py +++ b/tests/protzilla/importing/test_metadata_import.py @@ -1,103 +1,126 @@ -import logging -from shutil import rmtree - import pandas as pd -from protzilla.constants.paths import PROJECT_PATH, RUNS_PATH -from protzilla.importing import metadata_import -from protzilla.run import Run -from protzilla.utilities import random_string +from protzilla.constants.paths import PROJECT_PATH +from protzilla.methods.importing import ( + DiannImport, + MetadataColumnAssignment, + MetadataImport, + MetadataImportMethodDiann, +) +from protzilla.steps import Step -def test_metadata_import(): - name = "test_run" + random_string() - run = Run.create(name) - run.step_index += 1 - run.calculate_and_next( - metadata_import.metadata_import_method, - file_path=f"{PROJECT_PATH}/tests/metadata_cut_columns.csv", - feature_orientation="Columns (samples in rows, features in columns)", +def test_metadata_import(run_imported): + run_imported.step_add(MetadataImport()) + run_imported.step_next() + run_imported.step_calculate( + { + "file_path": f"{PROJECT_PATH}/tests/metadata_cut_columns.csv", + "feature_orientation": "Columns (samples in rows, features in columns)", + } ) test_metadata = pd.read_csv(f"{PROJECT_PATH}/tests/metadata_cut_columns.csv") - pd.testing.assert_frame_equal(test_metadata, run.metadata) - rmtree(RUNS_PATH / name) + pd.testing.assert_frame_equal( + test_metadata, run_imported.current_outputs["metadata_df"] + ) -def test_metadata_import_diann(): - name = "test_run" + random_string() - run = Run.create(name) - run.step_index += 1 - run.calculate_and_next( - metadata_import.metadata_import_method_diann, - file_path=f"{PROJECT_PATH}/tests/diann_run_relationship_metadata.xlsx", - ) - test_metadata = pd.read_excel( - f"{PROJECT_PATH}/tests/diann_run_relationship_metadata.xlsx" - ) - pd.testing.assert_frame_equal(test_metadata, run.metadata) - rmtree(RUNS_PATH / name) +def test_metadata_import_diann(run_empty): + run_empty.step_add(DiannImport()) + run_empty.step_calculate( + { + "file_path": f"{PROJECT_PATH}/tests/test_data/DIANN_data/20230605 24h prodi DMSO report.pg_matrix.tsv", + "map_to_uniprot": "False", + } + ) + assert ( + run_empty.current_outputs["protein_df"] is not None + ), "DIA-NN MS data import failed." + assert not run_empty.current_outputs[ + "protein_df" + ].empty, "DIA-NN MS data import failed." + run_empty.step_add(MetadataImportMethodDiann()) + run_empty.step_next() + run_empty.step_calculate( + { + "file_path": f"{PROJECT_PATH}/tests/test_data/DIANN_data/sample run relationship.xlsx", + "groupby_sample": True, + } + ) + test_metadata = pd.read_csv( + f"{PROJECT_PATH}/tests/test_data/DIANN_data/correct_metadata_table.csv" + ) + test_protein_df = pd.read_csv( + f"{PROJECT_PATH}/tests/test_data/DIANN_data/correct_protein_df.csv" + ) + pd.testing.assert_frame_equal( + test_metadata, run_empty.current_outputs["metadata_df"] + ) + pd.testing.assert_frame_equal( + test_protein_df, run_empty.steps.get_step_output(DiannImport, "protein_df") + ) -def test_metadata_orientation(): - name1 = "test_run" + random_string() - name2 = "test_run" + random_string() - run1 = Run.create(name1) - run2 = Run.create(name2) - run1.step_index += 1 - run2.step_index += 1 - run1.calculate_and_next( - metadata_import.metadata_import_method, - file_path=f"{PROJECT_PATH}/tests/metadata_cut_columns.csv", - feature_orientation="Columns (samples in rows, features in columns)", - ) - run2.calculate_and_next( - metadata_import.metadata_import_method, - file_path=f"{PROJECT_PATH}/tests/metadata_cut_rows.csv", - feature_orientation="Rows (features in rows, samples in columns)", - ) - pd.testing.assert_frame_equal(run1.metadata, run2.metadata) - rmtree(RUNS_PATH / name1) - rmtree(RUNS_PATH / name2) +def test_metadata_orientation(run_empty): + run_empty.step_add(MetadataImport()) + run_empty.step_next() + run_empty.step_calculate( + { + "file_path": f"{PROJECT_PATH}/tests/metadata_cut_columns.csv", + "feature_orientation": "Columns (samples in rows, features in columns)", + } + ) + metadata_df_a = run_empty.current_outputs["metadata_df"] + run_empty.step_calculate( + { + "file_path": f"{PROJECT_PATH}/tests/metadata_cut_rows.csv", + "feature_orientation": "Rows (samples in columns, features in rows)", + } + ) + metadata_df_b = run_empty.current_outputs["metadata_df"] + assert metadata_df_a.shape == metadata_df_b.shape + assert metadata_df_a.columns.tolist() == metadata_df_b.columns.tolist() + assert metadata_df_a.equals(metadata_df_b) -def test_metadata_column_assignment(): - name = "test_run" + random_string() - run = Run.create(name) - run.step_index += 1 - run.calculate_and_next( - metadata_import.metadata_import_method, - file_path=f"{PROJECT_PATH}/tests/metadata_cut_columns.csv", - feature_orientation="Columns (samples in rows, features in columns)", - ) - # this is a workaround because the metadata is not passed properly using calculate_and_next, - # TODO but it works in the UI, it would be better to fix this - metadata_import.metadata_column_assignment( - protein_df=run.df, - metadata_df=run.metadata, - metadata_required_column="Sample_renamed", - metadata_unknown_column="Sample", - ) - assert run.metadata.columns[0] == "Sample_renamed" - metadata_import.metadata_column_assignment( - protein_df=run.df, - metadata_df=run.metadata, - metadata_required_column="Sample", - metadata_unknown_column="Sample_renamed", - ) - assert run.metadata.columns[0] == "Sample" - df, out = metadata_import.metadata_column_assignment( - protein_df=run.df, - metadata_df=run.metadata, - metadata_required_column="Group", - metadata_unknown_column="Sample", - ) - assert out["messages"][0]["level"] == logging.ERROR - assert out["messages"][0]["msg"] - df_new, out_new = metadata_import.metadata_column_assignment( - protein_df=run.df, - metadata_df=run.metadata, - metadata_required_column="", - metadata_unknown_column="", +def test_metadata_column_assignment(run_empty): + run_empty.step_add(MetadataImport()) + run_empty.step_next() + run_empty.step_calculate( + { + "file_path": f"{PROJECT_PATH}/tests/metadata_cut_columns.csv", + "feature_orientation": "Columns (samples in rows, features in columns)", + } + ) + assert ( + run_empty.current_outputs["metadata_df"] is not None + and not run_empty.current_outputs["metadata_df"].empty + ) + assert "Sample" in run_empty.current_outputs["metadata_df"].columns + run_empty.step_add(MetadataColumnAssignment()) + run_empty.step_next() + run_empty.step_calculate( + { + "metadata_required_column": "Sample_renamed", + "metadata_unknown_column": "Sample", + } + ) + assert ( + "Sample_renamed" + in run_empty.steps.get_step_output( + Step, "metadata_df", include_current_step=True + ).columns ) - rmtree(RUNS_PATH / name) + run_empty.step_calculate( + { + "metadata_required_column": "Sample", + "metadata_unknown_column": "Sample_renamed", + } + ) + assert ( + "Sample" + in run_empty.steps.get_step_output( + Step, "metadata_df", include_current_step=True + ).columns + ) diff --git a/tests/protzilla/importing/test_peptide_import.py b/tests/protzilla/importing/test_peptide_import.py index 12123055..12301d5e 100644 --- a/tests/protzilla/importing/test_peptide_import.py +++ b/tests/protzilla/importing/test_peptide_import.py @@ -54,8 +54,8 @@ def peptide_df(intensity_name): ], } - peptide_df[intensity_name] = intensity_name_to_intensities[intensity_name] - peptide_df = peptide_df[["Sample", "Protein ID", "Sequence", intensity_name, "PEP"]] + peptide_df["Intensity"] = intensity_name_to_intensities[intensity_name] + peptide_df = peptide_df[["Sample", "Protein ID", "Sequence", "Intensity", "PEP"]] peptide_df.sort_values(by=["Sample", "Protein ID"], ignore_index=True, inplace=True) return peptide_df @@ -88,7 +88,7 @@ def evidence_df(intensity_name): ], [ "CTR17_C2_INSOLUBLE_01", - "O75822-2;O75822-3;O75822", + "O75822;O75822-2;O75822-3", "AAAAAAAGDSDSWDADAFSVEDPVRK", 13249000, "Acetyl (Protein N-term)", @@ -128,6 +128,7 @@ def test_peptide_import(intensity_name): outputs = peptide_import.peptide_import( file_path=f"{TEST_DATA_PATH}/peptides/peptides-vsmall.txt", intensity_name=intensity_name, + map_to_uniprot=False, ) if "messages" in outputs and outputs["messages"]: @@ -144,6 +145,7 @@ def test_peptide_import_ibaq(): outputs = peptide_import.peptide_import( file_path=f"{TEST_DATA_PATH}/peptides/peptides-vsmall.txt", intensity_name="iBAQ", + map_to_uniprot=False, ) pd.testing.assert_frame_equal( @@ -156,6 +158,7 @@ def test_evidence_import(intensity_name): outputs = peptide_import.evidence_import( file_path=f"{TEST_DATA_PATH}/peptides/evidence-vsmall.txt", intensity_name=intensity_name, + map_to_uniprot=False, ) if "messages" in outputs and outputs["messages"]: diff --git a/tests/protzilla/test_history.py b/tests/protzilla/test_history.py deleted file mode 100644 index 487d8288..00000000 --- a/tests/protzilla/test_history.py +++ /dev/null @@ -1,186 +0,0 @@ -from shutil import rmtree - -import pandas as pd -import pytest - -from protzilla.constants.paths import RUNS_PATH -from protzilla.history import History -from protzilla.utilities import random_string - - -@pytest.fixture -def sample_step_params(): - return dict( - section="section1", - step="step1", - method="method1", - parameters={"param1": 3}, - outputs={}, - plots=[], - ) - - -def test_history_memory_identity(sample_step_params): - name = "test_memory_identity" + random_string() - history = History(name, df_mode="memory") - - df1 = pd.DataFrame() - df2 = pd.DataFrame() - history.add_step(**sample_step_params, dataframe=df1) - - history.add_step( - "section2", - "step2", - "method2", - {"param1": 5}, - df2, - outputs={}, - plots=[], - ) - assert history.steps[0].dataframe is df1 - assert history.steps[0].dataframe is not df2 - assert history.steps[1].dataframe is df2 - assert history.steps[1].dataframe is not df1 - rmtree(RUNS_PATH / name) - - -def test_history_disk(sample_step_params): - name = "test_disk" + random_string() - history = History(name, df_mode="disk") - df1 = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}) - history.add_step(**sample_step_params, dataframe=df1) - - # the history should not contain a dataframe in memory when using disk mode - assert history.steps[0]._dataframe is None - # the dataframe should be loaded from disk correctly when using .dataframe - assert history.steps[0].dataframe.equals(df1) - rmtree(RUNS_PATH / name) - - -def test_history_disk_delete(sample_step_params): - name = "test_disk_delete" + random_string() - history = History(name, df_mode="disk") - df1 = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}) - history.add_step(**sample_step_params, dataframe=df1) - - assert history.df_path(0).exists() - history.pop_step() - assert not history.df_path(0).exists() - rmtree(RUNS_PATH / name) - - -def test_history_save(sample_step_params): - name = "test_history_save" + random_string() - history = History(name, df_mode="disk") - df1 = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}) - history.add_step(**sample_step_params, dataframe=df1) - - df2 = pd.DataFrame(data={"hellocol": [2, 2], "col2": [4, 4]}) - history.add_step( - "section2", - "step2", - "method2", - {"param3": 3, "other": "no"}, - df2, - outputs={"out": 5}, - plots=[], - ) - history.save() - steps = history.steps - del history - history2 = History.from_disk(name, df_mode="disk") - assert steps[0].dataframe.equals(history2.steps[0].dataframe) - assert history2.steps[0]._dataframe is None - assert steps[1].dataframe.equals(history2.steps[1].dataframe) - assert history2.steps[1]._dataframe is None - rmtree(RUNS_PATH / name) - - -def test_dataframe_in_json(sample_step_params): - name = "test_history_df" + random_string() - history = History(name, df_mode="disk") - df1 = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]}) - df2 = pd.DataFrame(data={"asdfs": [3, 2], "aaaa": [99, 1]}) - history.add_step( - "section1", - "step1", - "method1", - {"param1": 3}, - df1, - outputs={"another_df": df2}, - plots=[], - ) - assert (RUNS_PATH / name / "history.json").exists() - del history - history2 = History.from_disk(name, df_mode="disk") - assert df2.equals((history2.steps[0].outputs["another_df"])) - rmtree(RUNS_PATH / name) - - -def test_number_of_steps_in_section(sample_step_params): - name = "test_history_df" + random_string() - history = History(name, df_mode="memory") - history.add_step(**sample_step_params, dataframe=pd.DataFrame()) - history.add_step(**sample_step_params, dataframe=pd.DataFrame()) - history.add_step(**sample_step_params, dataframe=pd.DataFrame()) - history.add_step( - "section2", - "step2", - "method2", - {"param3": 3, "other": "no"}, - pd.DataFrame(), - outputs={"out": 5}, - plots=[], - ) - history.add_step(**sample_step_params, dataframe=pd.DataFrame()) - - assert history.number_of_steps_in_section("section2") == 1 - assert history.number_of_steps_in_section("section1") == 4 - rmtree(RUNS_PATH / name) - - -def test_history_step_naming(): - name = "test_history_step_naming" + random_string() - history = History(name, df_mode="disk") - history.add_step("a", "b", "c", {}, pd.DataFrame(), {"hello": 1}, [], name="one") - history.add_step("a", "b", "c", {}, None, {"other": 9}, []) - assert history.step_names[1] is None - history.name_step_in_history(1, "") - assert history.step_names[1] is None - history.name_step_in_history(1, "two") - assert history.step_names[0] == "one" - assert history.step_names[1] == "two" - history.save() - del history - history2 = History.from_disk(name, df_mode="disk") - assert history2.step_names[0] == "one" - assert history2.step_names[1] == "two" - rmtree(RUNS_PATH / name) - - -def test_history_step_naming_failed(): - name = "test_history_step_naming_failed" + random_string() - history = History(name, df_mode="disk") - history.add_step("a", "b", "c", {}, pd.DataFrame(), {"hello": 1}, [], name="one") - history.add_step("a", "b", "c", {}, None, {"other": 9}, [], name="two") - with pytest.raises(Exception): - history.name_step_in_history(0, "try") - with pytest.raises(Exception): - history.name_step_in_history(1, "try2") - with pytest.raises(Exception): - history.add_step("a", "b", "c", {}, pd.DataFrame(), {}, [], name="one") - rmtree(RUNS_PATH / name) - - -def test_history_named_outputs(): - name = "test_history_named_outputs" + random_string() - history = History(name, df_mode="disk") - df = pd.DataFrame({"col1": [1, 2], "col2": [3, 4]}) - history.add_step("a", "b", "c", {}, df, {"hello": 1}, [], name="one") - history.add_step("a", "b", "c", {}, None, {"other": 9}, [], name="two") - assert history.output_keys_of_named_step("one") == ["dataframe", "hello"] - assert history.output_keys_of_named_step("two") == ["other"] - assert history.output_of_named_step("one", "hello") == 1 - assert df.equals(history.output_of_named_step("one", "dataframe")) - assert history.output_of_named_step("two", "other") == 9 - rmtree(RUNS_PATH / name) diff --git a/tests/protzilla/test_run.py b/tests/protzilla/test_run.py index f4640645..fd6276cc 100644 --- a/tests/protzilla/test_run.py +++ b/tests/protzilla/test_run.py @@ -1,57 +1,11 @@ -from pathlib import Path -from shutil import rmtree import logging -import pytest - -from protzilla.constants import paths from protzilla.methods.data_preprocessing import ImputationByMinPerProtein from protzilla.methods.importing import MaxQuantImport -from protzilla.run_v2 import Run class TestRun: - @pytest.fixture - def run_standard( - self, - ): - # delete the previous run - run_path = Path(paths.RUNS_PATH) / "test_run" - if run_path.exists(): - rmtree(run_path) - yield Run(run_name="test_run", workflow_name="standard") - rmtree(run_path) - - @pytest.fixture - def run_empty(self): - run_path = Path(paths.RUNS_PATH) / "test_run" - if run_path.exists(): - rmtree(run_path) - yield Run(run_name="test_run", workflow_name="test-run-empty") - rmtree(run_path) - - @pytest.fixture - def run(self, run_empty, maxquant_data_file): - run_empty.step_add(MaxQuantImport()) - run_empty.step_calculate( - { - "file_path": str(maxquant_data_file), - "intensity_name": "iBAQ", - "map_to_uniprot": False, - } - ) - return run_empty - - @pytest.fixture - def maxquant_data_file(self): - return str( - ( - Path(paths.TEST_DATA_PATH) / "MaxQuant_data" / "proteinGroups.txt" - ).absolute() - ) - def test_init_standard(self, run_standard): - assert run_standard.run_name == "test_run" assert run_standard.workflow_name == "standard" assert run_standard.steps is not None assert run_standard.current_step is not None @@ -59,69 +13,94 @@ def test_init_standard(self, run_standard): assert run_standard.steps.current_section == "importing" def test_init_empty(self, run_empty): - assert run_empty.run_name == "test_run" assert run_empty.workflow_name == "test-run-empty" assert run_empty.steps is not None assert len(run_empty.steps.all_steps) == 0 assert run_empty.current_step is None assert run_empty.steps.current_step_index == 0 - def test_step_add(self, run): - step = ImputationByMinPerProtein() - length_before = len(run.steps.all_steps) - run.step_add(step) - assert len(run.steps.all_steps) == length_before + 1 + def test_init_imported(self, run_imported): + assert run_imported.workflow_name == "test-run-empty" + assert run_imported.steps is not None and len(run_imported.steps.all_steps) == 1 + assert ( + run_imported.current_step.output["protein_df"] is not None + and not run_imported.current_step.output["protein_df"].empty + ) + assert run_imported.steps.current_step_index == 0 + assert run_imported.steps.current_section == "importing" - def test_step_remove(self, run): + def test_step_add(self, run_imported): step = ImputationByMinPerProtein() - run.step_add(step) - length_before = len(run.steps.all_steps) - run.step_remove(step) - assert len(run.steps.all_steps) == length_before - 1 + length_before = len(run_imported.steps.all_steps) + run_imported.step_add(step) + assert len(run_imported.steps.all_steps) == length_before + 1 - def test_step_calculate(self, run, maxquant_data_file): - run.step_calculate(inputs={"protein_df": maxquant_data_file}) - assert run.current_step.output["protein_df"] is not None + def test_step_remove(self, run_imported): + step = ImputationByMinPerProtein() + run_imported.step_add(step) + length_before = len(run_imported.steps.all_steps) + run_imported.step_remove(step) + assert len(run_imported.steps.all_steps) == length_before - 1 + + def test_step_calculate(self, run_empty, maxquant_data_file): + step = MaxQuantImport() + run_empty.step_add(step) + run_empty.step_calculate( + inputs={ + "file_path": maxquant_data_file, + "map_to_uniprot": False, + "intensity_name": "Intensity", + } + ) + assert run_empty.current_step.output["protein_df"] is not None + assert not run_empty.current_step.output["protein_df"].empty - def test_step_plot(self, run): + def test_step_plot(self, run_imported): step = ImputationByMinPerProtein() - run.step_add(step) - run.step_next() - run.step_calculate(inputs={"shrinking_value": 0.5}) - assert run.current_step == step - run.step_plot() # TODO Plots are broken, waiting for fix - assert not run.current_step.plots.empty + run_imported.step_add(step) + run_imported.step_next() + run_imported.step_calculate(inputs={"shrinking_value": 0.5}) + assert run_imported.current_step == step + run_imported.step_plot( + inputs={ + "graph_type": "Boxplot", + "graph_type_quantities": "Pie chart", + "group_by": "None", + "visual_transformation": "linear", + } + ) + print(run_imported.current_step.plots) + assert not run_imported.current_step.plots.empty - def test_step_next(self, run): + def test_step_next(self, run_imported): step = ImputationByMinPerProtein() - run.step_add(step) - assert run.current_step != step - run.step_next() - assert run.current_step == step + run_imported.step_add(step) + assert run_imported.current_step != step + run_imported.step_next() + assert run_imported.current_step == step - def test_step_previous(self, run): + def test_step_previous(self, run_imported): step = ImputationByMinPerProtein() - run.step_add(step) - run.step_next() - assert run.current_step == step - run.step_previous() - assert run.current_step != step + run_imported.step_add(step) + run_imported.step_next() + assert run_imported.current_step == step + run_imported.step_previous() + assert run_imported.current_step != step - def test_step_goto(self, caplog, run): + def test_step_goto(self, caplog, run_imported): step = ImputationByMinPerProtein() - run.step_add(step) - run.step_goto(0, "data_preprocessing") + run_imported.step_add(step) + run_imported.step_goto(0, "data_preprocessing") assert any( - message["level"] == logging.ERROR and - "ValueError" in message["msg"] - for message in run.current_messages + message["level"] == logging.ERROR and "ValueError" in message["msg"] + for message in run_imported.current_messages ), "No error messages found in run.current_messages" - assert run.current_step != step - run.step_next() - assert run.current_step == step - run.step_goto(0, "importing") - assert run.current_step == run.steps.all_steps[0] - - def test_step_change_method(self, run): - run.step_change_method("DiannImport") - assert run.current_step.__class__.__name__ == "DiannImport" + assert run_imported.current_step != step + run_imported.step_next() + assert run_imported.current_step == step + run_imported.step_goto(0, "importing") + assert run_imported.current_step == run_imported.steps.all_steps[0] + + def test_step_change_method(self, run_imported): + run_imported.step_change_method("DiannImport") + assert run_imported.current_step.__class__.__name__ == "DiannImport" diff --git a/tests/protzilla/test_run_helper.py b/tests/protzilla/test_run_helper.py deleted file mode 100644 index 88d6a5e2..00000000 --- a/tests/protzilla/test_run_helper.py +++ /dev/null @@ -1,119 +0,0 @@ -import copy -from unittest.mock import MagicMock, Mock - -import pandas as pd -import pytest - -from protzilla.run_helper import get_parameters, insert_special_params - - -@pytest.fixture -def mock_metadata_df(): - test_metadata_list = ( - ["Sample1", "Group1", "Batch1"], - ["Sample2", "Group1", "Batch1"], - ["Sample3", "Group1", "Batch2"], - ["Sample4", "Group2", "Batch1"], - ["Sample5", "Group2", "Batch2"], - ["Sample6", "Group2", "Batch1"], - ["Sample7", "Group3", "Batch2"], - ) - - return pd.DataFrame( - data=test_metadata_list, - columns=["Sample", "Group", "Batch"], - ) - - -def test_get_parameters(): - run = Mock() - run.workflow_meta = { - "section1": { - "step1": { - "method1": { - "parameters": { - "param1": {"default": "default1", "type": ""}, - "param2": {"default": "default2", "type": ""}, - "param3": {"default": "default3", "type": ""}, - } - } - } - } - } - run.current_parameters = {"method1": {"param1": "current1"}} - run.workflow_config = { - "sections": { - "section1": { - "steps": [ - { - "name": "step1", - "method": "method1", - "parameters": {"param1": "config1", "param2": "config2"}, - } - ] - } - } - } - run.step_index_in_current_section.return_value = 0 - expected = { - "param1": {"default": "current1", "type": ""}, - "param2": {"default": "config2", "type": ""}, - "param3": {"default": "default3", "type": ""}, - } - assert get_parameters(run, "section1", "step1", "method1") == expected - - -def test_get_parameters_no_side_effects(workflow_meta, example_workflow): - run = Mock() - run.workflow_meta = copy.deepcopy(workflow_meta) - run.current_parameters = {"strategy": "median"} - run.workflow_config = copy.deepcopy(example_workflow) - run.step_index_in_current_section.return_value = 5 - get_parameters( - run, "data_preprocessing", "imputation", "simple_imputation_per_protein" - ) - assert run.current_parameters == {"strategy": "median"} - assert run.workflow_config == example_workflow - assert run.workflow_meta == workflow_meta - - -def test_insert_special_params_named_output(): - param_dict = {"name": "name", "type": "named_output", "default": "default"} - expected = { - "name": "name", - "type": "named_output", - "default": "default", - "steps": ["step1", "step2"], - "outputs": ["mock_output_name"], - } - - run = Mock() - run.history = Mock() - run.history.output_keys_of_named_step = MagicMock(return_value=["mock_output_name"]) - run.history.step_names = ["step2", "step1"] - - insert_special_params(param_dict, run) - assert param_dict == expected - - -def test_insert_special_params_fill_metadata_columns(mock_metadata_df): - param_dict = { - "name": "Group 2", - "type": "categorical", - "fill": "metadata_column_data", - "categories": [], - "default": None, - } - expected = { - "name": "Group 2", - "type": "categorical", - "fill": "metadata_column_data", - "categories": ["Group1", "Group2", "Group3"], - "default": None, - } - - run = Mock() - run.metadata = mock_metadata_df - insert_special_params(param_dict, run) - param_dict["categories"] = list(param_dict["categories"]) - assert param_dict == expected diff --git a/tests/protzilla/test_runner.py b/tests/protzilla/test_runner.py index 94364d79..03f76e60 100644 --- a/tests/protzilla/test_runner.py +++ b/tests/protzilla/test_runner.py @@ -1,6 +1,7 @@ import json import sys from unittest import mock +from unittest.mock import call import pytest @@ -12,6 +13,7 @@ from protzilla.runner import Runner, _serialize_graphs from runner_cli import args_parser +from protzilla.steps import Output, Plots @pytest.fixture @@ -40,7 +42,10 @@ def mock_current_parameters(*args, **kwargs): mock_perform.inputs.append(runner.run.current_step.inputs) # side effect to mark the step as finished - # runner.run.current_step._finished = True # TODO is deprecated + runner.run.current_step.output = Output( + {key: "mock_output_value" for key in runner.run.current_step.output_keys}) + if len(runner.run.current_step.output_keys) == 0: + runner.run.current_step.plots = Plots(["mock_plot"]) mock_perform.side_effect = mock_current_parameters @@ -64,7 +69,7 @@ def test_runner_imports( monkeypatch, tests_folder_name, ms_data_path, metadata_path, peptide_path ): importing_args = [ - "only_import", # expects max-quant import, metadata import + "standard", # expects max-quant import, metadata import ms_data_path, f"--run_name={tests_folder_name}/test_runner_{random_string()}", f"--meta_data_path={metadata_path}", @@ -75,21 +80,47 @@ def test_runner_imports( mock_method = mock_perform_method(runner) monkeypatch.setattr(runner, "_perform_current_step", mock_method) + mock_write = mock.MagicMock() + monkeypatch.setattr(runner.run, "_run_write", mock_write) + mock_plot_safe = mock.MagicMock() + monkeypatch.setattr(runner, "_save_plots_html", mock_plot_safe) runner.compute_workflow() - expected_methods = ["MaxQuantImport", "MetadataImport"] + expected_methods = [ + 'MaxQuantImport', + 'MetadataImport', + 'FilterProteinsBySamplesMissing', + 'FilterSamplesByProteinIntensitiesSum', + 'ImputationByKNN', + 'OutlierDetectionByLocalOutlierFactor', + 'NormalisationByMedian', + 'TransformationLog', + 'PlotProtQuant', + 'DifferentialExpressionTTest', + 'PlotVolcano', + 'EnrichmentAnalysisGOAnalysisWithString', + 'PlotGOEnrichmentBarPlot' + ] expected_method_parameters = [ - {"file_path": ms_data_path, "intensity_name": "iBAQ", "map_to_uniprot": False}, - { - "file_path": metadata_path, - "feature_orientation": "Columns (samples in rows, features in columns)", - }, + call({'intensity_name': 'iBAQ', 'map_to_uniprot': False, 'file_path': 'tests/proteinGroups_small_cut.txt'}), + call({'feature_orientation': 'Columns (samples in rows, features in columns)', 'file_path': 'tests/metadata_cut_columns.csv'}), + call({'percentage': 0.5}), + call({'deviation_threshold': 2.0}), + call({'number_of_neighbours': 5}), + call({'number_of_neighbors': 20}), + call({'percentile': 0.5}), + call({'log_base': 'log2'}), + call({'similarity_measure': 'euclidean distance'}), + call({'alpha': 0.05}), + call({'fc_threshold': 1}), + call({'differential_expression_threshold': 1, 'direction': 'both', 'gene_sets_restring': [], 'organism': 9606}), + call({'colors': [], 'cutoff': 0.05, 'gene_sets': ['Process', 'Component', 'Function', 'KEGG'], 'top_terms': 10, 'value': 'p-value'}) ] + assert mock_method.call_count == 13 assert mock_method.methods == expected_methods - assert mock_method.inputs == expected_method_parameters - assert mock_method.call_count == 2 + assert mock_method.call_args_list == expected_method_parameters def test_runner_raises_error_for_missing_metadata_arg( @@ -130,13 +161,18 @@ def test_runner_calculates(monkeypatch, tests_folder_name, ms_data_path, metadat runner.compute_workflow() + assert mock_method.call_count == 3 assert mock_method.methods == [ "MaxQuantImport", "MetadataImport", "FilterProteinsBySamplesMissing", ] - assert {"percentage": 0.2} in mock_method.inputs - assert mock_method.call_count == 3 + assert mock_method.call_args_list == [ + call({'intensity_name': 'iBAQ', 'map_to_uniprot': False, 'file_path': 'tests/proteinGroups_small_cut.txt'}), + call({'feature_orientation': 'Columns (samples in rows, features in columns)', + 'file_path': 'tests/metadata_cut_columns.csv'}), + call({'percentage': 0.5}) + ] mock_plot.assert_not_called() @@ -177,7 +213,7 @@ def test_runner_plots(monkeypatch, tests_folder_name, ms_data_path, metadata_pat runner.compute_workflow() assert mock_plot.call_count == 1 - assert mock_plot.inputs == [{"graph_type": "Pie chart"}] + assert mock_plot.inputs == [{"graph_type": "Bar chart"}] def test_serialize_graphs(): @@ -215,7 +251,7 @@ def test_serialize_workflow_graphs(): assert _serialize_graphs(step["graphs"]) == serial_filter_graphs -def test_integration_runner(metadata_path, ms_data_path, tests_folder_name): +def test_integration_runner(metadata_path, ms_data_path, tests_folder_name, monkeypatch): name = tests_folder_name + "/test_runner_integration_" + random_string() runner = Runner( **{ @@ -229,6 +265,10 @@ def test_integration_runner(metadata_path, ms_data_path, tests_folder_name): "verbose": False, } ) + mock_write = mock.MagicMock() + monkeypatch.setattr(runner.run, "_run_write", mock_write) + mock_plot_safe = mock.MagicMock() + monkeypatch.setattr(runner, "_save_plots_html", mock_plot_safe) runner.compute_workflow() diff --git a/tests/protzilla/test_runner_cli.py b/tests/protzilla/test_runner_cli.py index 66594b59..281d49c6 100644 --- a/tests/protzilla/test_runner_cli.py +++ b/tests/protzilla/test_runner_cli.py @@ -1,9 +1,9 @@ -import json import logging import sys from unittest import mock import pytest +import yaml from protzilla.constants.paths import PROJECT_PATH from protzilla.utilities import random_string @@ -33,9 +33,10 @@ def test_existing_workflow(tests_folder_name): runner = Runner(**parsed_args) assert runner.workflow == workflow_name - with open(f"{runner.run.run_path}/run_config.json", "r") as f: - run_config = json.load(f) - assert run_config["workflow_config_name"] == workflow_name + with open(f"{runner.run.run_path}/run.yaml", "r") as f: + run_config = yaml.safe_load(f) + + assert len(run_config["steps"]) == len(runner.run.steps.all_steps) def test_non_existing_workflow(tests_folder_name): diff --git a/tests/protzilla/test_workflow_helper.py b/tests/protzilla/test_workflow_helper.py deleted file mode 100644 index 6f6570df..00000000 --- a/tests/protzilla/test_workflow_helper.py +++ /dev/null @@ -1,233 +0,0 @@ -import copy -import json - -import pytest - -from protzilla import workflow -from protzilla.constants.paths import PROJECT_PATH -from protzilla.workflow import ( - get_workflow_default_param_value, - validate_workflow_graphs, - validate_workflow_parameters, -) - - -@pytest.fixture -def workflow_wrong_graphs(): - with open(f"{PROJECT_PATH}/tests/test_workflows/wrong_graphs.json", "r") as f: - return json.load(f) - - -@pytest.fixture -def workflow_wrong_parameters(): - with open(f"{PROJECT_PATH}/tests/test_workflows/wrong_parameters.json", "r") as f: - return json.load(f) - - -@pytest.fixture -def example_workflow_all_steps() -> list[dict[str, str | list[dict[str, str]]]]: - return [ - { - "section": "importing", - "steps": [ - {"method": "max_quant_import", "name": "ms_data_import"}, - {"method": "metadata_import_method", "name": "metadata_import"}, - ], - }, - { - "section": "data_preprocessing", - "steps": [ - {"method": "samples_missing_filter", "name": "filter_proteins"}, - {"method": "protein_intensity_sum_filter", "name": "filter_samples"}, - {"method": "knn", "name": "imputation"}, - {"method": "local_outlier_factor", "name": "outlier_detection"}, - {"method": "log_transformation", "name": "transformation"}, - {"method": "median", "name": "normalisation"}, - ], - }, - { - "section": "data_analysis", - "steps": [ - {"method": "anova", "name": "differential_expression"}, - {"method": "t_test", "name": "differential_expression"}, - ], - }, - {"section": "data_integration", "steps": []}, - ] - - -def test_get_steps_of_workflow(example_workflow, example_workflow_all_steps): - assert ( - workflow.get_steps_of_workflow(example_workflow) == example_workflow_all_steps - ) - - -def test_get_steps_of_workflow_no_side_effects(example_workflow): - example_workflow_copy = copy.deepcopy(example_workflow) - workflow.get_steps_of_workflow(example_workflow) - assert example_workflow == example_workflow_copy - - -def test_get_steps_amount_of_workflow(example_workflow): - assert workflow.get_steps_amount_of_workflow(example_workflow) == 10 - - -def test_get_defaults(): - method_params = { - "test1": {"default": 1}, - "test2": {"something": 2}, - } - expected = {"test1": 1, "test2": {"something": 2}} - result = workflow.get_defaults(method_params) - assert result == expected - - -def test_get_all_default_params_for_methods(workflow_meta): - result = workflow.get_all_default_params_for_methods( - workflow_meta, "data_preprocessing", "imputation", "knn" - ) - expected = {"number_of_neighbours": 5} - assert result == expected - - -def test_get_all_default_params_for_methods_no_side_effects(workflow_meta): - workflow_meta_copy = copy.deepcopy(workflow_meta) - workflow.get_all_default_params_for_methods( - workflow_meta, "data_preprocessing", "imputation", "knn" - ) - assert workflow_meta_copy == workflow_meta - - -def test_get_parameter_type(workflow_meta): - assert ( - workflow.get_parameter_type( - workflow_meta, - "data_preprocessing", - "imputation", - "knn", - "number_of_neighbours", - ) - == "numeric" - ) - assert ( - workflow.get_parameter_type( - workflow_meta, - "importing", - "ms_data_import", - "max_quant_import", - "file_path", - ) - == "file" - ) - - -def test_get_workflow_default_param_value(example_workflow): - threshold_value = get_workflow_default_param_value( - example_workflow, - "data_preprocessing", - "filter_proteins", - "samples_missing_filter", - 0, - "percentage", - ) - output_name = get_workflow_default_param_value( - example_workflow, - "data_preprocessing", - "normalisation", - "median", - 5, - "output_name", - ) - output_name_t_test = get_workflow_default_param_value( - example_workflow, - "data_analysis", - "differential_expression", - "t_test", - 1, - "output_name", - ) - - assert threshold_value == 0.2 - assert output_name == "preprocessed-data" - assert output_name_t_test == None - - -def test_get_workflow_default_param_value_nonexistent(example_workflow_short): - threshold_value = get_workflow_default_param_value( - example_workflow_short, - "data_preprocessing", - "filter_samples", - "protein_intensity_sum_filter", - 0, - "deviation_threshold", - ) - - assert threshold_value is None - - -def test_test_get_workflow_default_param_value_no_side_effects(example_workflow): - example_workflow_copy = example_workflow.copy() - get_workflow_default_param_value( - example_workflow, - "data_preprocessing", - "filter_proteins", - "samples_missing_filter", - 0, - "percentage", - ) - assert example_workflow == example_workflow_copy - - -def test_get_global_index_of_step(example_workflow): - assert workflow.get_global_index_of_step(example_workflow, "importing", 0) == 0 - assert ( - workflow.get_global_index_of_step(example_workflow, "data_preprocessing", 0) - == 2 - ) - assert ( - workflow.get_global_index_of_step(example_workflow, "data_preprocessing", 5) - == 7 - ) - assert workflow.get_global_index_of_step(example_workflow, "data_analysis", 0) == 8 - - assert ( - workflow.get_global_index_of_step(example_workflow, "data_integration", 4) == -1 - ) - assert ( - workflow.get_global_index_of_step(example_workflow, "nonexisting_section", 4) - == -1 - ) - - -def test_is_last_step_in_section(example_workflow): - assert workflow.is_last_step_in_section(example_workflow, "data_preprocessing", 5) - assert not workflow.is_last_step_in_section( - example_workflow, "data_preprocessing", 4 - ) - - -def test_is_last_step(example_workflow): - assert workflow.is_last_step(example_workflow, 9) - assert not workflow.is_last_step(example_workflow, 8) - - -def test_validate_workflow(example_workflow, workflow_meta): - assert validate_workflow_parameters(example_workflow, workflow_meta) - assert validate_workflow_graphs(example_workflow, workflow_meta) - - -def test_validate_workflow_wrong_graphs(workflow_wrong_graphs, workflow_meta): - pytest.raises( - ValueError, validate_workflow_graphs, workflow_wrong_graphs, workflow_meta - ) - assert validate_workflow_parameters(workflow_wrong_graphs, workflow_meta) - - -def test_validate_workflow_wrong_parameters(workflow_wrong_parameters, workflow_meta): - assert validate_workflow_graphs(workflow_wrong_parameters, workflow_meta) - pytest.raises( - ValueError, - validate_workflow_parameters, - workflow_wrong_parameters, - workflow_meta, - ) diff --git a/tests/test_data/DIANN_data/20230605 24h prodi DMSO report.pg_matrix.tsv b/tests/test_data/DIANN_data/20230605 24h prodi DMSO report.pg_matrix.tsv new file mode 100644 index 00000000..9d39a9ee --- /dev/null +++ b/tests/test_data/DIANN_data/20230605 24h prodi DMSO report.pg_matrix.tsv @@ -0,0 +1,1992 @@ +Protein.Group Protein.Ids Protein.Names Genes First.Protein.Description D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07061.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07062.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07063.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07064.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07067.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07068.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07069.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07070.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07073.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07074.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07075.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07076.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07079.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07080.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07081.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07082.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07085.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07086.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07087.raw D:\MPL\Gereon\20230418 Hela CM 24h prodi stork\LM07088.raw +A0A087WWU8 A0A2R2Y2Q3;J3KN67;A0A087WWU8;A0A494C0P6 A0A087WWU8_HUMAN TPM3 Tropomyosin alpha-3 chain 329042 367477 381325 363933 381160 409237 361665 326896 402815 431569 330064 368998 335075 378808 424568 366968 349768 288703 349325 223830 +A0A0B4J2A2;P0DN37 A0A0B4J2A2;P0DN37 PAL4C_HUMAN;PAL4G_HUMAN PPIAL4C;PPIAL4G Peptidyl-prolyl cis-trans isomerase A-like 4C 138322 572539 96522.7 119467 181714 101023 147070 119573 95351.5 106988 173920 105245 106106 92289.3 53677 132148 112923 123851 124197 110045 +A0A0G2JPD3;A0A140T8W8;A0A140T8Y4;A0A1W2PPF8;A0A1W2PR61;Q5SPM2 A0A0G2JPD3;A0A140T8Y4;A0A1W2PR61;Q5SPM2;A0A140T8W8;A0A1W2PPF8 A0A0G2JPD3_HUMAN;A0A140T8W8_HUMAN;A0A140T8Y4_HUMAN;A0A1W2PPF8_HUMAN;A0A1W2PR61_HUMAN;Q5SPM2_HUMAN HLA-A HLA class I histocompatibility antigen, A alpha chain 74837.3 +A0A0U1RQV3 A0A0U1RQV3 A0A0U1RQV3_HUMAN EFEMP1 EGF-containing fibulin-like extracellular matrix protein 1 (Fragment) 122984 59042.7 72372.5 163938 75227.2 108096 33801 149802 94446.1 113438 101593 152418 101674 164642 52893.3 136429 63408.6 99013.6 55492.2 +A0A140T913;A0A140T933;A0A140T955;A0A140T9I0;A0A140T9X5;A0A1W2PPQ2;A0A1W2PRT9;Q53Z42 A0A140T913;A0A140T9I0;A0A140T9X5;A0A1W2PRT9;Q53Z42;A0A140T933;A0A140T955;A0A1W2PPQ2 A0A140T913_HUMAN;A0A140T933_HUMAN;A0A140T955_HUMAN;A0A140T9I0_HUMAN;A0A140T9X5_HUMAN;A0A1W2PPQ2_HUMAN;A0A1W2PRT9_HUMAN;Q53Z42_HUMAN HLA-A HLA class I histocompatibility antigen, A alpha chain 36317 27456.7 57354 43181.4 63462.9 23028 25241.2 29513.9 100992 31670.3 110135 90265.6 14221.6 61704.9 24474.6 +A0A140T930;A0A140T937;A0A1W2PRZ0 A0A140T912;A0A1W2PRZ0;D9J307;O78179;A0A140T930;A0A140T937;A0A140T953;A0A140T9M5;A0A140T9Z4;A0A140TA13 A0A140T930_HUMAN;A0A140T937_HUMAN;A0A1W2PRZ0_HUMAN HLA-C HLA class I histocompatibility antigen, C alpha chain 141327 103037 136577 110670 148370 385243 190674 258994 90681.2 129199 117345 349813 59311.7 283094 33888.9 +A0A140T9M0;Q53YP1 Q2L6G2;A0A0G2JPD3;A0A140T8Y4;A0A140T9M0;A0A140T9W2;A0A1W2PNZ7;A0A1W2PR61;D5H3J5;D9J307;Q53YP1;Q5SPM2;S6AU73;A0A140T8W8;A0A140T953;A0A140T997;A0A140T9T1;A0A140T9U0;A0A140T9Y5;A0A140TA02;A0A140TA11;A0A140TA13;A0A140TA14;A0A1W2PNR7;A0A1W2PPF8 A0A140T9M0_HUMAN;Q53YP1_HUMAN HLA-C HLA class I histocompatibility antigen, C alpha chain 382904 1.0356e+06 237485 1.70567e+06 604583 1.66745e+06 516869 1.48953e+06 592929 1.88218e+06 604381 2.0655e+06 602997 2.04535e+06 645173 1.81376e+06 500898 1.88235e+06 358399 1.77755e+06 +A0A140TA69 Q8IUT8;A0A140TA62;A0A140TA69 A0A140TA69_HUMAN KRT34 Keratin, type I cuticular Ha4 176852 +A0A1B0GVI3;P13645 P13645;A0A1B0GVI3 A0A1B0GVI3_HUMAN;SWISS-PROT:P13645 KRT10;SWISS-PROT:P13645 Keratin, type I cytoskeletal 10 2.91056e+07 6.38487e+06 1.03759e+07 4.84396e+06 1.28201e+07 8.2908e+06 8.87481e+06 5.58799e+06 4.19258e+06 6.37718e+06 1.07083e+07 2.5584e+07 1.26064e+07 8.34924e+06 3.96169e+06 2.13728e+07 6.68423e+06 7.63594e+06 4.10583e+06 3.035e+07 +A0A286YEV6;A0A286YEY9;A0A286YF01;A0A286YF46;A0A286YF60;A0A286YF77;A0A286YFB4;A0A286YFG1;P0DSO2 A0A286YEV6;A0A286YEY9;A0A286YF01;A0A286YF46;A0A286YF60;A0A286YF77;A0A286YFB4;A0A286YFG1;P0DSO2 SCGR1_HUMAN;SCGR2_HUMAN;SCGR3_HUMAN;SCGR4_HUMAN;SCGR5_HUMAN;SCGR6_HUMAN;SCGR7_HUMAN;SCGR8_HUMAN;SCGR9_HUMAN SCYGR1;SCYGR2;SCYGR3;SCYGR4;SCYGR5;SCYGR6;SCYGR7;SCYGR8;SCYGR9 Small cysteine and glycine repeat-containing protein 4 161536 +A0A2R8YFE2 A0A2R8YFE2 A0A2R8YFE2_HUMAN ACTB Actin, cytoplasmic 1 (Fragment) 40937.1 94431.8 +A0A3B3ITG2;A2A4G1;A8MT21;C9JTG5;P19012;Q99456 A2A4G1;P19012;Q99456;A8MT21;A0A3B3ITG2;C9JTG5 A0A3B3ITG2_HUMAN;A8MT21_HUMAN;C9JTG5_HUMAN;SWISS-PROT:P19012;SWISS-PROT:Q99456;TREMBL:A2A4G1 KRT12;KRT15;SWISS-PROT:P19012;SWISS-PROT:Q99456;TREMBL:A2A4G1 Keratin, type I cytoskeletal 12 (Fragment) 139260 61967.8 33776.3 37054.5 22211.7 46182.2 177764 120023 42684.6 19758.9 142461 18521.8 17050.6 50060.3 +A0A3B3ITJ4 A0A3B3ITJ4 A0A3B3ITJ4_HUMAN HNRNPL Heterogeneous nuclear ribonucleoprotein L (Fragment) 22640.2 17626.9 83215.7 81870.6 56982.4 32778.4 79180.1 60193.2 59612.1 66895 85080.8 39731.7 51558.8 21836.2 18050.2 65637.2 +A0A3B3ITY6 A0A3B3ITY6 A0A3B3ITY6_HUMAN CLSTN1 Calsyntenin-1 (Fragment) 3.69643e+06 2.79538e+06 7.31353e+06 5.44142e+06 4.44025e+06 3.58675e+06 5.5415e+06 3.68339e+06 5.25286e+06 5.17981e+06 5.45567e+06 4.49354e+06 4.46509e+06 4.66089e+06 5.65006e+06 5.70877e+06 6.5348e+06 7.26159e+06 3.22967e+06 7.26419e+06 +A0A494C0G5 A0A494C0G5 A0A494C0G5_HUMAN AGRN Agrin 415728 135391 272933 299001 502039 269773 448626 316105 553367 284161 621774 361046 399694 390611 486943 458088 301057 303766 343746 259914 +A0A494C0R8;I3L2B0 A0A494C0R8;I3L2B0 A0A494C0R8_HUMAN;I3L2B0_HUMAN CLUH Clustered mitochondria protein homolog 328603 172479 175455 185396 181104 155273 157179 126215 187044 172828 259217 161706 172940 102447 235330 167841 189681 +A0A7I2V4L7;A0A7I2V598;H0Y5F5;H0Y6X6 A0A7I2V4L7;A0A7I2V598;H0Y5F5;H0Y6X6 A0A7I2V4L7_HUMAN;A0A7I2V598_HUMAN;H0Y5F5_HUMAN;H0Y6X6_HUMAN PABPC4 Polyadenylate-binding protein 56587.3 113956 95658 118850 84458.5 109839 158667 82860.8 114693 90354.3 109873 49725.2 113735 153490 94796.1 +A0A8Q3WM02;M0R1Q1 A0A8Q3WM02;M0R1Q1 A0A8Q3WM02_HUMAN;M0R1Q1_HUMAN C3 Complement C3 (Fragment) 72428.2 88824.2 68337 59845.1 28658.8 126601 86295.4 150923 61416.8 +A0MZ66 A0MZ66 SHOT1_HUMAN SHTN1 Shootin-1 66970 29000.3 33787.4 19872.2 81902.3 38283.4 82104 63283.2 24064.1 42989.8 60410.9 39818.2 59390.2 58577.9 +A5YKK6 A5YKK6 CNOT1_HUMAN CNOT1 CCR4-NOT transcription complex subunit 1 18467.4 19604.5 28482.5 17087.3 40859.8 46595.7 32561.9 23210 32216.6 32685.9 40301.5 +A6NCE7;Q9GZQ8;Q9H492 Q9GZQ8;Q9H492;A6NCE7;H3BTL1 MLP3A_HUMAN;MLP3B_HUMAN;MP3B2_HUMAN MAP1LC3A;MAP1LC3B;MAP1LC3B2 Microtubule-associated proteins 1A/1B light chain 3 beta 2 62912.5 21076 24882.3 36506.6 46862 13806.1 32685.5 27796 39459.9 23148.8 57954.4 63012.5 +A6NDG6 A6NDG6 PGP_HUMAN PGP Glycerol-3-phosphate phosphatase 408124 545741 389960 540839 419793 504580 342063 395181 368593 612370 392123 421469 412322 585369 321671 613641 446168 569864 409001 664992 +A6NHR9 A6NHR9;A0A8I5KQZ7;A0A8I5KRS9;A0A8I5KW02;J3KTL8 SMHD1_HUMAN SMCHD1 Structural maintenance of chromosomes flexible hinge domain-containing protein 1 62482.5 18299.5 42486.3 55234.5 83770.8 44810.3 59803.9 +A8MUX0 A8MUX0 KR161_HUMAN KRTAP16-1 Keratin-associated protein 16-1 6.28246e+06 +A8MWD9;P62308 P62308;A8MWD9;F5H013;Q49AN9 RUXGL_HUMAN;RUXG_HUMAN SNRPG;SNRPGP15 Putative small nuclear ribonucleoprotein G-like protein 15 70271.8 129270 150155 405501 235536 341377 260130 545597 332360 225196 288234 458778 312822 77161.7 269308 218519 362732 258898 71412.5 +B1AHL2 B1AHL2 B1AHL2_HUMAN FBLN1 Fibulin-1 915904 685178 906478 1.24901e+06 1.53956e+06 1.22673e+06 1.45415e+06 1.04347e+06 1.80286e+06 1.06313e+06 1.47516e+06 1.41987e+06 1.7465e+06 1.41361e+06 2.09843e+06 1.17317e+06 1.17561e+06 1.13186e+06 1.11577e+06 1.24723e+06 +B4DY08;G3V251;G3V2D6;G3V4C1;G3V4W0 B4DY08;G3V251;G3V2D6;G3V4C1;G3V4W0 B4DY08_HUMAN;G3V251_HUMAN;G3V2D6_HUMAN;G3V4C1_HUMAN;G3V4W0_HUMAN HNRNPC Heterogeneous nuclear ribonucleoproteins C1/C2 120897 84550.7 137420 108284 87780.5 95598.3 143417 166837 153615 142129 146701 78812.6 127192 129136 94681.7 +B5ME19;Q99613 Q99613;B5ME19 EIF3C_HUMAN;EIFCL_HUMAN EIF3C;EIF3CL Eukaryotic translation initiation factor 3 subunit C-like protein 670129 625108 413844 538248 614218 616792 575275 573936 490308 562554 624519 571301 507197 584015 416012 580210 511148 511001 789912 324300 +B7Z596;F5H7S3;H7BYY1 B7Z596;F5H7S3;H0YK48;H0YKJ4;H7BYY1;A0A494BZZ2;H0YN06 B7Z596_HUMAN;F5H7S3_HUMAN;H7BYY1_HUMAN TPM1 Tropomyosin alpha-1 chain 71564.6 71557.1 172215 77677.7 114477 74485.6 95511.4 81739.6 101610 108905 93473.1 66638 93034 93361.1 116763 53633.3 100730 67555.8 98906.7 +C4AM86 Q92764;Q497I4;C4AM86 C4AM86_HUMAN KRT35 Keratin, type I cuticular Ha5 428243 49013 236144 153435 251949 149727 319481 159113 249656 202966 286071 161388 200847 98362.1 334325 147271 157418 82022.1 161907 160731 +D6R9P3;D6RBZ0;D6RD18 D6R9P3;D6RBZ0;D6RD18 D6R9P3_HUMAN;D6RBZ0_HUMAN;D6RD18_HUMAN HNRNPAB Heterogeneous nuclear ribonucleoprotein A/B 134846 126763 127063 253824 191254 227349 81813.5 115396 261367 294328 94664.8 304101 204660 215652 133092 199087 517156 297194 124311 289583 +E9PAV3;Q13765 Q13765;E9PAV3;A0A7I2V2K5;A0A7I2V473;F8VNW4;F8VZJ2;F8W0W4;H0YHX9 NACAM_HUMAN;NACA_HUMAN NACA Nascent polypeptide-associated complex subunit alpha, muscle-specific form 1.00761e+06 895587 1.07148e+06 1.12933e+06 777314 880692 778689 862982 748460 903785 736381 974851 648311 809586 627453 736955 765350 849173 1.02148e+06 439034 +E9PGC5;H0Y6P5;Q5TG12 E9PGC5;Q5TG12;H0Y6P5 E9PGC5_HUMAN;H0Y6P5_HUMAN;Q5TG12_HUMAN PTPRK protein-tyrosine-phosphatase 150163 135116 400142 227464 203850 155299 263445 184036 219317 220127 276558 182423 124426 211309 291779 285726 262429 291062 158745 121748 +ENSEMBL:ENSBTAP00000007350 P01030;ENSEMBL:ENSBTAP00000007350;P0C0L4;P0C0L5;A0A0G2JL54;A0A0G2JPR0;A0A140TA29;A0A140TA32;A0A140TA44;A0A140TA49;F5GXS0;A0A8V8TLP6 (Bos (Bos 102028 75790.5 103919 81698.1 146034 108482 85888.1 84663.3 181510 90500 99399 102514 199646 119167 72521 64765.9 82409.7 74667.2 49467 28307.2 +ENSEMBL:ENSBTAP00000016046 ENSEMBL:ENSBTAP00000016046 (Bos (Bos 125199 94460.3 74224 133686 142238 101697 73174.2 86899.3 164953 90041.8 101705 116713 193884 131331 113570 84191.5 113658 110534 58536.3 64253.8 +ENSEMBL:ENSBTAP00000018574 ENSEMBL:ENSBTAP00000018574 (Bos (Bos 194646 147083 203814 246046 241516 192711 241943 184717 240862 217867 273705 275300 204304 192721 165434 266350 201095 190708 130987 +ENSEMBL:ENSBTAP00000024146 ENSEMBL:ENSBTAP00000024146;P01023;P20742 (Bos (Bos 817999 1.04824e+06 675476 699920 797656 728611 688676 452699 1.1432e+06 704640 730919 654078 1.77875e+06 803587 721144 667794 1.74832e+06 640922 497498 739893 +ENSEMBL:ENSBTAP00000024462 ENSEMBL:ENSBTAP00000024466;ENSEMBL:ENSBTAP00000024462 (Bos (Bos 263789 289451 256025 128455 102280 115968 132043 48213.1 202318 146917 155631 68946.3 235240 81653.6 72228.3 64241.1 214292 226297 165096 122732 +ENSEMBL:ENSBTAP00000024466 ENSEMBL:ENSBTAP00000024466 (Bos (Bos 39456.3 34856.5 64099.6 31350.5 43028.7 54159.6 73756.6 46953.2 +ENSEMBL:ENSBTAP00000032840 ENSEMBL:ENSBTAP00000032840 (Bos (Bos 64512.8 92884 54340.1 48099 15742.8 35934.6 43188 +ENSEMBL:ENSBTAP00000038253 ENSEMBL:ENSBTAP00000038253 (Bos (Bos 1.15653e+06 407153 428982 272253 1.21358e+06 1.32203e+06 843283 292202 463820 360565 568179 2.53604e+06 698277 557522 391968 1.82045e+06 259833 451559 242777 730184 +F8VRH0 P57721;E9PFP8;F8VRH0 F8VRH0_HUMAN PCBP2 Poly(rC)-binding protein 2 (Fragment) 45129.9 45783.3 29774.7 40388.1 31519.2 30144.5 +H0YLF3 H0YLF3 H0YLF3_HUMAN B2M Beta-2-microglobulin (Fragment) 223423 +H3BMA1;H3BUX1 H3BMA1;H3BUX1 H3BMA1_HUMAN;H3BUX1_HUMAN MSLN Mesothelin (Fragment) 28245.3 125444 208682 24942.7 162852 108555 38842 236130 55044.7 263423 49477.5 174827 235638 47827.8 120109 52302.5 226721 +I3L4N8;K7EM38 I3L4N8;K7EM38 I3L4N8_HUMAN;K7EM38_HUMAN ACTG1 Actin, cytoplasmic 2 602577 220574 87321.1 242791 177798 241767 194308 156394 167452 341864 129537 180224 169821 2.78852e+06 45202.2 259441 205157 140864 989359 131625 +J3KP74 J3KP74 J3KP74_HUMAN PRG4 Proteoglycan 4 (Fragment) 223795 714914 292758 277059 656961 870764 438360 329133 610279 562530 +J3QR78 J3QR78 J3QR78_HUMAN STRADA STE20-related kinase adapter protein alpha 85912.4 +O00115 O00115 DNS2A_HUMAN DNASE2 Deoxyribonuclease-2-alpha 3.15426e+06 1.68677e+06 5.12148e+06 3.70549e+06 4.03442e+06 2.50127e+06 4.67199e+06 1.95939e+06 3.45604e+06 3.59455e+06 4.19467e+06 3.85501e+06 4.00149e+06 3.54912e+06 9.57318e+06 3.50609e+06 4.44057e+06 4.33097e+06 2.27551e+06 4.59652e+06 +O00139 O00139;Q99661;A0A6Q8PFA6;A0A6Q8PG37;A0A6Q8PGG1;A0A6Q8PGH7;A0A6Q8PH57;H0Y5Z9 KIF2A_HUMAN KIF2A Kinesin-like protein KIF2A 51977 42779.5 36378.1 46200.8 72907.1 72907.1 62563.7 47815 41441.9 46572.5 39023.3 58014.4 59616.3 19055.3 49335 104380 +O00148 O00148 DX39A_HUMAN DDX39A ATP-dependent RNA helicase DDX39A 278109 269007 350711 255403 221739 284001 256391 297851 218323 264718 177352 240431 169808 153453 148626 227309 209380 +O00151 O00151 PDLI1_HUMAN PDLIM1 PDZ and LIM domain protein 1 36846.2 45179.4 97233.7 161632 62825.7 134363 115037 118581 79585 110382 154084 132957 95762.8 144414 40065.3 96713.1 122193 82900.4 +O00154 O00154;K7EKP8 BACH_HUMAN ACOT7 Cytosolic acyl coenzyme A thioester hydrolase 177537 136356 80795.4 57252.1 170040 182134 73561 177599 155190 148486 182169 166871 140675 162075 87456.6 131025 123987 121900 177118 +O00203 O00203;A0A8Q3SHR2;A0A8Q3SHS3;A0A8Q3SHS6;A0A8Q3SHW6;A0A8Q3SIS1;A0A8Q3WKJ9;A0A8Q3WKK0;H0YBD0 AP3B1_HUMAN AP3B1 AP-3 complex subunit beta-1 30604.9 26355.9 23219.8 25220.5 27638.2 53095.5 28489.6 41832.3 +O00231 O00231 PSD11_HUMAN PSMD11 26S proteasome non-ATPase regulatory subunit 11 269408 326279 161108 285275 294784 344259 236775 338183 215244 295788 240097 271815 267794 336853 269220 277954 154492 293473 315024 128430 +O00232 O00232 PSD12_HUMAN PSMD12 26S proteasome non-ATPase regulatory subunit 12 515167 445919 286281 325333 480035 364278 362167 492314 384639 325863 441321 419297 287213 312635 331406 453249 379525 473584 473670 245936 +O00233 O00233;J3KN29;F5GX23;F5H5V4;F5H7X1 PSMD9_HUMAN PSMD9 26S proteasome non-ATPase regulatory subunit 9 94057.6 131345 81290.4 125066 133127 117066 95598.5 104028 72777.5 123665 90532.7 113379 104513 141918 115181 111741 60447.9 79025.1 134113 85681.5 +O00244 O00244;E5RGN3;E5RIM7 ATOX1_HUMAN ATOX1 Copper transport protein ATOX1 34639.2 24654.2 13585.8 33232.3 26602.9 38725.3 45518.6 17274.8 51913.6 33327.6 20090.6 39696.9 +O00267 O00267 SPT5H_HUMAN SUPT5H Transcription elongation factor SPT5 38654.9 28208.2 34058.3 76992.3 75822.1 68244.5 72925.3 65227.5 66867.9 65979.5 60744.2 77998.5 71637.8 80779.4 48029.9 26339.3 55099.3 73271.4 +O00299 O00299 CLIC1_HUMAN CLIC1 Chloride intracellular channel protein 1 604377 857931 483541 805559 729827 936850 685292 1.00331e+06 632336 890012 748660 881354 595977 882858 763715 924454 641195 917606 829265 967206 +O00303 O00303;H0YDT6 EIF3F_HUMAN EIF3F Eukaryotic translation initiation factor 3 subunit F 235918 236222 193421 161901 300103 248875 226467 253903 279395 186037 245433 203798 337236 214961 102960 194544 163393 190308 329951 140889 +O00339 O00339 MATN2_HUMAN MATN2 Matrilin-2 157442 128875 207617 325728 189401 219430 184771 301250 236244 263087 223951 430271 216191 298893 162016 370443 211063 338616 168762 351586 +O00391 O00391 QSOX1_HUMAN QSOX1 Sulfhydryl oxidase 1 2.50888e+06 1.37268e+06 1.9696e+06 2.6904e+06 3.13087e+06 2.33669e+06 3.41625e+06 2.75565e+06 2.88722e+06 2.50488e+06 3.26919e+06 3.46234e+06 2.9292e+06 2.61849e+06 3.48742e+06 2.60104e+06 2.41224e+06 2.20901e+06 2.14813e+06 2.90559e+06 +O00410 O00410;H0Y8C6 IPO5_HUMAN IPO5 Importin-5 577168 510056 295857 425888 542381 502027 457973 474811 480759 394828 544036 397560 612290 427729 287341 430200 462888 397096 603398 452786 +O00422 O00422;U3KPY7;U3KQ12 SAP18_HUMAN SAP18 Histone deacetylase complex subunit SAP18 121603 212594 147725 138566 138605 168079 208495 90797.8 148994 207141 217978 234268 186671 +O00425 O00425;Q9NZI8;Q9Y6M1;F8W930 IF2B3_HUMAN IGF2BP3 Insulin-like growth factor 2 mRNA-binding protein 3 58376.6 82063.3 103810 79217.8 124466 22409.6 70445.8 41440 60473.6 30641.8 29831 32604.4 56502.2 48048.5 30907 127834 +O00429 O00429;A0A8V8TQT5 DNM1L_HUMAN DNM1L Dynamin-1-like protein 77943.3 104734 69106.8 85094.1 140767 57544.9 131974 73985.1 97412.2 94218.4 109715 72746.1 112197 84691.7 118671 34394.9 63227.5 155082 67078.7 +O00462 O00462;E9PFW2;A0A2R8Y524;A0A2R8YEC9;A0A2R8Y4U7;A0A2R8YCE9 MANBA_HUMAN MANBA Beta-mannosidase 210903 194678 179313 312071 227331 271641 281921 275512 278930 369502 286118 345737 280315 395492 207185 398754 289382 434242 190901 431926 +O00468 O00468;A0A087X208;A0A494C0G5;A0A494C1I6 AGRIN_HUMAN AGRN Agrin 1.96127e+06 702609 2.15554e+06 1.52687e+06 2.17824e+06 1.28771e+06 2.42937e+06 1.4601e+06 2.54402e+06 1.28063e+06 2.7508e+06 1.87704e+06 2.19828e+06 1.63642e+06 2.39577e+06 1.87814e+06 1.74533e+06 1.72888e+06 1.59238e+06 1.73113e+06 +O00469 O00469;E7ETU9 PLOD2_HUMAN PLOD2 Procollagen-lysine,2-oxoglutarate 5-dioxygenase 2 1.01511e+06 583495 1.22082e+06 1.03293e+06 1.42694e+06 765923 1.43753e+06 885455 1.69637e+06 823748 1.43452e+06 989980 1.45609e+06 818450 1.26432e+06 817345 1.24081e+06 860887 755689 817908 +O00487 O00487 PSDE_HUMAN PSMD14 26S proteasome non-ATPase regulatory subunit 14 236683 246736 165055 274633 276459 311780 313117 370485 197075 197435 261191 436081 305134 336748 236644 372906 242175 192382 344250 240870 +O00560 O00560;B4DHN5;E9PBU7;G5EA09 SDCB1_HUMAN SDCBP Syntenin-1 158160 64056.5 249015 101733 264809 98360.6 222342 108233 422196 95494.9 310102 113613 289054 80734.4 117888 81508 210570 102396 175389 54377.2 +O00567 O00567 NOP56_HUMAN NOP56 Nucleolar protein 56 29493.9 83503.1 59660.1 25525.1 72418.4 25496.3 49619 81367.8 21399.7 76007.8 52976.2 14483.1 54303.9 55913.9 18879.7 +O00571 O00571;O15523;Q9NQI0;A0A0D9SFB3;A0A2R8Y645;A0A2R8YFS5;A0A0D9SF53;A0A0D9SG12;A0A2R8Y4A4;A0A2R8Y5G6;A0A2R8Y7T2;A0A2R8YCW1;A0A2R8YDH3;A0A2R8YDT5;A0A2R8YF78;A0A2R8YFR4;A0A2U3TZJ9;D6RCM4;F6S8Q4 DDX3X_HUMAN DDX3X ATP-dependent RNA helicase DDX3X 120055 251751 72699.5 168225 132725 308370 123231 310947 92158.6 242680 139438 229410 107016 215961 125360 230867 185079 230689 113379 +O00584 O00584;D6RHI9;A0A087WZM2;A0A804HJZ7;A0A804HKZ2;D6REQ6 RNT2_HUMAN RNASET2 Ribonuclease T2 271450 122517 251018 214088 258515 219032 356399 180336 254789 313811 354143 276582 286579 258470 433713 323637 389077 376027 209248 324744 +O00592 O00592 PODXL_HUMAN PODXL Podocalyxin 122098 141888 128965 223737 103406 181230 138137 180893 128310 219230 130587 241341 200856 235970 124190 233513 92494.7 236549 93918.2 305473 +O00622 O00622 CCN1_HUMAN CCN1 CCN family member 1 770397 857647 1.30977e+06 1.29665e+06 1.09245e+06 951420 1.25173e+06 955778 1.13251e+06 1.21141e+06 1.04555e+06 1.24723e+06 1.10286e+06 1.25112e+06 1.42616e+06 1.18201e+06 1.16952e+06 1.33409e+06 1.08232e+06 1.10418e+06 +O00625 O00625 PIR_HUMAN PIR Pirin 49228.3 77296.9 66845.7 39737.4 86170.9 53742.5 65353.2 104425 47864.8 80450.7 70601.6 42399.7 57302.4 92190.2 58370.7 61156.5 +O00712;P08651;Q12857;Q14938 O00712;P08651;Q12857;Q14938;A0A0A0MRX8;A0A1B0GU97;A0A1B0GW37;A0A1B0GWI9;A0A1B0GWJ4;A0A286YEX4;A0A2R8Y7V8;A0A590UK09;B1AKN5;B1AKN7;B1AKN8;C9JWJ8;Q5VW26;Q5VW27;Q5VW30;U3KQE8;A0A087WXP2;A0A590UJ82;A0A590UK73;A0A8I5KYE6;A0A8J8YL86;B1AKN6;B4DHW2;K7EJB0;K7EKH0;K7EN08;K7ESG9;Q5W0Y9;S4R308;S4R3K4;U3KPY9;A0A8V8TP49;A0A8V8TP65;A0A8V8TPN0;A0A8V8TQG7;A0A8V8TQI3;A0A8V8TQV0;S4R3W2 NFIA_HUMAN;NFIB_HUMAN;NFIC_HUMAN;NFIX_HUMAN NFIA;NFIB;NFIC;NFIX Nuclear factor 1 B-type 62317.2 26091.3 78421.7 93444.6 86747.3 72582.8 99939.7 67439.5 68471.7 69724 91392.1 53370.2 39436 84846.6 101732 51433.6 +O00754 O00754 MA2B1_HUMAN MAN2B1 Lysosomal alpha-mannosidase 255039 232199 345019 306299 388713 327499 449267 237363 426552 358150 386912 333219 394132 409627 455358 416318 401376 358325 287566 427257 +O00764 O00764;F2Z2Y4 PDXK_HUMAN PDXK Pyridoxal kinase 119481 159428 90885.8 194679 148282 127798 145388 162815 182212 146355 154354 188354 137051 69082.1 124366 59596.7 123081 137677 55203.5 +O14579 O14579;M0QXB4 COPE_HUMAN COPE Coatomer subunit epsilon 40401.1 64949.5 36749 62142.5 94794.7 65969.6 57849.5 63572.3 38548.9 63850.9 57521.8 86941.1 27236.3 74553.5 49962.4 51393.2 +O14657 O14657 TOR1B_HUMAN TOR1B Torsin-1B 456718 225789 341615 490022 523430 514809 376963 566908 480233 638009 460174 742553 502822 616845 499412 586555 486902 559401 429781 549743 +O14672 O14672 ADA10_HUMAN ADAM10 Disintegrin and metalloproteinase domain-containing protein 10 325721 311562 288027 708165 341623 509960 386468 443795 405149 664085 405204 812922 327074 632376 372040 715388 495774 806206 338504 971251 +O14732 O14732 IMPA2_HUMAN IMPA2 Inositol monophosphatase 2 267555 436220 202590 350835 257004 256923 331140 310478 278074 342705 295167 278427 252387 269772 168948 408110 362597 396624 317585 294918 +O14737 O14737;K7EQA1 PDCD5_HUMAN PDCD5 Programmed cell death protein 5 155518 194109 263153 239037 269776 206140 285299 184941 288713 219532 219981 192630 264737 172715 312344 179145 225188 134154 211128 248714 +O14744 O14744;G3V2X6;G3V580;G3V5L5;H0YJX6 ANM5_HUMAN PRMT5 Protein arginine N-methyltransferase 5 184785 193414 65614.4 161624 250586 245074 188376 228702 237376 208285 221641 183203 208794 230790 233742 200688 185814 177319 219196 89434.1 +O14745 O14745 NHRF1_HUMAN SLC9A3R1 Na(+)/H(+) exchange regulatory cofactor NHE-RF1 160615 304721 144497 310561 288574 381747 252774 411927 215386 443975 302742 358988 265762 401535 378580 318637 231412 291424 332225 183401 +O14773 O14773;A0A2R8YGD1;A0A2R8Y7U1;A0A804HJ17;A0A2R8Y7I4;A0A2R8YD72 TPP1_HUMAN TPP1 Tripeptidyl-peptidase 1 1.11239e+06 895811 897900 1.58749e+06 1.85673e+06 1.41039e+06 1.82647e+06 1.21815e+06 1.96499e+06 1.75783e+06 1.79192e+06 1.75623e+06 1.97903e+06 1.65435e+06 2.566e+06 1.81008e+06 1.43462e+06 1.23593e+06 1.09778e+06 1.81989e+06 +O14776 O14776;A0A7P0T8N8 TCRG1_HUMAN TCERG1 Transcription elongation regulator 1 84260.6 97240.5 105268 90514.3 113476 89542.3 102698 73074.5 77423.2 129832 113700 107594 88372.8 75790.8 111845 110374 +O14786 O14786;E9PEP6;Q5JWQ6;Q5T7F0;E7EX60;H0Y4A0;Q5JWQ4 NRP1_HUMAN NRP1 Neuropilin-1 350458 49309.5 617423 72521.8 492429 71979.7 686679 90205 508720 81628.7 606352 77846.2 547654 97420.8 715171 124827 584813 106377 357167 120964 +O14818 O14818;H0Y586 PSA7_HUMAN PSMA7 Proteasome subunit alpha type-7 4.65571e+06 3.47267e+06 4.94011e+06 3.33377e+06 3.94552e+06 3.40658e+06 5.30621e+06 2.92573e+06 4.28183e+06 4.26416e+06 4.75847e+06 2.90402e+06 3.91188e+06 3.68321e+06 4.85373e+06 4.42305e+06 5.93349e+06 4.80097e+06 4.20431e+06 3.43068e+06 +O14929 O14929 HAT1_HUMAN HAT1 Histone acetyltransferase type B catalytic subunit 144843 278317 51928.2 204756 218672 239641 144163 292176 139704 212108 195304 187089 171458 193178 193506 201105 78599.5 189760 177021 136443 +O14950;P19105 O14950;P19105;J3QRS3 ML12A_HUMAN;ML12B_HUMAN MYL12A;MYL12B Myosin regulatory light chain 12B 276095 339551 358724 429738 306237 390927 293730 390937 323375 412147 379795 358018 267771 380519 235343 392276 291467 278916 331917 299624 +O14974 O14974;H0YIM2;H0YIS3 MYPT1_HUMAN PPP1R12A Protein phosphatase 1 regulatory subunit 12A 45445.6 100695 69748.7 119740 160970 80556.4 153170 122634 143940 68972.3 140860 232301 171061 117367 139345 58853.3 153069 90566.6 +O14979 O14979;A0A087WUK2 HNRDL_HUMAN HNRNPDL Heterogeneous nuclear ribonucleoprotein D-like 59399.6 94686 109570 125039 443443 498142 132078 139627 672687 506582 67727.7 151674 539091 521221 120609 113998 371405 317403 58101.2 76664.8 +O14980 O14980;A0A7I2V2C5;A0A7I2V2H0;A0A7I2V2S3;A0A7I2V2Y6;A0A7I2V3J1;A0A7I2V3N0;A0A7I2V3P3;A0A7I2V461;A0A7I2V4A3;A0A7I2YQP1;A0A7I2YQV4;A0A7I2YQX3;A0A7P0Z4B7;C9J673;C9JKM9;F8WF71 XPO1_HUMAN XPO1 Exportin-1 205835 198233 117810 127121 176931 203389 127205 210023 147300 167347 143163 170550 194513 193845 89895.2 162766 120629 164592 217324 133091 +O15027 O15027;A0A3F2YNX0;A0A0C4DH09;A0A3F2YNZ0;A0A8I5KPG1;F1T0I1;X6RGP5;A0A8I5KQ88;Q8N9G1 SC16A_HUMAN SEC16A Protein transport protein Sec16A 46923.3 59488.5 64632.2 16249 32321.6 30612.6 22810.3 +O15031 O15031;E2PU09 PLXB2_HUMAN PLXNB2 Plexin-B2 109205 67961.4 137622 209870 159533 132270 155473 152823 190229 189458 163276 208751 159133 197442 166663 165787 144087 159390 105131 163935 +O15042 O15042;E7ET15;H0Y8D9 SR140_HUMAN U2SURP U2 snRNP-associated SURP motif-containing protein 35074.1 61997.6 29007.2 54820.6 55326.7 76552.2 40747 82759.1 90415.2 46130.4 24842.1 34223.1 60955.5 +O15067 O15067;H0YGH1 PUR4_HUMAN PFAS Phosphoribosylformylglycinamidine synthase 687485 474715 618499 427810 626748 459207 635287 415526 618106 477746 587352 421573 607312 424401 554920 440859 738028 490960 621621 471638 +O15143 O15143;A0A1W2PNV4;C9JEY1;C9K057;F8VXW2 ARC1B_HUMAN ARPC1B Actin-related protein 2/3 complex subunit 1B 285490 234310 172366 276940 276934 242898 306167 225534 243251 199214 287533 247147 289044 243680 314003 322524 131618 256864 357815 350455 +O15144 O15144;C9JTV5;G5E9J0;G5E9S7 ARPC2_HUMAN ARPC2 Actin-related protein 2/3 complex subunit 2 237466 231781 212489 135994 277032 163157 279083 187508 230300 161316 269573 180317 244995 137654 193301 276515 252921 102949 330412 196555 +O15145 O15145;C9JZD1;F8VR50 ARPC3_HUMAN ARPC3 Actin-related protein 2/3 complex subunit 3 61393.2 30462.7 11295.9 82243.3 20992.5 67233.3 +O15160 O15160;A0A2R8Y5D3;A0A2R8YEY5;A0A2R8YEZ4;D6RDJ3;E7EQB9;A0A2R8Y8D6;H0Y723 RPAC1_HUMAN POLR1C DNA-directed RNA polymerases I and III subunit RPAC1 102251 122167 79191.4 91446.7 78592.7 152004 88116.4 116152 97218.9 115052 113897 92616 87843.3 116819 69566.9 95295.9 67054.5 112232 119572 59139.9 +O15164 O15164 TIF1A_HUMAN TRIM24 Transcription intermediary factor 1-alpha 33817.5 31278.3 +O15230 O15230 LAMA5_HUMAN LAMA5 Laminin subunit alpha-5 1.90936e+06 459587 2.21243e+06 1.04368e+06 2.11193e+06 834491 2.35618e+06 1.00472e+06 2.48887e+06 862927 2.68478e+06 1.30041e+06 2.18834e+06 1.05519e+06 2.20846e+06 1.15704e+06 1.82044e+06 1.08327e+06 1.49252e+06 1.05811e+06 +O15294 O15294;A0A8V8TP17;A0A8V8TPA3;A0A8V8TQ37;A0A8V8TQ64 OGT1_HUMAN OGT UDP-N-acetylglucosamine--peptide N-acetylglucosaminyltransferase 110 kDa subunit 79789.2 41099 68661.3 40817 64870.2 89186.1 87763 95061.5 +O15305 O15305;H3BV55;A0A804HKA7;H3BRM0 PMM2_HUMAN PMM2 Phosphomannomutase 2 66656.1 79049.9 24947.8 55943.8 65945.8 97354.8 50068.6 85935.5 26855.8 94023.6 83807.8 92852.7 24755.3 73513 59826.4 101300 143331 83429.7 67002.5 37271.3 +O15347 O15347;E7EQU1;E7ES08;E9PES6 HMGB3_HUMAN HMGB3 High mobility group protein B3 394605 277067 385826 325173 239062 256949 356734 219854 289375 241965 346175 299589 235019 268839 239227 285940 339427 295467 378779 285577 +O15355 O15355 PPM1G_HUMAN PPM1G Protein phosphatase 1G 227248 150013 125501 137256 207953 149931 185208 147667 177133 139025 192726 148151 158661 142463 170474 131120 131026 124021 226030 66864.1 +O15371 O15371;B0QYA5;B0QYA6 EIF3D_HUMAN EIF3D Eukaryotic translation initiation factor 3 subunit D 194196 171018 129534 177901 169905 157680 117302 134757 168408 148059 164351 154820 183105 150953 134653 117297 201396 150121 204835 87417.9 +O15372 O15372;B3KS98 EIF3H_HUMAN EIF3H Eukaryotic translation initiation factor 3 subunit H 1.37937e+06 865894 604981 665814 1.129e+06 855419 707837 896102 808400 736862 884830 869140 853101 806618 643894 716329 694000 560844 1.16214e+06 484418 +O15427 O15427;J3KTM6;J3QQV2;J3QRA0;J3QSC3;J3QQS9;J3QRP8 MOT4_HUMAN SLC16A3 Monocarboxylate transporter 4 75985 24948.5 88510.9 42070.1 60566.9 43543.5 89331 33209.8 118049 63628.5 142421 93940.8 117818 81203.7 47684.5 53914.8 126885 75635.6 110481 49044.1 +O15446 O15446 RPA34_HUMAN POLR1G DNA-directed RNA polymerase I subunit RPA34 40840.3 109211 103230 50198.5 69419.9 44430.4 95791.3 46051.8 85218.3 43884.9 90277.8 20831 68703.9 29498.2 71930.5 88301.3 52596.5 35149.2 +O15511 O15511 ARPC5_HUMAN ARPC5 Actin-related protein 2/3 complex subunit 5 301531 343852 527059 390567 407672 302585 607636 337724 375494 278531 530981 298383 449941 322408 626882 492205 362481 283269 461349 554395 +O15533 O15533;A0A0A0MSV9;A0A0G2JH37;A0A0G2JKZ1;A0A8V8TNS3;A0A8V8TP24;A0A8V8TPY1;A0A8V8TQC1;A0A8V8TQC5;A2AB90;C9JA35;Q6P1N7 TPSN_HUMAN TAPBP Tapasin 122729 83027.3 90708.7 89173.6 110632 180275 +O43143 O43143 DHX15_HUMAN DHX15 ATP-dependent RNA helicase DHX15 161165 168355 184258 156547 212974 218156 157296 242928 147303 208346 176093 207923 149135 193641 91103.9 220841 117961 183598 205882 81407.1 +O43172 O43172 PRP4_HUMAN PRPF4 U4/U6 small nuclear ribonucleoprotein Prp4 67766.9 67508.6 73283.1 73908 67650.2 75050.6 55793.1 89376.9 77400.1 75519 66261.5 90337.8 55514.4 91607.1 73497.9 71453.5 76566 85630.9 71559 +O43175 O43175;A0A286YF22;A0A2C9F2M7;A0A286YF78;A0A286YFA2;A0A286YFE1;A0A286YFL2;A0A286YFM8 SERA_HUMAN PHGDH D-3-phosphoglycerate dehydrogenase 190522 246847 152022 293796 247955 335500 190916 506439 266716 274318 353780 340383 169647 289471 239408 291313 134925 254432 414714 187930 +O43242 O43242 PSMD3_HUMAN PSMD3 26S proteasome non-ATPase regulatory subunit 3 331634 304121 174765 330066 246106 294856 220794 261708 242105 247470 260938 342610 227116 202084 234450 239291 219438 227699 364568 242680 +O43390 O43390;A0A6Q8PH35 HNRPR_HUMAN HNRNPR Heterogeneous nuclear ribonucleoprotein R 105809 104884 106817 115496 159089 119365 132228 128673 131971 136007 124586 132134 89497.6 122666 89700.8 90965.3 77835.6 102797 97460.3 43224.2 +O43396 O43396;K7EML9;K7ER96 TXNL1_HUMAN TXNL1 Thioredoxin-like protein 1 407381 451853 394721 483300 433614 484811 452260 400503 371116 420570 449573 443114 399218 476866 573246 565633 436571 435925 436122 391892 +O43399 O43399;A0A087WYR3 TPD54_HUMAN TPD52L2 Tumor protein D54 61702.6 42952.5 119903 107692 98872.9 82070.8 124694 157889 112545 85799.2 142220 141154 121606 122798 94140.8 78599.6 70220.6 82445.6 195511 +O43491 O43491;E9PHY5;E9PII3;E9PK52;Q6R5J7;E9PPC9;Q6ZSX4 E41L2_HUMAN EPB41L2 Band 4.1-like protein 2 86750.3 23322.3 130570 60488.2 103464 50415.6 169766 37886.8 176456 40998.1 117361 42554.1 172298 40670.5 99310.6 126906 +O43493 O43493;A0A5F9UY30 TGON2_HUMAN TGOLN2 Trans-Golgi network integral membrane protein 2 85110.8 57688.3 175180 139595 83180.4 56336.9 113633 65897.4 111511 98686.5 113855 104044 118004 100952 111194 119685 186799 117961 84125.6 164727 +O43505 O43505 B4GA1_HUMAN B4GAT1 Beta-1,4-glucuronyltransferase 1 229674 54476.9 255601 131242 260724 105386 222377 75518.8 306856 199184 295625 155717 430067 172462 259639 131987 413156 189775 260242 143900 +O43583 O43583;F8VVL1 DENR_HUMAN DENR Density-regulated protein 486848 269446 178845 221101 274097 198031 314735 175886 218989 253198 308794 192155 396431 200453 347065 275068 218144 225753 246994 340208 +O43592 O43592 XPOT_HUMAN XPOT Exportin-T 98760.5 89721.2 43917.5 138781 129734 208046 74263.4 157533 95687.8 129335 117783 157292 109940 149775 92068.3 120911 43049.8 122699 144012 79554.6 +O43598 O43598;H0Y8X4 DNPH1_HUMAN DNPH1 2'-deoxynucleoside 5'-phosphate N-hydrolase 1 49137.9 51673.4 47463 60586.2 86599.3 62268.3 49021.3 69103.2 26356.4 87576.7 107928 54608 94680.3 42323.9 40170.9 +O43617 O43617;A0A087WWM0;A0A087WYS5 TPPC3_HUMAN TRAPPC3 Trafficking protein particle complex subunit 3 123618 111095 76816.4 132383 111708 93039.8 89147.2 93540.8 98931.7 97036.6 90079.6 84155.1 112241 132418 98747.7 65617.9 82543.1 122032 +O43670 O43670;X6R4W8;J3KS31;J3QRS9 ZN207_HUMAN ZNF207 BUB3-interacting and GLEBS motif-containing protein ZNF207 49266.9 35634.5 20957.5 86908.4 50333.9 43700.2 51142.1 61045.7 +O43681 O43681 GET3_HUMAN GET3 ATPase GET3 87871.7 85461.9 39750.8 77677.4 120062 78611.6 105902 69268.9 89683.1 92148.9 71490.3 64895 92574.7 84761.8 67432.8 38699.8 60381.4 103879 +O43684 O43684;J3QSX4;J3QT28 BUB3_HUMAN BUB3 Mitotic checkpoint protein BUB3 1.46999e+06 1.7593e+06 1.76206e+06 1.93476e+06 1.80274e+06 1.98279e+06 1.5194e+06 1.91152e+06 1.88925e+06 2.0182e+06 1.63458e+06 1.84638e+06 1.87738e+06 1.94966e+06 906494 1.75852e+06 1.5497e+06 1.996e+06 1.79925e+06 1.52034e+06 +O43707 O43707;F5GXS2;H7C144;A0A8V8TL71 ACTN4_HUMAN ACTN4 Alpha-actinin-4 6.02498e+06 8.29414e+06 5.74125e+06 6.93606e+06 6.16575e+06 7.81079e+06 7.19327e+06 5.63236e+06 5.74107e+06 8.72115e+06 6.34306e+06 5.99758e+06 6.14609e+06 7.93345e+06 6.11792e+06 8.58208e+06 7.07944e+06 7.98627e+06 6.6529e+06 8.19489e+06 +O43719 O43719;Q5H918;Q5H919 HTSF1_HUMAN HTATSF1 HIV Tat-specific factor 1 53994.6 72537.7 83504.8 47454.5 67180.2 41956.3 63072.4 27771.8 47981.5 32279 50825.1 42667.2 51882.1 36104.2 46158.1 60393.5 +O43747 O43747 AP1G1_HUMAN AP1G1 AP-1 complex subunit gamma-1 68526.8 53453.3 44327.5 52650.2 74723.2 52827 68635.4 44865.5 53784.1 35269.8 59260.6 66001.5 62291.9 43164.3 47817.6 27625.5 68716.3 +O43765 O43765;A0A7I2V2U2;A0A7I2V4T9;A0A7I2V588;A0A7I2V5Z3;A0A7I2V697 SGTA_HUMAN SGTA Small glutamine-rich tetratricopeptide repeat-containing protein alpha 101006 167983 65517.7 143873 119709 162370 115996 139503 101219 129976 118035 137193 139188 128320 181578 130515 65670.1 123530 169826 71450.3 +O43768 O43768;P56211;A6NMQ3;H3BMD8;H3BQ52;H3BTD3;Q5T5H1 ENSA_HUMAN ENSA Alpha-endosulfine 58229.8 105140 109290 121414 95766.2 75643.1 151156 85414.9 52862 148765 58950.3 79033.6 84925.1 108228 70575.8 +O43776 O43776;K7EMQ6;K7EPK2;K7EQ35 SYNC_HUMAN NARS1 Asparagine--tRNA ligase, cytoplasmic 350498 325135 219954 335204 328604 317006 258659 358478 228265 265785 319788 307297 242704 290383 285254 300970 272734 276423 322308 195626 +O43790 Q14533;P78385;O43790;Q6NT21;A6NCN2 SWISS-PROT:O43790 SWISS-PROT:O43790 3.80462e+07 136989 125484 75320.9 707049 103686 191316 95403.8 254055 127538 617602 200427 42882.6 71493.3 +O43813 O43813;E9PHS0;F8WDS9 LANC1_HUMAN LANCL1 Glutathione S-transferase LANCL1 226726 363101 338088 255718 433362 274450 343496 249109 513188 172590 328412 224106 467165 232486 341325 184663 528962 250024 289669 +O43818 O43818 U3IP2_HUMAN RRP9 U3 small nucleolar RNA-interacting protein 2 105123 67313 64690.5 117045 138976 84234.2 115535 119890 92824.4 81136.9 127608 104797 137684 81391.8 119906 91079.9 78834.7 91370.9 131619 +O43847 O43847;G3V1R5 NRDC_HUMAN NRDC Nardilysin 217300 144850 118568 148444 178013 145048 194503 117923 155262 143642 173385 130049 140061 142964 145148 145367 122582 196769 185972 140542 +O43852 O43852 CALU_HUMAN CALU Calumenin 563558 471357 732516 744933 471260 471405 611898 399670 451980 695996 534864 691045 319869 556449 493692 680806 656543 609183 480670 684638 +O43854 O43854 EDIL3_HUMAN EDIL3 EGF-like repeat and discoidin I-like domain-containing protein 3 82614.9 191892 191918 226178 193724 283292 302943 200915 320783 178227 +O43865 O43865;Q96HN2;H0Y8B3 SAHH2_HUMAN AHCYL1 S-adenosylhomocysteine hydrolase-like protein 1 118680 161944 106922 204034 229366 186381 280694 204200 201904 177635 187163 235110 193382 93124.2 230926 131476 200344 253266 125291 +O43897 O43897;E9PD25;D6RCE0 TLL1_HUMAN TLL1 Tolloid-like protein 1 300143 158796 261723 381415 272373 248862 337352 234161 286252 260629 327538 458920 334950 332678 334932 358019 377849 339683 245706 460388 +O60216 O60216;A0A8I5KWV3 RAD21_HUMAN RAD21 Double-strand-break repair protein rad21 homolog 78951 105803 188733 186570 89821.1 102012 134159 109573 51929.3 108712 60181.3 113040 94609.5 173615 71197.5 75138.7 132519 64329.8 59886.2 +O60231 O60231;Q5SQH5;A0A140T947;A0A140T9T4 DHX16_HUMAN DHX16 Pre-mRNA-splicing factor ATP-dependent RNA helicase DHX16 27595.4 15866.9 17165.6 +O60232 O60232;E9PN57;G3V1B8;H0YEB6;H0YEK1 ZNRD2_HUMAN ZNRD2 Protein ZNRD2 113765 120739 59582.1 101750 150913 108737 89873.1 139332 113240 104852 116662 155783 108597 66089.2 79640.1 111387 +O60341 O60341;A0A8I5KRB8;A0A8I5KSH0;A0A8I5KV76;A0A8I5KVZ4;A0A8I5KXU4;A0A8I5QJJ5;A0A8I5QKM3;R4GMQ1;A0A8I5QJR3;A0A8I5KSI8;R4GMP9 KDM1A_HUMAN KDM1A Lysine-specific histone demethylase 1A 79030.9 49711.6 83568 91582.5 104014 98991.6 53288.1 95306.6 97491.5 60851.1 75499.5 77989.3 +O60361 P22392;O60361;Q32Q12 NDK8_HUMAN NME2P1 Putative nucleoside diphosphate kinase 2.44449e+06 1.26162e+06 1.23932e+06 1.43966e+06 1.3626e+06 1.27385e+06 1.50223e+06 563417 1.45406e+06 1.60064e+06 1.63128e+06 1.42507e+06 1.10789e+06 1.14209e+06 2.05139e+06 1.04235e+06 1.43549e+06 1.1404e+06 2.1452e+06 1.69382e+06 +O60493 O60493 SNX3_HUMAN SNX3 Sorting nexin-3 131931 122923 90091.8 159941 98615.3 94178.3 130786 95293 94601.9 117348 69817.8 137454 101451 140956 72029.7 64688 65662.7 157010 +O60502 O60502;H7C3X0 OGA_HUMAN OGA Protein O-GlcNAcase 93800.3 79830.8 79923 97248.5 76857.6 86259.1 68811.6 42332.9 79159.9 123319 76476.5 83766.1 101641 89784.7 75069.3 38569.6 128598 +O60506 O43390;O60506;A0A6Q8PH31;A0A6Q8PH35;A0A7I2V346;A0A7I2V4J0;A0A7I2V4Z0;A0A7I2V5Q6;A0A7I2YQN2;A0A7I2YQV8;F6UXX1 HNRPQ_HUMAN SYNCRIP Heterogeneous nuclear ribonucleoprotein Q 559680 746371 542829 740673 934595 829695 762897 825275 808589 794921 711032 759134 844129 788448 678624 723786 599208 684558 644260 557722 +O60513 O60513;C9J3R8;E7ETS9 B4GT4_HUMAN B4GALT4 Beta-1,4-galactosyltransferase 4 28591.5 76088 115100 130991 56674.3 163407 183140 134265 78147.7 141141 118682 158181 77736.3 133729 59418.6 98568.8 65497.2 70909.1 +O60547 O60547 GMDS_HUMAN GMDS GDP-mannose 4,6 dehydratase 181121 105385 98843 91016.6 161838 127870 97836.4 81713.7 109314 101457 174796 102187 211417 129612 104875 129535 85734 88120.6 51079.6 +O60568 O60568;H7C0B8;H7C2V1 PLOD3_HUMAN PLOD3 Multifunctional procollagen lysine hydroxylase and glycosyltransferase LH3 531768 1.00262e+06 750704 1.5173e+06 809229 1.30893e+06 1.03953e+06 1.24618e+06 918114 1.2857e+06 951592 1.40974e+06 750428 1.49439e+06 864157 1.95972e+06 737579 1.80871e+06 560064 2.04171e+06 +O60684 O60684;Q5TFJ7 IMA7_HUMAN KPNA6 Importin subunit alpha-7 134862 161888 95214.6 118308 188700 110530 200012 80211 126602 95577 149553 97132.7 157379 126093 142312 70998.5 149709 184845 88365 +O60701 O60701;D6RHF4;E7ER83;E7ER95;E7ETF4;E7EV97;E9PBD2 UGDH_HUMAN UGDH UDP-glucose 6-dehydrogenase 59941.1 110487 89262.3 32896.5 163967 142189 92309.4 150502 62826.3 143158 55788.6 147944 44388.1 125683 53144.5 112902 59997.8 107282 +O60749 O60749;D6RC15 SNX2_HUMAN SNX2 Sorting nexin-2 52377.9 63804.6 45547.8 73003.5 81387.4 27661 86603.6 79859.7 27041.4 50983.7 76280.3 70760.4 24153.2 73853.9 40882.9 59196.1 83594.8 +O60763 O60763 USO1_HUMAN USO1 General vesicular transport factor p115 83172.7 53952.9 105444 34079.8 103114 61342.8 40771.4 94575.7 33920.8 92123.5 82512.7 67906.9 53123.8 32511.7 +O60814;P57053;P58876;P62807;Q5QNW6;Q93079;Q99877;Q99879;Q99880 O60814;P06899;P23527;P33778;P57053;P58876;P62807;Q16778;Q5QNW6;Q8N257;Q93079;Q99877;Q99879;Q99880;U3KQK0 H2B1C_HUMAN;H2B1D_HUMAN;H2B1H_HUMAN;H2B1K_HUMAN;H2B1L_HUMAN;H2B1M_HUMAN;H2B1N_HUMAN;H2B2F_HUMAN;H2BFS_HUMAN H2BC10;H2BC12;H2BC12L;H2BC13;H2BC14;H2BC15;H2BC18;H2BC5;H2BC9 Histone H2B type 1-K 1.72085e+07 1.20726e+06 1.81582e+06 2.20416e+06 2.20927e+06 1.25121e+06 1.63364e+06 1.81389e+06 2.46918e+06 1.4434e+06 2.28403e+06 2.03505e+06 2.23616e+06 1.66623e+06 1.64979e+06 1.73569e+06 1.98084e+06 1.52137e+06 1.25484e+06 1.59712e+06 +O60828 O60828 PQBP1_HUMAN PQBP1 Polyglutamine-binding protein 1 102227 184067 309277 345338 374246 481909 287647 457072 491892 573523 296158 408863 495114 469049 376399 324869 313706 369445 326224 228355 +O60832 O60832;C9IYT0;A0A8Q3SIS9;A0A8Q3SIX2;A0A8Q3SIY6;A0A8Q3SJE8;A0A8Q3WLC8;A0A8Q3WLD1;A0A8Q3WLG7;A0A8V8TKY9 DKC1_HUMAN DKC1 H/ACA ribonucleoprotein complex subunit DKC1 191095 183352 129968 256139 161121 215041 166083 238417 125569 172740 177814 251998 127182 210906 150670 190725 127552 213373 161739 94797.1 +O60841 O60841;A0A087WUT6 IF2P_HUMAN EIF5B Eukaryotic translation initiation factor 5B 247120 256297 161395 215099 227546 234323 236767 222168 208097 209962 236773 232199 151123 227580 123019 215993 181758 194315 335110 119548 +O60869 O60869 EDF1_HUMAN EDF1 Endothelial differentiation-related factor 1 639188 852362 793056 1.01156e+06 859641 829806 845421 740223 677930 860093 737606 694657 748931 701557 959283 713646 431281 468786 787317 617383 +O60884 O60884;A0A087WT48 DNJA2_HUMAN DNAJA2 DnaJ homolog subfamily A member 2 152542 133261 166721 107549 203843 176881 177603 227092 140482 165689 242812 215770 114957 190699 229425 157298 110645 156972 303891 78826.4 +O60888 O60888;C9IZG4 CUTA_HUMAN CUTA Protein CutA 510361 637381 1.4139e+06 920185 882302 926556 1.30876e+06 883068 1.06777e+06 1.1873e+06 1.13518e+06 800971 1.14168e+06 989210 1.56086e+06 1.21684e+06 956185 1.30785e+06 767200 1.79749e+06 +O60911 O60911;A0A7P0T8X8;A0A7P0Z415;A0A804EQJ3 CATL2_HUMAN CTSV Cathepsin L2 483887 75778.4 867837 185152 456753 108659 606305 121286 408498 150123 690079 217380 533015 150865 708038 215231 666822 238713 531811 295570 +O75063 O75063 XYLK_HUMAN FAM20B Glycosaminoglycan xylosylkinase 67672.5 59860.6 62842 72297 119724 90904 128608 93459.6 138662 117732 109192 106018 121408 133097 183535 128109 142074 162432 83846.8 122535 +O75083 O75083;A0A8V8TNT7;A0A8V8TNU2;A0A8V8TP22;A0A8V8TP31;A0A8V8TQ69;A0A8V8TQ74;A0A8V8TQK0 WDR1_HUMAN WDR1 WD repeat-containing protein 1 591967 500805 547286 674900 774232 684362 681092 634820 738518 628970 779526 620012 875657 635294 566732 601280 619365 684181 617692 564575 +O75116 O75116;E9PF63;A0A8V8TL82;A0A8V8TL90 ROCK2_HUMAN ROCK2 Rho-associated protein kinase 2 1.06977e+06 807150 673521 440065 774273 327997 355024 446457 838039 652593 682901 490185 518829 449462 622068 502635 902447 +O75144 O75144;K4DIA0;A0A2R8Y5W7;A0A8V8TQ42;A0A8V8TQ73;A0A8V8TQV9;A0A8V8TQW2;B7Z1W8 ICOSL_HUMAN ICOSLG ICOS ligand 37143.1 41527.8 32772.9 65478 49785.6 55423 45018.7 85160.9 97369.9 45311.8 47305.9 66996.9 90860.8 100752 86204.5 70846.6 51488 48666.7 34771.2 +O75153 O75153;A0A494C0R8;I3L2B0;K7EIG1 CLU_HUMAN CLUH Clustered mitochondria protein homolog 172855 228201 87747.3 159560 198652 234600 140954 210780 159227 186117 168401 190966 152519 211796 182623 178824 119872 172953 218446 103842 +O75223 O75223;B8ZZK2;H7BZK5;M0QZK8 GGCT_HUMAN GGCT Gamma-glutamylcyclotransferase 384979 221943 342500 200265 253420 209235 327177 147062 206998 271849 257969 235831 263228 238300 248609 341092 477674 235731 307210 347621 +O75312 O75312;H7BZM7;H7C0E5 ZPR1_HUMAN ZPR1 Zinc finger protein ZPR1 15352.8 18924.8 38978.1 34775.7 31789.4 21558.3 47196.8 32130.1 15995.5 27225.6 46435.6 19239.1 38904.4 +O75326 O75326;F5GYX3 SEM7A_HUMAN SEMA7A Semaphorin-7A 81384.5 56491 86620.7 50014.2 76739.2 60067.6 68215.2 59569 99057.7 63538.8 81020.1 84230.8 81384.5 90254.2 +O75342 O75342 LX12B_HUMAN ALOX12B Arachidonate 12-lipoxygenase, 12R-type 12387.6 44661 29420 +O75347 O75347;E5RIW3;E5RJD8;E5RHG6 TBCA_HUMAN TBCA Tubulin-specific chaperone A 739959 766492 578663 613096 745519 733729 945984 567576 548346 786230 810435 653348 752859 812491 1.35627e+06 894627 579158 768709 1.08958e+06 926667 +O75351;Q6PIW4;Q9UN37 O75351;Q6PIW4;Q9UN37;I3L4J1 FIGL1_HUMAN;VPS4A_HUMAN;VPS4B_HUMAN FIGNL1;VPS4A;VPS4B Vacuolar protein sorting-associated protein 4B 58815.4 55913.1 54486.6 83854.4 38740.3 75180.8 23802.5 25740 13630 35146.5 37759.3 42678.8 39872.5 55140.9 77320.8 +O75367 O75367;B4DJC3 H2AY_HUMAN MACROH2A1 Core histone macro-H2A.1 305860 50981.2 48696.8 75425 91441.4 67648.5 90058.7 76874.8 111304 118122 104841 138812 80424 +O75368 O75368 SH3L1_HUMAN SH3BGRL Adapter SH3BGRL 67464.3 17830.8 28613.5 22801.2 62239.4 32640.9 30776.7 25359.1 42917.1 46799.5 29833.2 +O75369 O75369;A0A804HK76;A0A804HL72;E7EN95 FLNB_HUMAN FLNB Filamin-B 1.60596e+06 2.55969e+06 1.28586e+06 2.83458e+06 1.68789e+06 2.91787e+06 1.45769e+06 2.85114e+06 1.54203e+06 3.00376e+06 1.51528e+06 2.80251e+06 1.44615e+06 2.97745e+06 1.53404e+06 2.41235e+06 1.44427e+06 2.86836e+06 1.49487e+06 2.02793e+06 +O75390 O75390;A0A0C4DGI3;B4DJV2;H0YIC4 CISY_HUMAN CS Citrate synthase, mitochondrial 430072 175023 298164 141803 304074 170566 256445 146517 331190 175325 233233 155063 286670 226909 302553 156550 319257 178679 202425 +O75436 O75436 VP26A_HUMAN VPS26A Vacuolar protein sorting-associated protein 26A 728117 770079 1.19849e+06 730890 691407 590086 829760 599008 767024 749538 878559 518323 709780 543674 570259 830279 819705 641730 830191 810863 +O75459 O75459 PAGE1_HUMAN PAGE1 P antigen family member 1 300603 192938 190837 225569 237138 245540 174763 193245 260949 291451 +O75462 O75462;A0A804HI12;M0QZL6 CRLF1_HUMAN CRLF1 Cytokine receptor-like factor 1 141839 116943 196501 130895 29455.5 240425 150754 182343 40653.5 149824 127539 119726 +O75467;Q6AW86 O75467;Q6AW86;M0R090;M0R0B1;M0R1X9;M0R3B5 Z324A_HUMAN;Z324B_HUMAN ZNF324;ZNF324B Zinc finger protein 324A 153686 158466 363845 +O75475 O75475 PSIP1_HUMAN PSIP1 PC4 and SFRS1-interacting protein 77048.5 74435.5 147602 83063.7 79077.8 72075.5 86968.4 98745.1 61202.3 77900 47794.5 42116.6 58547.7 99705.7 78418.9 61148.5 102786 +O75494 O75494;Q5JRI1;Q6IQ42;R4GMP8 SRS10_HUMAN SRSF10 Serine/arginine-rich splicing factor 10 101288 81273.9 92392.8 102487 110271 131792 154556 116967 114918 70807.1 118382 96121.9 144685 86856.1 152410 99517.1 135697 +O75496 O75496;E2QRF9;H7C608;C9K0U5 GEMI_HUMAN GMNN Geminin 20050.7 22532.1 29969.1 39948.3 +O75503 O75503;A0A087WZY0;A0A1B0GTR6;A0A1B0GTV7;A0A1B0GUE8;A0A1B0GV94;A0A1B0GWI2;A0A1B0GWJ7;A0A1C7CYZ2 CLN5_HUMAN CLN5 Ceroid-lipofuscinosis neuronal protein 5 309265 91476.8 133127 114651 145111 116646 152371 81034.6 114404 154050 132756 147953 127173 146018 69219.4 141789 197986 263199 138290 145268 +O75509 O75509 TNR21_HUMAN TNFRSF21 Tumor necrosis factor receptor superfamily member 21 113341 90745 95250.1 111813 93725.3 121121 119077 116788 70009.8 90141.9 133350 150831 122581 124407 243760 +O75531 O75531 BAF_HUMAN BANF1 Barrier-to-autointegration factor 280693 232516 237409 340817 181158 178703 185252 169141 106452 209992 274583 254114 139380 192820 205148 274712 196323 198330 282966 247368 +O75533 O75533 SF3B1_HUMAN SF3B1 Splicing factor 3B subunit 1 313661 209997 279011 402796 376320 359473 299744 378751 405529 406149 374747 433413 344067 366826 306937 305157 388677 361356 473970 282684 +O75534 O75534;E9PLT0;A0A8I5KPC0;A0A8I5KPJ6;A0A8I5KV85;A0A8I5KW72;A0A8I5KXH5;A0A8I5QJA6;A0A8I5QKX0;E9PNG3 CSDE1_HUMAN CSDE1 Cold shock domain-containing protein E1 137669 179293 48736.6 134098 166694 224781 122186 170739 88895.4 185104 119618 135993 136421 153912 150115 149945 131638 114837 273402 108525 +O75607 O75607 NPM3_HUMAN NPM3 Nucleoplasmin-3 165842 131618 223934 108950 156696 173061 114707 188526 131958 142902 112919 79475.4 134585 82398.4 155109 97175 +O75608 O75608;A0A087X1K9;B4DP64;E5RGR0 LYPA1_HUMAN LYPLA1 Acyl-protein thioesterase 1 304235 134432 154093 76727.6 121418 134572 111325 74350.1 55327.1 155371 127732 43091.6 185528 159596 131481 +O75629 O75629;A0A3B3IRL2 CREG1_HUMAN CREG1 Protein CREG1 812219 403598 666817 586115 679123 558595 659374 301256 681906 499260 698447 698727 729801 762803 719908 1.73153e+06 863179 669383 745149 906143 +O75643 O75643;A0A494C1A5 U520_HUMAN SNRNP200 U5 small nuclear ribonucleoprotein 200 kDa helicase 191159 166609 112024 236335 150429 189016 129388 190990 119043 175103 146163 213536 143996 159672 110368 149955 113607 159960 191779 93042.9 +O75718 O75718;C9JP16 CRTAP_HUMAN CRTAP Cartilage-associated protein 163727 65855.3 210205 149698 192924 135859 176664 135880 116553 128387 217143 206917 169254 140519 136500 130296 177288 145545 144844 125092 +O75787 O75787;A0A1B0GUT7;A0A1B0GV24;A0A1B0GV60;A0A1B0GVB9;A0A1B0GVW0;A0A1B0GW13;A0A1B0GWJ8;A0A1C7CYW4;B7Z1I9;H7C3E1;A0A1B0GTB0 RENR_HUMAN ATP6AP2 Renin receptor 404829 391113 364530 794889 494912 480720 576392 925425 481736 497071 785808 1.19795e+06 566369 591708 585789 827401 321655 544718 527636 676017 +O75794 O75794;X6RA30;X6RF82;X6RKY7 CD123_HUMAN CDC123 Cell division cycle protein 123 homolog 112937 105856 77585.2 110234 115152 88811.8 139964 100025 93284.9 99273.7 97106.2 121122 118467 112707 74950.2 131335 +O75821 O75821;K7EL20;K7ENA8;K7EP16;K7ER90 EIF3G_HUMAN EIF3G Eukaryotic translation initiation factor 3 subunit G 514315 487014 375905 480623 624082 616230 577059 456802 496589 582155 557303 657527 1.00782e+06 585224 571964 642451 405302 597243 461590 580098 +O75822 O75822 EIF3J_HUMAN EIF3J Eukaryotic translation initiation factor 3 subunit J 176003 198950 85694.5 161322 177548 239917 126021 186009 137910 263563 161240 243299 191258 260594 150067 232303 173450 238185 188407 117265 +O75874 O75874 IDHC_HUMAN IDH1 Isocitrate dehydrogenase [NADP] cytoplasmic 713068 387699 519992 433057 708395 537692 638646 460741 660848 505888 697992 465906 820162 482719 824824 553759 755260 475455 634873 442797 +O75882 O75882 ATRN_HUMAN ATRN Attractin 135607 78243.9 250433 207068 173092 122946 199715 111128 181318 189063 174879 189070 235382 166307 173576 223097 323482 213721 153263 210971 +O75937 O75937 DNJC8_HUMAN DNAJC8 DnaJ homolog subfamily C member 8 222854 247215 410952 304390 234214 226350 225199 192424 224183 235274 221773 247469 264709 207558 218070 214579 252727 157917 219750 250745 +O76003 O76003 GLRX3_HUMAN GLRX3 Glutaredoxin-3 236514 280123 119129 290784 110470 264086 157552 212604 107838 356576 178008 391984 163891 262786 92692.8 289223 297255 242171 260001 333030 +O76009 O76009;Q6NTB9 SWISS-PROT:O76009 SWISS-PROT:O76009 1.36536e+06 133259 108548 +O76011 O76009;O76011;O76013;O76015;Q14525;Q14532;Q15323;Q6IFU5;Q9UE12;Q8IUT8;Q6NTB9;A2A5Y0;Q9D646;REFSEQ:XP_986630;Q6A162;A0A140TA62;A0A140TA69;G3V1C2 SWISS-PROT:O76011 SWISS-PROT:O76011 3.06835e+07 300285 70179.9 45040.7 139608 840443 296702 273361 +O76013 O76013;Q92764;REFSEQ:XP_986630;C4AM86 SWISS-PROT:O76013 SWISS-PROT:O76013 987171 94533.7 864684 67994.2 1.8521e+06 603496 96684.4 +O76015 O76015 SWISS-PROT:O76015 SWISS-PROT:O76015 709947 440125 8496.15 382602 655702 194207 169815 171021 +O76021 O76021;J3QSV6 RL1D1_HUMAN RSL1D1 Ribosomal L1 domain-containing protein 1 269633 1.01463e+06 560946 1.58937e+06 196358 865091 299390 882806 209376 944713 343582 1.028e+06 195499 848162 203568 1.17198e+06 141469 1.00912e+06 172909 779620 +O76061 O76061;E5RG57;H0YB13 STC2_HUMAN STC2 Stanniocalcin-2 540785 381210 942566 772028 632899 350992 1.00124e+06 502103 605716 347583 963452 545482 422201 381715 1.1025e+06 642721 510852 442415 503285 241087 +O76070 O76070;F8W754 SYUG_HUMAN SNCG Gamma-synuclein 122645 364641 186330 593640 205583 403064 156537 440149 188010 488233 143461 510606 222008 433477 181856 410264 110530 291058 135109 345842 +O76071 O76071 CIAO1_HUMAN CIAO1 Probable cytosolic iron-sulfur protein assembly protein CIAO1 51101.5 41600.7 44890.3 29817.8 58685.3 55239.9 61194.9 68936.9 35492.8 57220.7 66341.9 40892.7 32246 52079.4 80556 56695.6 32681.6 55415.1 +O76076 O76076 CCN5_HUMAN CCN5 CCN family member 5 60488.6 129837 357842 45116.1 348915 43084.1 301452 57083.4 324492 34733.2 382841 62585.5 430825 74654 319303 26834.8 224076 48717.3 344660 +O76094 O76094;D6RDY6;R4GNC1 SRP72_HUMAN SRP72 Signal recognition particle subunit SRP72 10921 21455.7 21063.5 25682.4 12852.2 15991.2 17272.5 19139.6 27277 +O94760 O94760;B4DYP1 DDAH1_HUMAN DDAH1 N(G),N(G)-dimethylarginine dimethylaminohydrolase 1 164516 146879 167397 90603.2 184456 113907 183962 72220.6 170123 144555 170275 104014 174483 117399 169099 151276 205642 109930 167392 93692.1 +O94766 O94766;E9PNA1;G3V150 B3GA3_HUMAN B3GAT3 Galactosylgalactosylxylosylprotein 3-beta-glucuronosyltransferase 3 41702.3 46792.9 73114.2 26013.4 26359.3 113035 44908.6 70475.4 84253.3 78054.4 38962.3 +O94776 O94776 MTA2_HUMAN MTA2 Metastasis-associated protein MTA2 65421.7 65722.8 70149.2 57953.9 82707.6 45288.6 99597.3 59294.6 123912 65549.1 101924 105282 57136.5 80125.9 88127.7 100983 +O94875 O94875;A0A8Q3WKF2;A0A8Q3WKH1;A0A8Q3WKK4;A0A8Q3WLU7;A0A8V8TMA7;A0A8V8TMC7;A0A8V8TMD6;A0A8V8TNC1;H7BXR3;H7BZK1;H7C1R7 SRBS2_HUMAN SORBS2 Sorbin and SH3 domain-containing protein 2 76910.9 71608.4 258239 177396 303856 116391 236494 206854 223004 81722.8 +O94907 O94907 DKK1_HUMAN DKK1 Dickkopf-related protein 1 64073.7 370726 135176 744928 212962 690647 149018 647848 215506 669300 123636 732841 149964 866912 218407 595581 107608 912411 111054 525087 +O94979 O94979;D6REX3;D6RHZ5;H7BXG7 SC31A_HUMAN SEC31A Protein transport protein Sec31A 69278.1 38706.7 57110.7 85208.9 171852 150940 179169 216525 117854 182013 127719 86481 299559 188026 207461 153649 125293 189751 173619 +O94985 O94985;Q5SR54 CSTN1_HUMAN CLSTN1 Calsyntenin-1 5.40281e+06 3.72171e+06 6.93348e+06 6.39645e+06 5.59401e+06 4.68398e+06 7.39768e+06 4.41979e+06 7.07812e+06 6.3413e+06 7.1876e+06 6.32004e+06 6.55271e+06 6.38431e+06 7.72417e+06 7.78815e+06 9.39889e+06 7.57044e+06 5.19685e+06 8.39448e+06 +O95084 O95084 PRS23_HUMAN PRSS23 Serine protease 23 339406 388972 230014 470291 413190 539490 369256 746049 309108 576145 323729 746157 229402 627966 409235 679953 198056 504681 378754 411756 +O95147 O95147 DUS14_HUMAN DUSP14 Dual specificity protein phosphatase 14 771706 20371.6 +O95163 O95163;A0A6Q8PEX6;A0A6Q8PF66;A0A6Q8PFB7;A0A6Q8PFH9;A0A6Q8PFN8;A0A6Q8PG86;A0A6Q8PGW3;A0A6Q8PH48;A0A6Q8PHA0;A0A6Q8PHC9;A0A6Q8PHF4;A0A6Q8PHI5;F5H2T0 ELP1_HUMAN ELP1 Elongator complex protein 1 150235 134263 124705 118306 160843 133471 163252 159209 165333 155942 151020 101258 123623 206363 132696 142607 195865 +O95218 O95218 ZRAB2_HUMAN ZRANB2 Zinc finger Ran-binding domain-containing protein 2 133005 58389.3 162822 125102 131164 113913 147564 140702 108728 106778 134789 113053 90574.7 86950.9 124214 179420 +O95232 O95232;J3KPP4;D6RDI2;D6RHH0;U3KQT3 LC7L3_HUMAN LUC7L3 Luc7-like protein 3 21337.7 20193.6 42340.9 12727.3 42248.3 17269.6 28853.2 35503.2 43032.1 +O95274 O95274 LYPD3_HUMAN LYPD3 Ly6/PLAUR domain-containing protein 3 176782 573788 352039 917199 184331 647720 253857 581182 199105 912174 238504 622516 181732 891108 330549 900487 289927 1.12378e+06 137298 866249 +O95297 O95297 MPZL1_HUMAN MPZL1 Myelin protein zero-like protein 1 46697.3 50896.4 58466.9 14193.5 43133.5 27921.4 47443.3 28498.8 67336.7 27097 63282.4 17002.5 58007.5 9947.82 39064.2 43289.4 54105.2 +O95302 O95302 FKBP9_HUMAN FKBP9 Peptidyl-prolyl cis-trans isomerase FKBP9 162240 76610.2 110079 153131 102706 106730 136746 97511.3 92918.1 150506 115506 120850 124333 112655 118364 46216.4 90141 107303 133396 +O95336 O95336;M0R1L2;M0R261;M0R0U3 6PGL_HUMAN PGLS 6-phosphogluconolactonase 307460 422715 221938 340043 289189 341502 294452 206629 393571 441492 229614 176931 339376 373002 334549 312292 382163 432314 301683 318052 +O95340 O95340 PAPS2_HUMAN PAPSS2 Bifunctional 3'-phosphoadenosine 5'-phosphosulfate synthase 2 42456 104593 46767.3 70913.3 139259 55465.4 106092 100756 55809.2 75441 24395.2 98662.3 61593.4 74174.5 71564.1 68163.1 +O95347 O95347 SMC2_HUMAN SMC2 Structural maintenance of chromosomes protein 2 84275.2 109561 58242.9 85578.6 134062 63032.4 96986.9 87853.2 86668.6 82218.2 83186.9 90654.5 102498 73608.3 95709.6 76324.5 126916 +O95373 O95373 IPO7_HUMAN IPO7 Importin-7 499302 661920 220724 499244 382088 554126 384296 549866 381821 459930 513814 506837 425498 517667 342899 577456 343199 483030 615531 436989 +O95394 O95394;A0A494C0G1;A0A494C1E2;H0Y987;J3KN95;A0A087WT27;A0A494C1Q4;A0A494C099;A0A494C163;A0A8V8TLT8;A0A8V8TM25;A0A8V8TMH7;A0A8V8TMI1;A0A8V8TNH4;D6RIS6;H0Y8I3 AGM1_HUMAN PGM3 Phosphoacetylglucosamine mutase 45072.7 54641.8 46094.7 77869.1 56233.1 65680.3 48361.5 58584.9 59285.4 62470.5 26885.2 68394.2 59283.6 39151.5 49718.7 49983.2 25369 +O95400 O95400 CD2B2_HUMAN CD2BP2 CD2 antigen cytoplasmic tail-binding protein 2 15516.4 56347.9 52290 16956.4 56797.2 35271.6 47826.4 52093.2 36566.8 46548.5 37089.6 13839.4 51513.2 27050.4 +O95433 O95433;G3V438;H0YJ63 AHSA1_HUMAN AHSA1 Activator of 90 kDa heat shock protein ATPase homolog 1 206566 218861 72967.8 174958 205235 205594 172174 197290 143187 172131 185323 168109 208397 194720 140581 169943 179514 167687 259072 111682 +O95456 O95456 PSMG1_HUMAN PSMG1 Proteasome assembly chaperone 1 42603.5 83593 111187 57233.8 100062 97998.2 68793.6 140126 53786.1 77729.8 108832 73792.6 63412.6 58444.1 67929.9 113526 101677 +O95502 O95502;A0A1X7SBT7 NPTXR_HUMAN NPTXR Neuronal pentraxin receptor 43426.9 66025.7 82295.3 87802.5 12024.9 56908.6 21266.5 31433.3 63665.3 51063.4 35245.9 +O95633 O95633;A0A087X1Q2 FSTL3_HUMAN FSTL3 Follistatin-related protein 3 1.19786e+06 851635 1.88934e+06 1.46571e+06 2.12406e+06 1.24404e+06 1.8789e+06 1.13811e+06 2.05969e+06 1.42115e+06 1.92596e+06 1.57468e+06 2.07511e+06 1.25156e+06 2.92e+06 1.28147e+06 1.40085e+06 995780 1.38678e+06 1.54364e+06 +O95757 O95757;D6RJ96;E9PDE8 HS74L_HUMAN HSPA4L Heat shock 70 kDa protein 4L 340145 384829 226915 377338 398900 370495 335722 404317 359278 357379 395517 319961 336484 364727 446925 347199 345835 318343 384134 203557 +O95782 O95782 AP2A1_HUMAN AP2A1 AP-2 complex subunit alpha-1 123549 90677.7 93372 83695.4 97108.4 96126.4 76742.6 119416 82553.8 94328 101932 101600 131850 94562.8 70430.6 92967.2 47476.4 95904.9 100235 68201.1 +O95816 O95816 BAG2_HUMAN BAG2 BAG family molecular chaperone regulator 2 123409 76471.4 66893.8 89497.1 73167.5 63815.3 95226.4 55507.9 63605 53959.6 62950.8 73788 73802 83801.6 91855.1 75447.8 64991.1 +O95817 O95817 BAG3_HUMAN BAG3 BAG family molecular chaperone regulator 3 60655.8 33751.4 76144.8 53250.6 89161.6 68556.3 57210.3 79240.7 138149 68008.4 94401 68778.3 77263.5 45567.4 72979.1 83984.1 44372.6 83365.1 +O95831 O95831;A0A6Q8PFE1;A0A6Q8PFM5;A0A6Q8PFQ8;A0A6Q8PFS4;A0A6Q8PG15;A0A6Q8PHC0;A0A6Q8PHJ4;A0A6Q8PHJ9;A0A7I2PK44;E9PMA0;A0A6Q8PFA7;A0A6Q8PFW2;A0A7I2RLJ8 AIFM1_HUMAN AIFM1 Apoptosis-inducing factor 1, mitochondrial 34671.9 58194.3 102824 57804.1 77427.4 40344.9 85271.1 46419.6 71233.1 41018.7 76747.9 69349.3 80629.5 66883.7 86184.5 66077.1 71155.1 39526.5 42962.2 +O95834 O95834;A0A0C4DGQ7;C9JRL6;K7EIK7;K7EII6;K7EKG3;K7ELI8 EMAL2_HUMAN EML2 Echinoderm microtubule-associated protein-like 2 83383.4 70859.6 71909.6 74943.4 95196.9 72382.2 53998.9 97129.8 68679 81680.3 41383.8 93203.9 95131.9 87626.6 108504 92991.1 86324.9 83624.3 +O95861 O95861;A6NF51 BPNT1_HUMAN BPNT1 3'(2'),5'-bisphosphate nucleotidase 1 245727 353566 250630 246856 242179 257323 236090 212973 184279 330352 272684 203599 192275 242950 260353 394631 297798 355691 284563 466619 +O95881 O95881 TXD12_HUMAN TXNDC12 Thioredoxin domain-containing protein 12 213444 317052 402100 247784 200483 198134 235641 168891 282788 285651 239954 252311 156168 203582 332648 302934 273459 262448 254188 317031 +O95989 O95989;A0A1W2PQS6;S4R435 NUDT3_HUMAN NUDT3 Diphosphoinositol polyphosphate phosphohydrolase 1 58083.7 61258.7 190586 113613 71550.5 81238.1 +O96019 O96019 ACL6A_HUMAN ACTL6A Actin-like protein 6A 113648 140499 164542 144281 179711 176318 161335 189573 180140 200582 143397 151768 187769 161091 58035.7 142487 101089 186079 128861 127042 +P00338 P00338 LDHA_HUMAN LDHA L-lactate dehydrogenase A chain 1.0136e+07 1.25032e+07 1.08203e+07 9.85314e+06 1.06884e+07 9.3049e+06 1.21315e+07 7.32869e+06 1.20946e+07 1.02616e+07 1.10214e+07 7.42555e+06 1.11436e+07 9.43405e+06 1.35333e+07 1.01197e+07 1.25983e+07 1.06171e+07 1.01987e+07 1.14143e+07 +P00367 P00367;P49448;A0A804HJD7;A0A804HKE0;A0A804HHS2;A0A804HJZ0 DHE3_HUMAN GLUD1 Glutamate dehydrogenase 1, mitochondrial 137111 103336 307676 188880 141881 88804.5 176112 54480 151472 103761 155749 93369.6 136205 90580.2 156562 120494 186738 147616 94288.7 152420 +P00374 P00374 DYR_HUMAN DHFR Dihydrofolate reductase 43051.1 81560.6 56813.5 55114.7 107353 84696.9 106088 55121.7 90418.2 50233.5 99591.8 70636.7 31641.1 95380.6 77822.1 22652.5 +P00390 P00390 GSHR_HUMAN GSR Glutathione reductase, mitochondrial 730189 1.06581e+06 824812 884100 671960 694481 794946 545212 626443 818332 661818 579715 701364 766000 869808 888299 816417 804308 653670 932955 +P00441 P00441;H7BYH4 SODC_HUMAN SOD1 Superoxide dismutase [Cu-Zn] 1.05291e+06 1.36139e+06 1.25117e+06 987231 853840 954758 1.05543e+06 641140 714923 1.20128e+06 913168 788035 1.03634e+06 1.12759e+06 1.11903e+06 1.18434e+06 1.09574e+06 1.23076e+06 933831 1.30722e+06 +P00491 P00491;G3V5M2;A0A8V8TL37 PNPH_HUMAN PNP Purine nucleoside phosphorylase 649195 1.04418e+06 1.04962e+06 791877 702116 838959 840223 610098 634136 879742 621450 541095 786835 716946 444667 964142 868727 939449 715095 878147 +P00492 P00492 HPRT_HUMAN HPRT1 Hypoxanthine-guanine phosphoribosyltransferase 976423 1.0317e+06 876105 810154 729090 733049 889584 420596 782260 875268 807671 657749 843009 707738 567437 950517 1.23077e+06 874467 987926 945132 +P00505 P00505 AATM_HUMAN GOT2 Aspartate aminotransferase, mitochondrial 1.24968e+06 1.0604e+06 2.7179e+06 1.3993e+06 1.26663e+06 813418 1.66435e+06 704511 1.39477e+06 1.01952e+06 1.27987e+06 880730 1.49375e+06 993520 1.56689e+06 1.18377e+06 1.68215e+06 1.08032e+06 926474 1.54123e+06 +P00533 P00533;Q504U8;C9JYS6 EGFR_HUMAN EGFR Epidermal growth factor receptor 60837.3 38717.6 11427.4 36664.8 14780.1 46558.3 59997.2 51368.4 39216.1 28904.2 26455.3 46503.2 +P00558 P00558 PGK1_HUMAN PGK1 Phosphoglycerate kinase 1 1.55472e+06 2.14319e+06 1.58967e+06 1.92094e+06 1.98613e+06 2.5839e+06 1.93103e+06 2.12256e+06 2.06822e+06 2.60034e+06 1.88842e+06 1.94951e+06 1.78367e+06 2.37896e+06 1.8748e+06 2.54062e+06 2.00489e+06 2.62503e+06 1.86353e+06 2.45507e+06 +P00568 P00568;Q5T9B7 KAD1_HUMAN AK1 Adenylate kinase isoenzyme 1 163788 231281 40632.1 208261 190096 212247 142349 213817 119903 213185 136356 165060 156423 226571 156533 196822 115460 157425 187333 190768 +P00735 P00735 SWISS-PROT:P00735 SWISS-PROT:P00735 137133 149614 178303 228038 158810 188619 134177 160146 153558 229498 154143 208715 179445 210771 116280 144271 131737 149895 116456 172991 +P00736 P00736;A0A3B3ISR2;B4DPQ0;F5H2D0 C1R_HUMAN C1R Complement C1r subcomponent 137317 702159 144588 1.25998e+06 160916 892311 245575 850606 143831 930341 238745 1.22956e+06 227501 1.17863e+06 307337 1.65872e+06 223111 1.51368e+06 189474 1.96061e+06 +P00740 P00740 FA9_HUMAN F9 Coagulation factor IX 106580 95843.7 234227 111573 139145 88391.1 141363 87000.5 197101 91585.5 236916 99914.9 212520 101074 157594 123805 161455 80976 154395 +P00746 P00746;A0A8Q3WKV6;A0A8Q3WKW0;K7ERG9 CFAD_HUMAN CFD Complement factor D 276720 567843 276679 854113 274005 404856 422284 550539 244773 482504 395421 1.10655e+06 206039 719232 603285 1.22167e+06 291110 466690 292634 905163 +P00750 P00750;A0A7I2YQ93;B4DN26;E7ESF4;A0A7I2V6D6;B4DNJ1 TPA_HUMAN PLAT Tissue-type plasminogen activator 45421.8 102100 41231.5 149969 135609 168967 174809 45164.1 183606 84814.3 115410 172351 23549.9 105513 +P00751 P00751;B4E1Z4;E7ETN3;H7C5H1 CFAB_HUMAN CFB Complement factor B 192000 432095 237897 664174 196384 550883 259494 444826 229479 693799 234014 676555 204383 690101 274554 746153 238936 737760 162769 847553 +P00761 P00761 SWISS-PROT:P00761|TRYP_PIG SWISS-PROT:P00761|TRYP_PIG 7.13698e+07 6.95396e+07 1.48161e+08 1.14116e+08 7.89859e+07 7.75114e+07 8.37466e+07 5.46745e+07 1.02527e+08 7.5726e+07 8.81882e+07 6.54831e+07 8.15452e+07 8.37614e+07 9.56236e+07 7.74329e+07 1.02451e+08 9.81048e+07 5.43431e+07 8.50294e+07 +P00813 P00813;F5GWI4;A0A8Q3SI46;A0A8Q3SIA9;A0A8Q3SIC0;A0A8Q3SIC7;A0A8Q3SIG8;A0A8Q3SIH0;A0A8Q3SJ63;A0A8Q3WKY2;A0A8Q3WKZ1;A0A8Q3WKZ4;A0A8Q3WKZ5;A0A8Q3WKZ6;A0A8Q3WKZ9;A0A8Q3WL01;A0A8Q3WM09;A0A8Q3WM84;A0A8Q3SI69;A0A8Q3WKX5;A0A8Q3WKX8;A0A8Q3WL06;A0A8Q3WLK2;A0A8Q3WM58;F5GXW0 ADA_HUMAN ADA Adenosine deaminase 31533.7 66974.1 35831.6 56231.4 48790.7 +P00966 P00966;Q5T6L5;Q5T6L6 ASSY_HUMAN ASS1 Argininosuccinate synthase 1.96491e+06 2.53184e+06 1.15416e+06 2.23358e+06 1.8801e+06 2.84138e+06 1.7411e+06 2.53717e+06 1.87434e+06 2.53619e+06 1.7901e+06 2.56964e+06 1.93298e+06 2.76801e+06 1.99546e+06 2.55021e+06 1.80561e+06 2.51584e+06 2.01056e+06 2.27179e+06 +P00978 P00978 SWISS-PROT:P00978 SWISS-PROT:P00978 612821 387337 604411 548226 490052 359996 428409 290191 548992 482869 491714 454459 448094 411413 421217 475472 554534 300821 407683 350802 +P01023 P01023 A2MG_HUMAN A2M Alpha-2-macroglobulin 44678.4 41318.6 27713.6 41493.2 21375.6 32214.8 39035.8 64436.8 86107.1 52088.9 55105.5 +P01024 P01024;A0A8Q3SI05;A0A8Q3WM02 CO3_HUMAN C3 Complement C3 366811 2.42285e+06 378447 3.81501e+06 365208 2.95393e+06 438549 2.84563e+06 431061 3.52094e+06 456408 4.03903e+06 452634 3.91706e+06 437117 5.15507e+06 496693 4.80904e+06 307803 5.59561e+06 +P01033 P01033;Q5H9B4;Q5H9B5;Q5H9A7 TIMP1_HUMAN TIMP1 Metalloproteinase inhibitor 1 3.13109e+06 1.84285e+06 2.53152e+06 3.96805e+06 2.95029e+06 2.42557e+06 3.32255e+06 2.02603e+06 2.2794e+06 2.84934e+06 3.68144e+06 3.98988e+06 2.76604e+06 2.63795e+06 4.76941e+06 2.11268e+06 2.24636e+06 2.12298e+06 3.438e+06 3.53727e+06 +P01034 P01034 CYTC_HUMAN CST3 Cystatin-C 521507 522134 696413 927328 864239 675799 892236 585820 974108 993857 820470 791883 752457 813470 885673 846873 696054 960983 703805 1.03958e+06 +P01040 P01040;C9J0E4 CYTA_HUMAN CSTA Cystatin-A 92736.4 12914.2 45416.4 121585 57645.2 73838.2 27064.2 90687 64345.4 120266 193194 184064 160891 106172 45894.7 291254 +P01045-1 P01044-1;P01045-1;Q2KJ62 SWISS-PROT:P01045-1 SWISS-PROT:P01045-1 152946 286088 276596 389213 331890 257388 269709 290600 419802 376733 300442 288659 501974 342606 263226 304700 364341 329602 179005 370826 +P01111;P01116 P01111;P01116;A0A8I5KQ21;A0A8I5KUB5;A0A8I5KYH6 RASK_HUMAN;RASN_HUMAN KRAS;NRAS GTPase NRas 179318 125665 110710 165273 244306 167070 219109 166474 304385 176214 254250 188136 251258 214039 247445 192012 185136 192837 212219 145981 +P01130 P01130;H0YM92;H0YMD1;J3KMZ9;H0YMQ3 LDLR_HUMAN LDLR Low-density lipoprotein receptor 472316 384514 751523 686849 604174 535280 645663 491028 634591 760325 672712 648285 642735 672604 839277 823821 648880 807820 415050 912991 +P01137 A0A499FJK2;P01137;A0A7I2YQL8 TGFB1_HUMAN TGFB1 Transforming growth factor beta-1 proprotein 207661 57784.5 329160 207905 399973 160342 318762 168518 434507 183492 416652 275156 401156 194640 245348 197263 345868 224323 255990 210916 +P01344 P01344 IGF2_HUMAN IGF2 Insulin-like growth factor II 14901.6 22088 15936.2 61267.9 29411.7 62090.4 50208.8 43523.5 70083.2 57172.7 84471.4 69275.8 52401.4 89519.4 49024.3 +P01591 P01591;C9JA05;D6RD17;D6RHJ6 IGJ_HUMAN JCHAIN Immunoglobulin J chain 106214 +P01834 P01834;A0A5H1ZRQ3 IGKC_HUMAN IGKC Immunoglobulin kappa constant 474580 237373 +P01857 P01857;A0A0A0MS07;A0A0A0MS08 IGHG1_HUMAN IGHG1 Immunoglobulin heavy constant gamma 1 484567 22065.1 80125.5 16876.2 46983.7 69503.2 145073 21649.2 31005.8 66555.9 112418 877011 +P01859;P01860 P01859;P01860;A0A286YES1;A0A286YEY4;A0A4W9A917 IGHG2_HUMAN;IGHG3_HUMAN IGHG2;IGHG3 Immunoglobulin heavy constant gamma 2 238323 36617.1 10856.5 351910 +P01876 P01876;A0A286YEY1 IGHA1_HUMAN IGHA1 Immunoglobulin heavy constant alpha 1 421256 100171 49472.4 45559.3 59650.4 43639.6 +P01889 P01889;A0A1W2PP29;Q5SS57;A0A140T9D4;A0A140TA21;A0A8Q3SIV3 HLAB_HUMAN HLA-B HLA class I histocompatibility antigen, B alpha chain 148484 398238 367111 299220 451994 369056 476328 418578 1.32526e+06 340143 351101 253640 +P01966 P01966;P69905;A0A2R8Y7C0;G3V1N2 SWISS-PROT:P01966 SWISS-PROT:P01966 790096 405013 380724 467145 726005 654869 611526 497104 829661 682078 563915 608810 707025 692615 544431 649959 456114 621496 415799 485065 +P02462 P02462;P29400;A0A3B3ISV3 CO4A1_HUMAN COL4A1 Collagen alpha-1(IV) chain 473133 247533 283231 313135 624372 535198 627997 447589 567919 390495 626664 607569 521343 538121 695352 530798 405237 425814 431842 539585 +P02533 P08779;P13645;P02533;Q148H6;Q3ZAW8;A2A4G1;P02535-1;Q9Z2K1;Q7Z3Y8;Q7Z3Y9;Q7Z3Z0;Q7Z3Y7;A0A1B0GVI3 SWISS-PROT:P02533 SWISS-PROT:P02533 6.52322e+06 1.80742e+06 2.61332e+06 1.71167e+06 4.00374e+06 4.58689e+06 2.39006e+06 1.48306e+06 2.51643e+06 1.9971e+06 2.14965e+06 1.40796e+07 3.38624e+06 1.25268e+07 1.93593e+06 6.20424e+06 3.79586e+06 2.90282e+06 1.27678e+06 7.81561e+06 +P02538 P02538;P48668;P04259;P50446 SWISS-PROT:P02538 SWISS-PROT:P02538 1.36784e+06 231836 138028 415475 312904 1.09372e+06 191424 305086 485457 233768 145320 1.4896e+06 683281 5.54607e+06 264009 687451 1.16757e+06 464864 137924 1.48609e+06 +P02545 P02545;A0A6Q8PFJ0;Q3BDU5;Q5TCI8 LMNA_HUMAN LMNA Prelamin-A/C 1.62254e+06 1.8328e+06 1.54335e+06 2.75677e+06 1.5857e+06 2.3275e+06 1.54421e+06 2.28107e+06 1.5727e+06 2.47666e+06 1.52503e+06 2.64389e+06 1.4705e+06 2.2634e+06 1.54755e+06 2.0882e+06 1.37324e+06 1.89036e+06 1.43903e+06 1.50729e+06 +P02662 P02662 SWISS-PROT:P02662 SWISS-PROT:P02662 111676 9073.49 +P02666 P02666 SWISS-PROT:P02666 SWISS-PROT:P02666 374080 158770 451862 48820.5 101587 49905.7 88513.6 56515.2 54049.4 47843.9 111275 61963.6 92045.8 197027 154771 87453.7 96207.9 70025.2 475198 +P02750 P02750 A2GL_HUMAN LRG1 Leucine-rich alpha-2-glycoprotein 562171 1.40872e+06 834370 2.12745e+06 768594 2.05391e+06 835783 1.56453e+06 989976 2.43382e+06 877538 2.04104e+06 898499 2.43475e+06 985775 2.78623e+06 966041 2.78953e+06 566725 2.84029e+06 +P02751 P02751;H0Y7Z1 FINC_HUMAN FN1 Fibronectin 53625.6 73252.6 161099 70814.8 161518 74611.6 143648 60734.9 155573 54683 193080 90088 191925 101668 127663 60174.3 180913 29803.6 152571 +P02754 P02754 SWISS-PROT:P02754 SWISS-PROT:P02754 52008.2 1.47019e+06 9484.68 +P02768 P02768-1;P02768;A0A087WWT3;A0A0C4DGB6;B7WNR0;C9JKR2;D6RHD5;H0YA55 ALBU_HUMAN ALB Albumin 385199 66014.2 147929 46294.3 59328.4 89825.6 465851 +P02769 P02769 SWISS-PROT:P02769 SWISS-PROT:P02769 6.50325e+06 1.37199e+07 4.86907e+06 5.66217e+06 5.45592e+06 6.11542e+06 4.57459e+06 2.96445e+06 8.55899e+06 5.8867e+06 5.03045e+06 4.36514e+06 1.40959e+07 6.17601e+06 6.38712e+06 5.84581e+06 1.78387e+07 6.81613e+06 4.24287e+06 8.37574e+06 +P02777 P02777 SWISS-PROT:P02777 SWISS-PROT:P02777 50637.3 52802.4 113603 160140 88379 66056.2 85486.8 66378.3 82892.7 59472.2 76010.8 65244.2 98524.2 76566.8 83874.3 65781.6 50544.2 77951.5 +P02786 P02786;G3V0E5;A0A8V8TLK7;A0A8V8TLM6;A0A8V8TLN0;A0A8V8TM41;A0A8V8TM46;A0A8V8TN37 TFR1_HUMAN TFRC Transferrin receptor protein 1 3.01281e+06 1.35884e+06 3.70263e+06 1.74698e+06 3.02542e+06 1.60835e+06 3.9226e+06 1.26413e+06 3.47102e+06 2.06761e+06 3.69062e+06 1.62769e+06 3.29667e+06 1.9105e+06 3.77798e+06 2.07651e+06 4.89022e+06 2.335e+06 2.70163e+06 2.50209e+06 +P02788 P02788;E7ER44;E7EQB2 TRFL_HUMAN LTF Lactotransferrin 19553.9 +P02792 P02792 FRIL_HUMAN FTL Ferritin light chain 1.4484e+06 215758 1.97823e+06 304875 1.80786e+06 286337 2.38883e+06 311328 1.96287e+06 409847 1.83272e+06 235008 1.24891e+06 190145 1.89345e+06 241361 1.51749e+06 120845 768484 136827 +P02794 P02794;G3V192;G3V1D1 FRIH_HUMAN FTH1 Ferritin heavy chain 1.28756e+06 184275 836058 218410 1.17004e+06 308772 1.39659e+06 184394 1.03249e+06 250639 1.1344e+06 179667 746153 153969 820869 265200 692004 56791.5 436197 119317 +P02795;P04732;P13640;P80297;Q8N339 P67983;P02795;P04732;P13640;P80297;Q8N339 MT1E_HUMAN;MT1G_HUMAN;MT1M_HUMAN;MT1X_HUMAN;MT2_HUMAN MT1E;MT1G;MT1M;MT1X;MT2A Metallothionein-2 135338 116946 80165.7 113202 143465 49972.5 155343 57566.1 141723 91600.4 132698 67385.2 62996.6 75309.1 190753 93447.1 128976 91902.5 89675.7 +P03950 P03950 ANGI_HUMAN ANG Angiogenin 23833.3 65470.1 17835.2 23667 52685.3 36489.8 23863.2 51181.1 73295 50835.8 57636.7 100818 69275.1 52657.9 82430.2 +P03973 P03973 SLPI_HUMAN SLPI Antileukoproteinase 831011 622934 713088 1.27148e+06 1.25305e+06 636893 1.19587e+06 802691 998817 880441 1.24659e+06 1.05535e+06 941973 997627 1.61299e+06 1.29799e+06 571358 1.23612e+06 1.01468e+06 724947 +P04003 P04003 C4BPA_HUMAN C4BPA C4b-binding protein alpha chain 153419 2.62686e+06 180473 5.22511e+06 193076 3.94122e+06 229900 4.41999e+06 131535 4.22006e+06 238717 7.02584e+06 216285 5.35818e+06 305679 4.78293e+06 5.57067e+06 124438 5.50497e+06 +P04004 P04004;H0YJW9 VTNC_HUMAN VTN Vitronectin 343943 215850 162147 323358 268870 348246 235049 308323 239517 249541 213826 442649 189352 306961 275427 297513 149303 303651 180699 238108 +P04040 P04040 CATA_HUMAN CAT Catalase 195180 135049 183613 127185 152677 143446 143408 103772 66529.4 146454 156359 301294 137773 173151 108758 287350 83639.6 190289 71099.3 188842 +P04062 P04062;A0A0G2JLB3;A0A0G2JNZ0;A0A0G2JNZ5 GBA1_HUMAN GBA1 Lysosomal acid glucosylceramidase 839768 350403 572874 648844 1.04403e+06 589899 747254 682548 1.48981e+06 687865 1.00705e+06 841288 1.2309e+06 777053 430835 534649 875376 794691 568981 504995 +P04066 P04066 FUCO_HUMAN FUCA1 Tissue alpha-L-fucosidase 116334 106220 240018 212306 190643 160024 164747 136129 224962 174504 186536 177245 236624 208301 288865 206080 323706 291348 158320 289808 +P04075 P04075;H3BPS8;H3BQN4;H3BR04;H3BU78;J3KPS3 ALDOA_HUMAN ALDOA Fructose-bisphosphate aldolase A 1.87514e+06 4.30559e+06 1.64533e+06 4.40074e+06 2.33457e+06 4.24678e+06 2.16773e+06 3.5576e+06 2.30066e+06 4.55227e+06 2.07086e+06 3.52694e+06 2.31782e+06 4.09448e+06 2.39155e+06 4.44653e+06 1.91599e+06 4.44383e+06 1.81068e+06 4.31564e+06 +P04080 P04080 CYTB_HUMAN CSTB Cystatin-B 427926 508410 460100 485301 581394 353033 682542 374714 515233 442745 613958 227606 712212 476261 1.05318e+06 505753 377392 670574 623419 566216 +P04083 P04083 ANXA1_HUMAN ANXA1 Annexin A1 146946 98152.8 193692 335632 94017.4 104177 140913 248398 139858 138802 152711 309554 192731 200887 157766 297756 109831 237956 153536 241798 +P04085 P04085;H7BYW6 PDGFA_HUMAN PDGFA Platelet-derived growth factor subunit A 242326 186339 415397 350061 349456 203070 544887 248187 299687 210241 527816 290627 287714 218622 706679 407086 264256 261665 351476 346474 +P04114 P04114 APOB_HUMAN APOB Apolipoprotein B-100 186687 217858 126813 157989 26935.1 170070 162571 31729 79272.8 68745.6 99727.5 94089.1 +P04179 P04179 SODM_HUMAN SOD2 Superoxide dismutase [Mn], mitochondrial 131397 83935.7 315406 153060 110911 60669.6 181041 50404.2 182912 106249 133674 77816.3 156615 85195.1 94137.1 71623.6 252131 76799.9 98415.7 93396.2 +P04259 P48668;P04259 SWISS-PROT:P04259 SWISS-PROT:P04259 322563 60233.8 117372 70639.2 525839 41374.7 73415.9 202788 56279.2 407307 393128 7.75706e+06 38341.2 370190 806689 120513 1.1738e+06 +P04264 P04264 SWISS-PROT:P04264 SWISS-PROT:P04264 1.95624e+07 5.61805e+06 1.03866e+07 5.53545e+06 1.38837e+07 1.07555e+07 9.82715e+06 3.75276e+06 8.68771e+06 7.92199e+06 1.01664e+07 4.14181e+07 1.10602e+07 1.22301e+07 5.42557e+06 2.77769e+07 1.04237e+07 9.16055e+06 3.88413e+06 1.71738e+07 +P04406 P04406;E7EUT5 G3P_HUMAN GAPDH Glyceraldehyde-3-phosphate dehydrogenase 1.86095e+07 1.27707e+07 1.86979e+07 1.50685e+07 1.87455e+07 1.84337e+07 1.87816e+07 1.7661e+07 1.9212e+07 1.95287e+07 2.1244e+07 2.01706e+07 2.04863e+07 2.05933e+07 2.53998e+07 2.15138e+07 1.86664e+07 2.06744e+07 2.12455e+07 2.1171e+07 +P04439 P04439;A0A0G2JPD3;A0A140T8Y4;A0A140T913;A0A140T9I0;A0A140T9X5;A0A1W2PQS8;A0A1W2PR61;A0A1W2PRT9;A0A1W2PS24;B0UXQ0;Q53Z42;Q5RJ27;Q5SPM2;Q5SRN5;Q9GJ45;A0A140T8W8;A0A140T933;A0A140T941;A0A140T955;A0A140T975;A0A140T9Z6;A0A1W2PPF8;A0A1W2PPQ2;A0A1W2PS39;Q5SRN7 HLAA_HUMAN HLA-A HLA class I histocompatibility antigen, A alpha chain 542344 865818 774745 1.63703e+06 736569 1.36639e+06 719067 1.26607e+06 859623 1.68924e+06 770017 1.59776e+06 824521 1.80918e+06 765724 1.34525e+06 654497 1.44036e+06 496248 1.42011e+06 +P04792 P04792;A0A6Q8PGK1;A0A6Q8PF43;A0A6Q8PFE7;A0A6Q8PFK8;A0A6Q8PGY2;A0A6Q8PH65;A0A6Q8PHA6;A0A6Q8PHJ6 HSPB1_HUMAN HSPB1 Heat shock protein beta-1 905646 1.13867e+06 657303 1.21036e+06 1.14209e+06 1.7683e+06 974605 1.39755e+06 987844 1.59149e+06 917870 1.17951e+06 1.10887e+06 1.71789e+06 1.34226e+06 1.15964e+06 931196 1.06588e+06 970607 791660 +P04818 P04818 TYSY_HUMAN TYMS Thymidylate synthase 143942 167131 139192 204668 183431 144465 152571 135756 150569 134531 132844 186041 198292 165869 163336 92122.5 174799 172827 60368.3 +P05023 P05023;P13637;P20648;P50993;P54707;Q13733;A0A0A0MT26;A0A2R8YEY8;B1AKY9;H0Y7C1;M0R116 AT1A1_HUMAN ATP1A1 Sodium/potassium-transporting ATPase subunit alpha-1 318940 89141.2 432993 177368 413622 171435 463434 190405 665023 196178 595982 219591 597915 261728 493461 237675 463402 277370 389218 208540 +P05026 P05026;A0A8I5KNT2;A0A8I5KX89;B7Z9S8 AT1B1_HUMAN ATP1B1 Sodium/potassium-transporting ATPase subunit beta-1 77158.6 39237.2 88617.3 64440.7 112321 38559.5 140017 56649.8 92467.4 48685.4 127111 86516.1 163657 85340.8 102040 33737.9 54602.5 91289.1 59187.3 +P05067 E9PG40;P05067;Q06481;A0A0A0MRG2;H7C0V9 A4_HUMAN APP Amyloid-beta precursor protein 501326 154843 555037 307655 588785 276410 643575 319414 749298 298904 688989 390073 585682 322328 743488 304329 464093 419243 439335 200522 +P05089 P05089;A0A5F9ZGN6;A0A5F9ZH78 ARGI1_HUMAN ARG1 Arginase-1 302204 107176 45224.7 172549 116800 107216 28126.4 67073.6 65796 123787 400350 145714 133759 70868.5 424975 79707.8 130155 43923.8 309012 +P05090 P05090;C9JF17;C9JX71 APOD_HUMAN APOD Apolipoprotein D 272915 +P05109 P05109 S10A8_HUMAN S100A8 Protein S100-A8 58038.4 480078 43296 70848.3 66714 133818 52666.9 +P05114 P05114;A0A494C035;F2Z2W6;F2Z2Y5;H7BXJ5 HMGN1_HUMAN HMGN1 Non-histone chromosomal protein HMG-14 87818.1 59052.2 82886.6 98617.1 43864.5 28778 64769.2 39313.4 36656 33293.4 37278.8 48054.4 150765 54896.8 66754.1 41607.5 29965.2 +P05121 P05121 PAI1_HUMAN SERPINE1 Plasminogen activator inhibitor 1 195328 217876 216032 189775 198656 193896 245477 263720 263943 276710 +P05155 P05155;A0A087WUD9;A0A7I2V2D2;A0A7I2V2X2;A0A7I2V4I9;A0A7I2V5R3;E9PGN7;H9KV48 IC1_HUMAN SERPING1 Plasma protease C1 inhibitor 292837 476369 39414.8 344994 36981.7 448654 15652.8 468942 39833.6 471079 54314.8 402819 37567.9 444504 53770.3 607998 44267.9 271440 +P05156 Q32PI4;P05156;A0A2R8Y3M9;E7ETH0;G3XAM2;D6R9Z8 CFAI_HUMAN CFI Complement factor I 48542 60122.6 55079 63906.2 50671.8 50613.1 52357.6 72580.5 +P05187 P05187;P10696 PPB1_HUMAN ALPP Alkaline phosphatase, placental type 347520 25790.7 477361 492776 23933.2 531091 705980 637851 33398.9 643721 407146 585761 23686 536454 404915 +P05198 P05198;G3V4T5;H0YJS4 IF2A_HUMAN EIF2S1 Eukaryotic translation initiation factor 2 subunit 1 612081 616442 346282 624311 719396 692493 466321 695148 676364 576772 569418 611672 669705 607779 353687 649848 527792 621207 601088 341699 +P05362 P05362;E7ESS4 ICAM1_HUMAN ICAM1 Intercellular adhesion molecule 1 266514 202109 556555 331904 388515 250548 461718 231998 483058 353653 461599 251750 459116 326609 547025 322240 586381 432921 306382 437332 +P05386 P05386 RLA1_HUMAN RPLP1 60S acidic ribosomal protein P1 721229 651561 598218 768147 648179 880809 673142 662577 520240 726628 818401 747951 142030 758665 2.83141e+06 53238.8 288206 685134 484804 +P05387 P05387 RLA2_HUMAN RPLP2 60S acidic ribosomal protein P2 1.67846e+06 1.28322e+06 1.18812e+06 1.78844e+06 806097 1.0146e+06 763210 945955 865937 1.1812e+06 964168 1.38563e+06 933594 972291 482511 945917 1.50891e+06 889085 1.29987e+06 889011 +P05388 P05388;Q8NHW5;F8VWS0;F8VPE8;F8VQY6;F8VRK7;F8VU65;F8VW21;F8VWV4;G3V210 RLA0_HUMAN RPLP0 60S acidic ribosomal protein P0 2.22449e+06 2.27424e+06 1.95618e+06 2.83236e+06 1.77235e+06 1.79903e+06 1.84598e+06 2.10576e+06 1.77211e+06 1.58597e+06 1.93731e+06 2.1944e+06 1.54264e+06 1.80931e+06 1.61305e+06 1.67504e+06 1.31775e+06 1.74668e+06 1.96194e+06 1.29525e+06 +P05455 P05455;E7ERC4;E9PFL9;E9PGX9 LA_HUMAN SSB Lupus La protein 167760 130739 131078 130351 157868 144049 167363 132346 120240 116457 135451 146582 114608 131940 144220 146568 100221 133862 197443 +P05556 P05556;A0A7I2V2F9;A0A7I2V348;C9JPK5;E7EUI6;H7C4K3;A0A7I2V5T3;A0A7I2V5Z8 ITB1_HUMAN ITGB1 Integrin beta-1 80890.8 63989.5 149798 127454 95693.3 94134.6 121666 89229.5 145294 112400 142320 99421.6 129459 113324 132617 109313 116252 152201 78757.6 110601 +P05783 P05784;P05783;F8VZY9 K1C18_HUMAN KRT18 Keratin, type I cytoskeletal 18 2.2454e+06 6.83155e+06 3.62629e+06 9.70545e+06 1.49261e+07 3.40924e+07 6.95478e+06 1.78686e+07 2.5864e+07 4.05784e+07 5.08116e+06 2.06983e+07 2.49935e+07 4.14106e+07 7.94973e+06 1.9006e+07 1.79478e+07 3.82316e+07 4.44815e+06 1.3496e+07 +P05784 P05784 SWISS-PROT:P05784 SWISS-PROT:P05784 20557.3 84702.5 16287.8 36520.2 101280 34466.5 45444.1 114270 19106.1 14111.7 9450.23 83291.8 +P05787 P05787;Q9H552 SWISS-PROT:P05787 SWISS-PROT:P05787 177577 327585 167223 493380 753595 1.99873e+06 368236 888971 1.3121e+06 2.68068e+06 234535 1.14367e+06 1.42354e+06 2.49921e+06 561635 1.10311e+06 1.31738e+06 2.18673e+06 267840 863804 +P06132 P06132;A0A494C085;A0A494C0Q8;A0A1B0GVN9;A0A494C0S8;H0Y5R6 DCUP_HUMAN UROD Uroporphyrinogen decarboxylase 97305.5 115326 101154 89853 139565 140041 133260 87593 139871 111579 118310 63127.4 133201 83573.4 146546 100453 140820 129299 117744 129365 +P06280 P06280;A0A3B3IRU3;A0A669KAY7;A0A669KB83;A0A6Q8PFA9;A0A6Q8PGG0;A0A6Q8PHD1;A0A6Q8PHM8;V9GYN5 AGAL_HUMAN GLA Alpha-galactosidase A 408943 424875 278438 738011 545233 759737 429590 829111 549264 788898 559812 1.08661e+06 588104 945612 558603 706752 466967 874776 294708 696203 +P06396 Q3SX14;P06396;A0A0A0MS51;A0A0A0MT01;A0A8V8TND7 GELS_HUMAN GSN Gelsolin 86816.6 40110.8 69088.5 92431.9 95473.2 73284.3 97762.5 95848.4 101847 86245.6 87249.6 103164 97991.9 102790 82012.6 76706.1 70088.2 70987.9 50755.7 55392.6 +P06454 P06454;B8ZZQ6 PTMA_HUMAN PTMA Prothymosin alpha 564288 295558 738750 303130 415623 189681 577047 248491 586998 396351 714782 280185 154702 287567 281772 402854 889697 447466 560763 97101 +P06493 P06493;A0A024QZP7;E5RIU6 CDK1_HUMAN CDK1 Cyclin-dependent kinase 1 525657 619389 153179 390557 509742 758216 425688 673977 329698 625005 450750 495799 423856 608822 477855 580809 328380 574845 678739 283146 +P06576 P06576;H0YH81;F8W0P7 ATPB_HUMAN ATP5F1B ATP synthase subunit beta, mitochondrial 682481 192718 302207 378111 355050 157064 283741 178302 281192 224088 272158 227973 297880 249194 271633 291846 252370 268427 188498 +P06702 P06702 S10A9_HUMAN S100A9 Protein S100-A9 86831.8 24566.1 49713.5 12156.2 +P06703 P06703;R4GN98 S10A6_HUMAN S100A6 Protein S100-A6 3.35597e+06 3.23164e+06 5.45202e+06 4.80948e+06 1.97233e+06 2.93935e+06 2.17827e+06 1.29896e+06 2.04056e+06 4.01681e+06 3.30865e+06 3.88367e+06 2.17427e+06 2.71544e+06 2.63093e+06 3.27076e+06 6.03089e+06 3.4399e+06 2.47278e+06 2.3413e+06 +P06730 P06730;D6RBW1 IF4E_HUMAN EIF4E Eukaryotic translation initiation factor 4E 128117 62934.8 106924 118189 95644.1 112209 92004.3 98902.3 91748.4 84470.2 104314 121597 124125 70331.2 113864 108548 +P06733 P06733;A0A2R8Y6G6 ENOA_HUMAN ENO1 Alpha-enolase 7.3879e+06 8.17965e+06 6.99491e+06 8.07758e+06 7.08701e+06 6.11473e+06 8.78177e+06 5.59603e+06 6.83345e+06 7.24109e+06 7.8465e+06 6.80371e+06 7.3707e+06 7.25222e+06 8.43051e+06 1.0388e+07 8.03927e+06 8.62732e+06 8.09254e+06 1.09577e+07 +P06737 P06737;E9PK47 PYGL_HUMAN PYGL Glycogen phosphorylase, liver form 398388 262881 306557 311677 274080 195672 293033 182229 268913 200780 343229 165132 222407 175853 265698 245724 481366 255713 396778 270281 +P06744 P06744;A0A2U3TZU2;K7EQ48;A0A0J9YXP8;A0A0J9YYH3;A0A0J9YX90;A0A2R8Y6C7;K7EP41;K7EPY4;K7ERC6 G6PI_HUMAN GPI Glucose-6-phosphate isomerase 3.89529e+06 4.34451e+06 3.47349e+06 3.59347e+06 3.29325e+06 3.13306e+06 3.86696e+06 2.5083e+06 3.50985e+06 3.66069e+06 3.20415e+06 2.3624e+06 3.55089e+06 3.19023e+06 3.91214e+06 3.79511e+06 4.32595e+06 3.6509e+06 3.18746e+06 3.83613e+06 +P06748 P06748;A0A7I2V5J8;A0A7I2V5S2;A0A7I2YQC0;E5RI98;A0A7I2V4G8 NPM_HUMAN NPM1 Nucleophosmin 2.47135e+06 2.23573e+06 2.37625e+06 2.75996e+06 2.08834e+06 2.52993e+06 2.30291e+06 2.64559e+06 2.29527e+06 2.4019e+06 2.56864e+06 2.37028e+06 1.95595e+06 2.0623e+06 1.64482e+06 2.26877e+06 1.42029e+06 2.22585e+06 2.18917e+06 1.44262e+06 +P06753 P06753;A0A2R2Y2Q3;J3KN67;A0A087WWU8;A0A494C0P6 TPM3_HUMAN TPM3 Tropomyosin alpha-3 chain 43079.9 42090 24533.7 35411.9 37300.1 7711.37 46157.6 27230.4 60493 56540 63916.6 109987 76730.7 19081.9 +P06865 P06865;H3BP20;H3BS10;A0A804HIC8;A0A804HIQ5;A0A804HIU3;A0A804HKX5;A0A804HLJ5;H3BTD4;H3BU85 HEXA_HUMAN HEXA Beta-hexosaminidase subunit alpha 2.07815e+06 1.26808e+06 1.78609e+06 2.40423e+06 1.73561e+06 1.52148e+06 1.73863e+06 1.39238e+06 2.01314e+06 2.32316e+06 2.15162e+06 2.30486e+06 2.12282e+06 2.2201e+06 1.34474e+06 1.98785e+06 3.68512e+06 2.73369e+06 1.34301e+06 2.7368e+06 +P06868 P06868;P00747 SWISS-PROT:P06868 SWISS-PROT:P06868 183288 142555 215646 214362 198255 177626 220737 156814 193690 167730 228072 222575 181205 188934 218183 159927 213227 178169 149391 105723 +P07093 P07093 GDN_HUMAN SERPINE2 Glia-derived nexin 2.88087e+06 162694 2.10768e+06 235726 2.83065e+06 200076 2.78283e+06 199064 3.49314e+06 222037 3.08474e+06 235149 3.05786e+06 182791 2.49825e+06 167237 2.72184e+06 217782 2.49283e+06 151207 +P07108 P07108;B8ZWD1 ACBP_HUMAN DBI Acyl-CoA-binding protein 733163 1.24769e+06 637267 927841 703684 731362 870124 610805 829115 875405 805125 543117 904758 811501 698122 908316 719735 1.05782e+06 937001 1.02001e+06 +P07195 P07195;A0A3B3IS95;A0A5F9ZHM4 LDHB_HUMAN LDHB L-lactate dehydrogenase B chain 1.0114e+07 8.76278e+06 1.07294e+07 7.70675e+06 8.72772e+06 7.16812e+06 9.87527e+06 5.08244e+06 9.77556e+06 8.23253e+06 8.8029e+06 5.10214e+06 8.72258e+06 7.39766e+06 1.07708e+07 7.75835e+06 1.24386e+07 9.35702e+06 8.54375e+06 8.15988e+06 +P07225 P07225;A0A0S2Z4L3;A0A3B3IRK9;A0A3B3ISJ1;G5E9F8;A0A3B3IUA6 PROS_HUMAN PROS1 Vitamin K-dependent protein S 206304 207908 163980 186372 213978 175202 180201 214687 238913 154381 185045 213168 169206 151834 181261 +P07237 P07237;A0A7P0T8C0;A0A7P0T8J3;A0A7P0T8X1;A0A7P0T9K9;A0A7P0TA35;A0A7P0TA71;A0A7P0TA97;A0A7P0TB26;B3KTQ9;H0Y3Z3;H7BZ94;I3L0S0;I3L3P5;I3L4M2 PDIA1_HUMAN P4HB Protein disulfide-isomerase 1.65984e+06 1.07903e+06 1.93282e+06 1.30855e+06 1.49156e+06 1.3257e+06 1.57704e+06 1.15741e+06 1.51501e+06 1.7175e+06 1.65728e+06 1.49523e+06 1.31544e+06 1.44826e+06 1.31239e+06 1.55732e+06 1.64758e+06 1.76381e+06 1.18833e+06 1.3288e+06 +P07305 P07305 H10_HUMAN H1-0 Histone H1.0 218973 +P07339 P07339;A0A1B0GV23;A0A1B0GVD5;A0A1B0GVP3;A0A1B0GW44;A0A1B0GWE8;C9JH19;A0A1B0GU03;A0A1B0GU92;F8WD96 CATD_HUMAN CTSD Cathepsin D 2.48579e+06 1.80027e+06 3.71803e+06 3.00945e+06 3.36627e+06 2.68522e+06 3.25765e+06 2.17588e+06 4.18229e+06 3.52417e+06 3.20205e+06 2.72986e+06 4.15013e+06 3.21042e+06 2.55702e+06 2.69972e+06 4.28524e+06 2.82215e+06 2.08116e+06 2.79457e+06 +P07355 A6NMY6;P07355;H0YL33;H0YM50;H0YMD0;H0YMU9;H0YMW4;H0YN28;H0YN42;H0YNP5 ANXA2_HUMAN ANXA2 Annexin A2 521336 380940 516551 1.42568e+06 373895 459631 534887 1.04946e+06 417864 543121 544245 1.34917e+06 460183 694780 500416 1.26601e+06 342112 860333 416322 1.31235e+06 +P07384 P07384;E9PIA9;E9PJA6;E9PJJ3;E9PLC9;E9PLX0;E9PMC6;E9PQB3;E9PRM1;E9PSA6 CAN1_HUMAN CAPN1 Calpain-1 catalytic subunit 104069 70064.7 63154.3 115039 107789 110176 87540 83988 91077.8 94653.5 115613 100075 92740.4 116222 79689.7 66752.4 153527 +P07437 P04350;P07437;P68371;Q13509;Q13885;Q9BVA1;A0A0B4J269;Q5JP53;Q5ST81 TBB5_HUMAN TUBB Tubulin beta chain 5.52396e+06 5.27962e+06 2.28757e+06 3.76469e+06 5.27646e+06 5.95137e+06 3.86355e+06 5.68464e+06 4.4261e+06 5.48914e+06 4.34359e+06 4.70454e+06 5.29774e+06 5.8122e+06 3.4724e+06 5.12729e+06 4.01722e+06 5.10232e+06 6.42054e+06 3.82873e+06 +P07602 P07602;A0A0J9YXB8 SAP_HUMAN PSAP Prosaposin 5.56274e+06 3.56705e+06 6.59161e+06 6.56899e+06 5.4189e+06 4.47705e+06 5.45936e+06 4.03468e+06 6.76389e+06 6.25586e+06 6.38984e+06 7.10382e+06 6.57765e+06 6.02895e+06 4.86897e+06 4.98241e+06 8.04373e+06 5.50842e+06 4.84015e+06 6.59895e+06 +P07686 P07686 HEXB_HUMAN HEXB Beta-hexosaminidase subunit beta 2.60335e+06 2.48512e+06 3.56832e+06 4.05993e+06 2.96531e+06 3.21481e+06 3.62957e+06 2.72945e+06 3.49176e+06 3.91029e+06 3.29724e+06 3.45904e+06 3.5512e+06 3.7052e+06 3.19604e+06 4.26596e+06 3.89809e+06 4.76888e+06 2.05119e+06 4.73311e+06 +P07711 P07711;Q5T8F0;A0A7I2V2H7;A0A7I2V5M3;A0A7I2YQA2;A0A7I2YQB5 CATL1_HUMAN CTSL Procathepsin L 4.40451e+06 3.99319e+06 4.67415e+06 5.63859e+06 5.32263e+06 5.92796e+06 5.40154e+06 4.06772e+06 4.88327e+06 7.36967e+06 5.15342e+06 7.2044e+06 4.08001e+06 6.80945e+06 7.74127e+06 7.46164e+06 5.73178e+06 7.2335e+06 3.882e+06 7.90755e+06 +P07737 P02584;P07737;K7EJ44 PROF1_HUMAN PFN1 Profilin-1 1.64243e+06 1.88606e+06 1.30982e+06 1.61048e+06 1.42274e+06 1.02126e+06 1.22589e+06 933189 1.27109e+06 1.59752e+06 1.25748e+06 864936 1.56403e+06 1.07373e+06 1.15313e+06 1.41028e+06 2.71321e+06 1.26594e+06 1.8692e+06 1.24414e+06 +P07741 P07741;H3BQB1;H3BQF1;H3BQZ9;H3BSW3 APT_HUMAN APRT Adenine phosphoribosyltransferase 187202 265469 45716.4 112302 183092 395130 171443 363636 104258 248430 172808 214064 204669 259608 144163 257835 91275.7 167444 353409 132839 +P07814 P07814 SYEP_HUMAN EPRS1 Bifunctional glutamate/proline--tRNA ligase 408120 608028 308690 563109 375644 644557 306227 635836 328814 589331 331154 597734 363323 570760 354694 585180 292278 507560 457463 442241 +P07858 P07858;A0A7I2V2J1;A0A7I2V302;A0A7I2V440;A0A7I2V4S6;A0A7I2V4Z9;A0A7I2YQK9;A0A7P0NGZ6;E9PCB3;E9PHZ5;E9PKQ7;E9PLY3;E9PNL5;E9PQM1;E9PR21;E9PSG5;E9PS78 CATB_HUMAN CTSB Cathepsin B 688133 533533 852033 822897 731301 691862 796136 517547 856282 1.08921e+06 766587 771638 776592 1.00025e+06 935052 972130 1.28373e+06 1.03985e+06 568067 992541 +P07900 P07900 HS90A_HUMAN HSP90AA1 Heat shock protein HSP 90-alpha 4.10004e+06 2.76652e+06 1.6358e+06 2.40529e+06 3.21339e+06 2.57906e+06 3.02163e+06 2.54345e+06 2.62216e+06 2.3232e+06 3.35063e+06 2.6922e+06 2.32646e+06 2.49851e+06 2.83128e+06 2.50133e+06 2.16687e+06 1.92905e+06 3.91556e+06 1.68449e+06 +P07910 P07910;G3V2Q1;B4DY08;G3V2D6;G3V4C1;G3V4W0;G3V555;G3V575;G3V576;G3V5V7 HNRPC_HUMAN HNRNPC Heterogeneous nuclear ribonucleoproteins C1/C2 3.64378e+06 3.22842e+06 5.24301e+06 4.81373e+06 3.72703e+06 2.93838e+06 4.21573e+06 3.73825e+06 3.36632e+06 2.3785e+06 4.46697e+06 4.10627e+06 2.31428e+06 3.09328e+06 3.14776e+06 3.10644e+06 2.01051e+06 2.88389e+06 3.2939e+06 2.64862e+06 +P07942 P07942;A0A7I2V2J0;A0A7I2V2R2;A0A7I2V2T9;A0A7I2V4J9;A0A7I2V5R0;A0A7I2V5X5;A0A7I2V682;C9J296;E7EPA6;G3XAI2;A0A7I2V3I5;A0A7I2V3J7;A0A7I2V407;A0A7I2V5T9 LAMB1_HUMAN LAMB1 Laminin subunit beta-1 4.2032e+06 2.34838e+06 4.66122e+06 4.23892e+06 4.35397e+06 4.11155e+06 5.36346e+06 3.77756e+06 5.17446e+06 4.30308e+06 5.93342e+06 5.56475e+06 4.46747e+06 4.93576e+06 5.02087e+06 5.68235e+06 4.7164e+06 4.58399e+06 3.87407e+06 5.5119e+06 +P07947 P07947;J3QRU1 YES_HUMAN YES1 Tyrosine-protein kinase Yes 63537.9 41879.2 31399.9 93628 29501.8 129984 96391.1 91267 69067.1 29374.8 82549.6 +P07951 Q3SX28;P07951;A7XZE4;Q5TCU3 TPM2_HUMAN TPM2 Tropomyosin beta chain 22933.2 22538.4 35954.1 7797.08 17111.8 38528.3 38106 27395.3 24077.5 20302.1 14852.2 +P07954 P07954;A0A804HI24 FUMH_HUMAN FH Fumarate hydratase, mitochondrial 826609 759403 1.07086e+06 716813 814701 614509 818226 420382 902989 735095 745307 500795 745561 679521 1.00738e+06 673359 1.06442e+06 627102 659011 582649 +P07996 Q28194;P07996 TSP1_HUMAN THBS1 Thrombospondin-1 2.49102e+06 1.64367e+06 1.79933e+06 3.37361e+06 2.50477e+06 2.46846e+06 2.14709e+06 2.50192e+06 2.68454e+06 2.3071e+06 2.26573e+06 3.13057e+06 2.75835e+06 2.77078e+06 2.21052e+06 2.40817e+06 2.35529e+06 2.81238e+06 1.56973e+06 2.54183e+06 +P08174 P08174;A0A2R8YDY3;A0A2R8YEC5;A0A8Q3SI94;A0A8Q3WKT6;B1AP13;B1AP15;H3BLV0 DAF_HUMAN CD55 Complement decay-accelerating factor 1.98717e+06 1.37136e+06 2.90906e+06 2.27233e+06 2.03633e+06 1.86237e+06 2.58358e+06 1.4959e+06 2.41974e+06 2.50417e+06 2.459e+06 2.35841e+06 2.28139e+06 2.23665e+06 2.51838e+06 2.41583e+06 3.48663e+06 2.39125e+06 1.93455e+06 2.64945e+06 +P08195 P08195;F5GZS6;J3KPF3;A0A7P0T8F1;A0A7P0T8U7;A0A7P0T9F7;A0A7P0TAN7;A0A7P0TAS6;A0A7P0TBF8;A0A7P0Z4A0;A0A7P0Z4P5;F5GZI0;F5H056;H0YFX4 4F2_HUMAN SLC3A2 4F2 cell-surface antigen heavy chain 883233 406314 1.23952e+06 712072 1.11201e+06 595936 1.24817e+06 527004 1.59373e+06 829988 1.52908e+06 791940 1.45695e+06 796597 1.25612e+06 740966 1.63196e+06 952342 1.07678e+06 880437 +P08236 P08236 BGLR_HUMAN GUSB Beta-glucuronidase 135229 358529 73363.7 483370 150671 384261 145104 167444 113220 457331 144819 392364 238191 386353 146243 488777 137768 489399 100598 627802 +P08237 P08237;A0A2R8Y891;F8VP00;F8VSF7;F8VTQ3;F8VUB8;F8VVE3;F8VW30;F8VX13;F8VYK8;F8VZI0;F8VZQ1 PFKAM_HUMAN PFKM ATP-dependent 6-phosphofructokinase, muscle type 52307 51357.2 27012.1 52213.9 61943.9 63297 72777 75327.9 52866.6 49988.2 54127.2 61126.3 79505.1 53057.4 81584.1 69989.1 73893.4 133283 +P08238 P08238 HS90B_HUMAN HSP90AB1 Heat shock protein HSP 90-beta 1.21698e+07 9.618e+06 5.29469e+06 8.56374e+06 9.08354e+06 9.32639e+06 8.43862e+06 8.36875e+06 7.99512e+06 8.7581e+06 9.08997e+06 9.34913e+06 7.40126e+06 8.92227e+06 7.78816e+06 8.56703e+06 7.55209e+06 7.92004e+06 1.18579e+07 6.06957e+06 +P08243 P08243;F8WEJ5;C9J057 ASNS_HUMAN ASNS Asparagine synthetase [glutamine-hydrolyzing] 474978 511358 269884 412685 593734 359187 423636 272340 360303 338515 360575 403920 405455 598966 482288 259778 380183 607092 494921 +P08572 P08572 CO4A2_HUMAN COL4A2 Collagen alpha-2(IV) chain 357618 279261 540519 762848 905322 970033 964673 974768 1.15786e+06 1.06291e+06 1.06377e+06 1.27098e+06 1.06008e+06 1.05708e+06 905772 1.00896e+06 941882 956730 648836 1.13924e+06 +P08581 P08581 MET_HUMAN MET Hepatocyte growth factor receptor 481700 357441 708941 744031 861824 630003 732280 616705 1.06563e+06 809213 756559 803332 1.00587e+06 763182 600035 581834 807088 674333 474654 722238 +P08621 P08621 RU17_HUMAN SNRNP70 U1 small nuclear ribonucleoprotein 70 kDa 275209 219669 333044 290383 624728 731933 444401 479879 601539 669641 432209 591392 565274 654299 387558 451698 528645 573502 577910 232163 +P08670 P08670;B0YJC4 VIME_HUMAN VIM Vimentin 1.08618e+06 1.86316e+06 1.1336e+06 2.34682e+06 1.39972e+06 2.47289e+06 1.07242e+06 1.93534e+06 1.47906e+06 2.62917e+06 1.12266e+06 2.54993e+06 1.57887e+06 2.56974e+06 1.1556e+06 1.90338e+06 1.66448e+06 2.15238e+06 1.30293e+06 1.56189e+06 +P08708 P08708;H3BNC9 RS17_HUMAN RPS17 40S ribosomal protein S17 289782 356347 224573 400465 254206 368415 254110 395063 232820 319701 262626 362835 203443 295503 176103 324661 149203 279318 271387 453697 +P08754 P08754 GNAI3_HUMAN GNAI3 Guanine nucleotide-binding protein G(i) subunit alpha-3 261613 367274 574426 487169 316059 345355 494394 347717 588775 461675 559617 329059 491997 476477 394840 434830 597025 526610 497699 +P08758 P08758;D6RBL5;E9PHT9 ANXA5_HUMAN ANXA5 Annexin A5 107094 69849 105088 137396 94858.1 78339.9 117603 73344.4 89878.1 109016 138291 93236.3 123024 92851.6 137676 149986 143535 116686 143742 +P08779 P08779;K7ENW6 SWISS-PROT:P08779 SWISS-PROT:P08779 552760 50776.1 69687.3 168028 103906 740947 91599.6 265267 672277 131968 136233 1.63541e+06 552552 4.12209e+06 169007 568882 646784 286874 81342.4 648258 +P08865 P08865;A0A0C4DG17;A0A8V8TLA4;A0A8V8TMX1;A0A8V8TMZ8;C9J9K3;F8WD59 RSSA_HUMAN RPSA 40S ribosomal protein SA 1.78654e+06 1.98031e+06 1.49132e+06 2.32447e+06 2.11719e+06 2.32819e+06 1.80344e+06 2.33499e+06 2.33464e+06 2.38692e+06 1.85303e+06 2.31083e+06 2.26215e+06 2.25924e+06 1.85007e+06 1.96491e+06 1.80662e+06 2.50664e+06 2.32102e+06 1.69645e+06 +P08962 P08962;F8VWK8;F8W022;F8VNT9;F8VV56 CD63_HUMAN CD63 CD63 antigen 77617.9 37452.2 45951.3 44363.1 29598 32955.8 35206 38219.4 50018.4 16867.7 91549.8 43806.1 +P09012 P08579;P09012;M0R221;M0R2B8;M0QXK2;M0QZG7;M0R0G9;M0R268 SNRPA_HUMAN SNRPA U1 small nuclear ribonucleoprotein A 141317 128345 134583 222259 268423 246108 210622 217799 183096 270804 210884 252848 293784 249760 176029 183731 260331 266372 269360 114395 +P09211 P09211;A8MX94;A0A2R8Y5E5 GSTP1_HUMAN GSTP1 Glutathione S-transferase P 238957 +P09382 P09382 LEG1_HUMAN LGALS1 Galectin-1 214753 159354 72428.8 205103 150809 151697 128328 133082 119563 154075 155207 176468 130802 156357 128638 153575 200128 116126 161945 149687 +P09429 P09429;P26583;B2RPK0;D6R9A6;Q5T7C4 HMGB1_HUMAN HMGB1 High mobility group protein B1 1.07254e+06 697065 681177 725223 590441 552836 736932 410105 533992 538709 744074 646895 427347 492945 622551 643121 659309 445041 1.04292e+06 677816 +P09467 P09467;A0A3B3IUC7 F16P1_HUMAN FBP1 Fructose-1,6-bisphosphatase 1 55326.1 +P09486 P09486;F5H4E2 SPRC_HUMAN SPARC SPARC 8.43288e+06 9.1644e+06 7.3926e+06 1.0269e+07 9.62444e+06 1.01407e+07 9.15544e+06 8.99177e+06 1.39615e+07 8.69822e+06 +P09493 P09493;A0A0S2Z4G6;A0A494C0V8;B7Z596;F5H7S3;H0YK48;H0YKJ4;H0YL42;H7BYY1;Q6ZN40;A0A494BZZ2;H0YKX5;H0YL80 TPM1_HUMAN TPM1 Tropomyosin alpha-1 chain 449411 647423 782447 633778 687162 843779 633183 683844 646569 900889 583828 612590 541021 685430 710080 620557 484238 584831 534106 697319 +P09496 P09496;F8WF69 CLCA_HUMAN CLTA Clathrin light chain A 311215 387404 432689 488569 370397 410316 353814 381100 382617 450098 371997 393566 392899 362481 371312 397320 250330 257887 454739 323984 +P09497 P09497;D6RJD1 CLCB_HUMAN CLTB Clathrin light chain B 155821 99919.3 105927 93756.1 100228 88427.9 101707 82247.4 79063.4 122068 84945.3 92371.2 93279.9 82762.4 106789 73926.4 62374.3 71090.8 100480 84181.9 +P09622 P09622;E9PEX6 DLDH_HUMAN DLD Dihydrolipoyl dehydrogenase, mitochondrial 472630 286714 872482 417853 470114 243511 576960 193004 529771 288537 524069 323975 376830 300531 369542 354491 597986 329732 283641 261068 +P09651 P09651;Q32P51;A0A2R8Y4L2;A0A7I2V2L6;A0A7I2V3R8;A0A7I2V3W0;F8VZ49;F8W6I7;A0A7I2V5U9;A0A7I2YQX9;F8VTQ5;H0YH80 ROA1_HUMAN HNRNPA1 Heterogeneous nuclear ribonucleoprotein A1 553573 1.20481e+06 630335 1.0921e+06 1.97184e+06 2.15809e+06 964275 1.06284e+06 2.7315e+06 2.58952e+06 521086 1.63945e+06 1.89574e+06 2.25648e+06 734421 1.15984e+06 2.10268e+06 2.01008e+06 606157 1.07347e+06 +P09661 P09661;H0YKK0 RU2A_HUMAN SNRPA1 U2 small nuclear ribonucleoprotein A' 196822 136625 226734 180907 215546 192390 169699 206464 218216 174365 220366 208196 225022 190166 156810 104183 253244 192269 253532 173923 +P09871 P09871;F8WCZ6 C1S_HUMAN C1S Complement C1s subcomponent 695433 1.41935e+06 734475 2.59217e+06 828684 2.07465e+06 1.0424e+06 1.6956e+06 1.0979e+06 2.7804e+06 1.01833e+06 2.69179e+06 1.03093e+06 2.70557e+06 1.13239e+06 3.31117e+06 1.36691e+06 3.51283e+06 765232 3.64683e+06 +P09874 P09874;A0A7I2V3E1 PARP1_HUMAN PARP1 Poly [ADP-ribose] polymerase 1 526022 729924 492824 650203 617630 722212 550462 787702 575775 633364 567306 704280 483133 628352 375862 675176 509144 689406 673137 442989 +P09884 P09884;A6NMQ1 DPOLA_HUMAN POLA1 DNA polymerase alpha catalytic subunit 55981.6 43162.6 29610.3 62199.7 31953.9 62019.4 53863 69875.5 60041.1 56598.6 52096.3 54943.6 +P09958 P09958;A0A7P0T8U2 FURIN_HUMAN FURIN Furin 13126 36543.5 43808.7 33587.9 71136.5 41966.7 18411.5 +P09960 P09960 LKHA4_HUMAN LTA4H Leukotriene A-4 hydrolase 887977 910290 1.00563e+06 685480 781407 662104 911401 565506 719643 792897 854502 613238 819650 691178 937804 781983 861178 788580 814915 780830 +P09972 P09972;A8MVZ9;C9J8F3;J3KSV6;J3QKK1;J3QKP5 ALDOC_HUMAN ALDOC Fructose-bisphosphate aldolase C 237584 376103 221510 266859 436765 327668 361322 234095 521042 498703 415880 247422 326265 416231 170706 231653 351885 309549 320866 151547 +P0C0L4;P0C0L5 P0C0L4;P0C0L5;A0A0G2JL54;A0A0G2JPR0;A0A140TA29;A0A140TA32;A0A140TA44;A0A140TA49;F5GXS0;A0A8V8TLP6 CO4A_HUMAN;CO4B_HUMAN C4A;C4B_2 Complement C4-A 10036.7 20126.4 24042.3 29609.2 +P0C0S8;P20671;Q16777;Q6FI13;Q96KK5;Q99878;Q9BTM1 P04908;P0C0S8;P20671;Q16777;Q6FI13;Q7L7L0;Q93077;Q96KK5;Q99878;Q9BTM1;A0A0U1RR32;A0A0U1RRH7;H0YFX9 H2A1D_HUMAN;H2A1H_HUMAN;H2A1J_HUMAN;H2A1_HUMAN;H2A2A_HUMAN;H2A2C_HUMAN;H2AJ_HUMAN H2AC12;H2AC14;H2AC17;H2AC19;H2AC20;H2AC7;H2AJ Histone H2A type 1 6.52387e+06 541353 1.08917e+06 1.36208e+06 1.76229e+06 852634 864986 989145 1.89174e+06 892358 1.26235e+06 1.02377e+06 1.76103e+06 999254 1.05945e+06 1.01028e+06 1.19161e+06 1.00999e+06 837683 1.16245e+06 +P0C7H8 Q9BYR9;P0C7H8 KRA23_HUMAN KRTAP2-3 Keratin-associated protein 2-3 3.80231e+06 +P0CW18 P0CW18 PRS56_HUMAN PRSS56 Serine protease 56 1.30692e+06 149719 750032 229718 1.79308e+06 436222 1.21185e+06 297697 1.92629e+06 373350 1.35556e+06 366783 1.92738e+06 458332 1.46688e+06 292498 1.12123e+06 407470 1.23292e+06 202664 +P0DMV8;P0DMV9 P0DMV8;P0DMV9;P34931;A0A0G2JIW1;Q53FA3;V9GZ37 HS71A_HUMAN;HS71B_HUMAN HSPA1A;HSPA1B Heat shock 70 kDa protein 1A 2.48705e+06 3.25253e+06 2.0258e+06 3.18111e+06 2.89753e+06 3.35717e+06 2.73563e+06 3.01623e+06 2.78101e+06 3.79584e+06 2.55845e+06 3.05635e+06 2.64172e+06 3.1963e+06 2.71519e+06 3.16034e+06 2.63346e+06 2.68839e+06 2.77854e+06 2.88639e+06 +P0DP24 P0DP23;P0DP24;P0DP25;E7EMB3;F8WBR5;G3V479;M0QZ52 CALM2_HUMAN CALM2 Calmodulin-2 134966 44603.9 58754.8 55015.3 103166 25448.4 85295.6 341088 29373.6 131308 151635 +P0DPA2 P0DPA2;P0DPA3 VSIG8_HUMAN VSIG8 V-set and immunoglobulin domain-containing protein 8 5.45206e+06 +P0DPB6 P0DPB6;A0A0R4J2F3 RPAC2_HUMAN POLR1D DNA-directed RNA polymerases I and III subunit RPAC2 90114.8 67869.6 50287.3 73333.2 92056.7 74001.8 40995.1 80809.9 42977.6 71664.3 61756.6 78853 71313.5 72864.8 88085 70317.2 44074.9 80745.2 +P10124 P10124 SRGN_HUMAN SRGN Serglycin 1.01842e+06 372288 1.02478e+06 728760 749276 615928 1.1502e+06 769821 1.05349e+06 847948 815847 975128 974301 714305 1.6556e+06 677193 3.02486e+06 1.18534e+06 1.14223e+06 1.21131e+06 +P10155 P10155;H0Y9N5 RO60_HUMAN RO60 RNA-binding protein RO60 116674 202841 85864.5 69602.9 122454 111008 144564 91014.1 64260.6 166386 124248 103511 110381 94742.8 147011 168990 142712 128597 119537 89200.2 +P10253 P10253 LYAG_HUMAN GAA Lysosomal alpha-glucosidase 424612 61502 358144 89329.9 549962 92514.5 496014 108490 675139 84729.7 550850 114421 584430 99867.3 466269 106642 444715 123869 264854 69255.7 +P10321 P01893;P04439;P10321;P30511;Q2L6G2;Q5SUL5;A0A0G2JIF2;A0A0G2JL56;A0A0G2JPD3;A0A140T8Y4;A0A140T912;A0A140T913;A0A140T9A9;A0A140T9G0;A0A140T9H3;A0A140T9H5;A0A140T9I0;A0A140T9J9;A0A140T9X5;A0A1W2PP77;A0A1W2PQS8;A0A1W2PR11;A0A1W2PR61;A0A1W2PRT9;A0A1W2PRX1;A0A1W2PRZ0;A0A1W2PS24;A0A1W2PSE7;A2AEA2;B0UXQ0;D5H3J5;D9J307;O19617;O78179;Q53Z42;Q5QT33;Q5RJ27;Q5SPM2;Q5SRN5;Q9GJ45;A0A0G2JH66;A0A0G2JI36;A0A0G2JNV5;A0A0G2JPA3;A0A140T8W8;A0A140T8X0;A0A140T921;A0A140T930;A0A140T933;A0A140T937;A0A140T939;A0A140T941;A0A140T942;A0A140T953;A0A140T955;A0A140T962;A0A140T975;A0A140T997;A0A140T9B3;A0A140T9B7;A0A140T9C2;A0A140T9D9;A0A140T9E1;A0A140T9J8;A0A140T9L3;A0A140T9M5;A0A140T9U8;A0A140T9V0;A0A140T9W3;A0A140T9W7;A0A140T9X6;A0A140T9Y5;A0A140T9Z6;A0A140TA05;A0A140TA11;A0A140TA13;A0A140TA14;A0A1W2PPF8;A0A1W2PPQ2;A0A1W2PQD0;A0A1W2PS39;D0EV57;Q5SRN7 HLAC_HUMAN HLA-C HLA class I histocompatibility antigen, C alpha chain 435344 804952 600038 1.51544e+06 603747 1.43966e+06 556982 1.27954e+06 678256 1.78437e+06 638242 1.88591e+06 701112 2.00408e+06 905163 1.24121e+06 871274 1.74268e+06 538894 1.85745e+06 +P10412 P10412;P16402;P16403 H14_HUMAN H1-4 Histone H1.4 1.30282e+06 774506 1.0859e+06 1.27009e+06 745458 706609 935155 736090 749021 611747 740425 743345 592856 742394 706838 789698 396360 829762 592853 659967 +P10586 P10586;H0Y380;H0Y4H1;H0Y6Z7;H0Y7Z9 PTPRF_HUMAN PTPRF Receptor-type tyrosine-protein phosphatase F 194516 266341 367957 371024 258533 317263 384103 304692 273577 376860 353447 334843 285427 339693 338143 455919 289224 437802 246620 429505 +P10599 P10599 THIO_HUMAN TXN Thioredoxin 1.1051e+06 1.74877e+06 958257 1.28516e+06 1.09451e+06 1.30059e+06 1.42622e+06 1.17988e+06 1.00428e+06 1.76182e+06 1.18533e+06 1.36462e+06 1.15201e+06 1.45031e+06 965546 2.12517e+06 1.0507e+06 1.60473e+06 1.34992e+06 2.19333e+06 +P10619 P10619;A0A7I2V4K5;X6R5C5;X6R8A1 PPGB_HUMAN CTSA Lysosomal protective protein 754473 657120 596701 1.33763e+06 857434 948769 927684 925993 1.09382e+06 1.07712e+06 1.01453e+06 1.39726e+06 945990 1.08815e+06 805467 1.14475e+06 1.0367e+06 1.18076e+06 682619 1.31378e+06 +P10644 P10644;P31321;C9JSK5;H7BYW5;K7EKR1;K7EM13;K7EPB2 KAP0_HUMAN PRKAR1A cAMP-dependent protein kinase type I-alpha regulatory subunit 59366.9 108906 71643 57428.3 136976 37857.8 120893 103396 38922.1 67128.9 91268.1 125962 100680 77186.9 27621.4 72924.3 76909.1 +P10646 P10646 TFPI1_HUMAN TFPI Tissue factor pathway inhibitor 55699.4 119060 148717 95620.8 184310 99062.6 181077 159673 106649 201413 98746.1 183694 73439.5 161440 66864.3 174985 78132.7 157070 +P10768 P10768;H7BZT7;U3KQT1;X6RA14 ESTD_HUMAN ESD S-formylglutathione hydrolase 115262 53869.1 93418.2 67260.1 117999 55069.8 141784 62533.2 84211.5 81455.9 117777 83836.8 123156 73512.9 114174 93753.4 111099 57508.7 77358.8 86673 +P10809 P10809;A0A7I2V2X6;A0A7I2V599;A0A7I2YQK6 CH60_HUMAN HSPD1 60 kDa heat shock protein, mitochondrial 969219 552892 1.9864e+06 969650 1.213e+06 372743 1.51846e+06 438554 1.17667e+06 411064 1.18966e+06 570303 1.43968e+06 522280 1.1401e+06 628789 1.0994e+06 398792 840838 644500 +P10909 P10909;H0YAS8 CLUS_HUMAN CLU Clusterin 4.22408e+06 6.89407e+06 3.86345e+06 1.23839e+07 6.04051e+06 1.37982e+07 5.49366e+06 1.33432e+07 7.61019e+06 1.38371e+07 6.3487e+06 1.7786e+07 6.47529e+06 1.62944e+07 5.98484e+06 1.36996e+07 6.10329e+06 1.4126e+07 4.05328e+06 1.16497e+07 +P11021 P11021;A0A7P0TAI0;A0A7P0TB36 BIP_HUMAN HSPA5 Endoplasmic reticulum chaperone BiP 3.82559e+06 1.91882e+06 5.36035e+06 2.84732e+06 3.96161e+06 2.27045e+06 4.51794e+06 2.05653e+06 4.46384e+06 2.84711e+06 4.07975e+06 2.43118e+06 3.63549e+06 2.3991e+06 3.76509e+06 2.65826e+06 4.55424e+06 2.74859e+06 2.92665e+06 2.45155e+06 +P11047 P11047 LAMC1_HUMAN LAMC1 Laminin subunit gamma-1 2.38343e+06 1.43458e+06 3.11752e+06 2.87364e+06 2.70157e+06 2.49644e+06 3.28137e+06 2.50189e+06 3.05223e+06 2.66839e+06 3.60846e+06 3.48036e+06 2.79785e+06 3.0222e+06 3.62868e+06 3.59903e+06 2.531e+06 3.03498e+06 2.16284e+06 3.5915e+06 +P11142 P11142;E9PI65;E9PK54;E9PKE3;E9PLF4;E9PN25;E9PN89;E9PPY6;E9PQK7;E9PQQ4 HSP7C_HUMAN HSPA8 Heat shock cognate 71 kDa protein 1.04083e+07 1.12462e+07 8.17781e+06 1.14468e+07 1.26938e+07 1.31693e+07 1.12448e+07 1.04274e+07 1.15113e+07 1.44163e+07 1.08189e+07 1.10572e+07 1.19038e+07 1.20825e+07 1.0803e+07 1.08698e+07 1.02396e+07 9.71738e+06 1.10677e+07 9.85996e+06 +P11166 P11166 GTR1_HUMAN SLC2A1 Solute carrier family 2, facilitated glucose transporter member 1 87712.2 96114.2 42535.5 111118 128073 63123.6 160309 52220.7 157579 61726.4 106228 54308.3 150085 57550.6 75466.3 30690.7 +P11216 P11216;H0Y4Z6 PYGB_HUMAN PYGB Glycogen phosphorylase, brain form 102263 91669.1 112707 91576.1 93775.3 117583 92486.6 103156 112281 261887 119379 92682 135538 108978 +P11279 P11279 LAMP1_HUMAN LAMP1 Lysosome-associated membrane glycoprotein 1 712225 323746 760397 585613 519181 410051 610977 325131 639114 508818 614133 553930 706037 524842 596779 536754 842698 617213 479819 691012 +P11387 P11387 TOP1_HUMAN TOP1 DNA topoisomerase 1 335466 222304 422289 320379 215492 176364 268953 218642 208233 161749 315805 268703 193950 154235 155153 189722 208765 153951 255975 129419 +P11388 P11388 TOP2A_HUMAN TOP2A DNA topoisomerase 2-alpha 124686 77852.4 106017 99158.8 107118 82065 115616 82036.8 81613.5 80951.6 125823 114302 123079 97338.5 104577 97451.5 47919.2 78116.4 118964 99943 +P11413 P11413;E7EUI8;A0A8Q3SIN6;A0A8Q3SIS5;A0A8Q3SJB9;A0A8Q3WL95 G6PD_HUMAN G6PD Glucose-6-phosphate 1-dehydrogenase 219025 206621 44798.1 210353 253771 341205 209373 275424 232944 366979 221161 303734 243611 328819 174652 256648 203307 322568 227578 195645 +P11464;Q00887;Q00889 P11464;Q00887;Q00888;Q00889;Q16557;Q13046;A0A087WT09;A0A087WYK1;A0A096LNM5;M0QX68;M0QZA7;M0R0B3;M0R2A2 PSG1_HUMAN;PSG6_HUMAN;PSG9_HUMAN PSG1;PSG6;PSG9 Pregnancy-specific beta-1-glycoprotein 1 19821.7 +P11586 P11586;F5H2F4;V9GYY3;V9GZ78;A0A494C1T2;A0A8V8TKS0;A0A8V8TKT4;A0A8V8TL50;A0A8V8TMA4 C1TC_HUMAN MTHFD1 C-1-tetrahydrofolate synthase, cytoplasmic 201359 329018 87284.9 209097 243333 366961 174889 371144 195495 281765 204677 271234 221442 295051 179740 266310 139804 268750 345582 194398 +P11717 P11717;A0A7I2V381 MPRI_HUMAN IGF2R Cation-independent mannose-6-phosphate receptor 202949 298880 255877 421362 294239 445082 307543 336847 289426 543692 288326 493486 337925 576785 333103 556147 326955 634507 224856 435313 +P11766 P11766 ADHX_HUMAN ADH5 Alcohol dehydrogenase class-3 211589 207351 115055 222497 191714 230038 203900 220190 159683 221568 181228 210457 175519 187360 180386 155777 162547 239955 153074 +P11802 P11802;F8VTV8;F8VWX7;F8VXD2;F8VYH9;F8VZ51;F8VZZ0 CDK4_HUMAN CDK4 Cyclin-dependent kinase 4 21301.4 21529.5 26400.9 51198.8 28946.9 25282.8 14233.9 16558 35506.8 25798.3 45039 +P11908 P11908;H7C540 PRPS2_HUMAN PRPS2 Ribose-phosphate pyrophosphokinase 2 143286 185576 139724 216683 89825.5 127515 215253 197705 167771 105997 104120 109557 200808 193268 +P11940 P11940;A0A087WTT1;A0A7I2V4N4;A0A7I2YQ88;A0A7I2YQE4;E7EQV3;E7ERJ7;A0A7I2V649;A0A7I2YQ90;H0YAS6;H0YAW6;H0YB75 PABP1_HUMAN PABPC1 Polyadenylate-binding protein 1 468097 745576 379768 664402 1.00621e+06 1.22471e+06 626410 830440 944285 1.09335e+06 591023 823148 993705 1.11184e+06 605976 716493 774871 1.04926e+06 620842 571216 +P12004 P12004 PCNA_HUMAN PCNA Proliferating cell nuclear antigen 663061 902116 639150 935016 752029 848471 685720 910252 694197 882116 763141 877757 705298 859858 745816 853620 491019 777938 593528 686277 +P12081 P12081;P49590;A0A2R8Y3N3;A0A2R8Y5P7;A0A2R8Y6I1;A0A6Q8PF07;A0A6Q8PF47;A0A6Q8PHA5;A0A6Q8PHE3;B4DDD8;B4DQ67;B4E1C5;D6RB22;E7ETE2;A0A6Q8PFF2 HARS1_HUMAN HARS1 Histidine--tRNA ligase, cytoplasmic 364915 388379 345199 385288 446078 381903 421250 280775 344800 407112 397600 388638 402587 324717 459868 412268 501672 256833 475018 412107 +P12109 P12109;A0A087X0S5 CO6A1_HUMAN COL6A1 Collagen alpha-1(VI) chain 64873.7 126862 39970.2 121177 115583 39092.6 141450 71877.6 174249 64887.6 177257 62591.5 136229 43022 140607 101845 +P12268 P12268;E7ETK5;A0A7I2V2T3 IMDH2_HUMAN IMPDH2 Inosine-5'-monophosphate dehydrogenase 2 499299 284935 264327 263557 735739 391790 616178 343303 448327 325334 633468 282265 598576 303412 645227 344441 446554 246856 1.1136e+06 234950 +P12270 P12270 TPR_HUMAN TPR Nucleoprotein TPR 554050 502534 748037 660916 636326 638014 695851 637800 716621 641136 653737 637996 621821 641327 665203 553997 561666 633547 534221 461339 +P12277 P12277;A0A0S2Z471;H0YJG0 KCRB_HUMAN CKB Creatine kinase B-type 888905 340669 768693 306866 1.12638e+06 360027 954027 400381 995762 495057 1.08294e+06 421875 1.08706e+06 473603 777522 467337 1.22685e+06 408086 956184 468791 +P12532 P12532;C9JSQ1;F8WCN3 KCRU_HUMAN CKMT1B Creatine kinase U-type, mitochondrial 339396 301926 207160 251078 247360 225820 187956 254638 233362 145733 +P12763 P12763 SWISS-PROT:P12763 SWISS-PROT:P12763 3.47157e+06 5.47114e+06 4.5541e+06 5.21991e+06 3.57701e+06 3.45295e+06 3.43495e+06 2.64921e+06 4.82151e+06 4.08326e+06 3.75834e+06 3.61948e+06 4.27721e+06 3.76693e+06 3.93627e+06 4.32505e+06 4.46103e+06 3.28834e+06 3.38919e+06 5.35203e+06 +P12814 P12814;A0A7I2V4Y4;A0A804HII9;A0A804HIN7;A0A804HJN7;A0A804HJQ9;A0A804HJU8;A0A804HK61;A0A804HKE2;A0A804HL31;A0A804HLD0;A0A804HLF4;G3V2N5;G3V2W4;H9KV75 ACTN1_HUMAN ACTN1 Alpha-actinin-1 244402 407727 320021 242006 306851 303067 340179 247290 277440 327130 309989 240581 304108 285109 218976 359832 331080 271300 303985 252107 +P12955 P12955;A0A494C165;A0A494C194;A0A8V8TLL9;A0A8V8TLN7;A0A8V8TLP2;A0A8V8TLP4;A0A8V8TLR1;A0A8V8TLR6;A0A8V8TN48;A0A8V8TNF2;K7ES25 PEPD_HUMAN PEPD Xaa-Pro dipeptidase 398133 299334 286833 232873 284235 231691 304785 160241 279708 257002 289791 203544 320024 247910 330134 262950 416536 272681 288431 278398 +P12956 P12956;B1AHC9 XRCC6_HUMAN XRCC6 X-ray repair cross-complementing protein 6 897384 856080 507336 820077 994946 952694 772163 900696 767058 771627 802121 817335 828312 795511 702349 694214 718606 723532 931519 473325 +P13010 P13010 XRCC5_HUMAN XRCC5 X-ray repair cross-complementing protein 5 630631 527427 301496 480476 564717 515662 445888 510006 463175 493730 450798 504399 518177 448907 376972 383640 462152 411931 608875 325434 +P13164;Q01628;Q01629 P13164;Q01628;Q01629;E9PQN9;A0A7P0T8E0;A0A7P0T953;A0A7P0T9Q3;E9PS44;H7BYV1 IFM1_HUMAN;IFM2_HUMAN;IFM3_HUMAN IFITM1;IFITM2;IFITM3 Interferon-induced transmembrane protein 1 98735.4 98524.6 178382 135853 119440 74064.2 135287 107204 149549 116654 145848 125817 137262 90513.6 231382 145243 105103 147343 97586.3 131046 +P13284 P13284 GILT_HUMAN IFI30 Gamma-interferon-inducible lysosomal thiol reductase 1.58209e+06 1.13306e+06 1.21596e+06 2.17807e+06 1.90237e+06 2.19813e+06 1.50685e+06 2.04371e+06 1.88432e+06 2.18288e+06 2.04278e+06 3.30474e+06 1.93839e+06 2.63621e+06 1.86184e+06 1.81364e+06 1.50211e+06 1.97673e+06 1.16435e+06 2.09153e+06 +P13473 P13473 LAMP2_HUMAN LAMP2 Lysosome-associated membrane glycoprotein 2 211917 162643 213782 250974 156106 199948 159749 88605.1 118483 235636 165341 254736 193431 219667 88486 243115 271247 190185 148538 318688 +P13489 P13489;H0YCR7 RINI_HUMAN RNH1 Ribonuclease inhibitor 1.28427e+06 1.25119e+06 472995 1.51756e+06 998703 1.14611e+06 969960 996145 662726 1.30158e+06 952375 1.09076e+06 839834 1.17896e+06 1.00934e+06 1.08978e+06 878542 850642 948404 1.01722e+06 +P13497 P13497 BMP1_HUMAN BMP1 Bone morphogenetic protein 1 403767 363176 426137 817083 517775 526625 677268 615746 545143 538715 716510 977739 542324 656078 607104 986497 483274 669033 517077 1.05559e+06 +P13501 P13501;A0A075B7C5;A0A494C1Q1 CCL5_HUMAN CCL5 C-C motif chemokine 5 124809 425804 134467 151595 38285.2 137225 266116 120550 264687 265310 407236 +P13639 P13639 EF2_HUMAN EEF2 Elongation factor 2 5.06668e+06 4.6182e+06 2.26764e+06 4.33547e+06 4.32852e+06 4.36727e+06 3.80379e+06 4.43426e+06 3.97085e+06 4.09338e+06 4.21173e+06 4.41244e+06 4.09437e+06 4.25444e+06 3.79271e+06 3.85349e+06 3.31291e+06 4.15885e+06 4.81526e+06 3.58497e+06 +P13646 P13646-1;P13646 K1C13_HUMAN KRT13 Keratin, type I cytoskeletal 13 6.4311e+06 2.31824e+06 2.8269e+06 1.10414e+06 4.72883e+06 3.27155e+06 4.32717e+06 2.74884e+06 1.61148e+06 2.1565e+06 4.02908e+06 6.70805e+06 6.51048e+06 3.10746e+06 1.68508e+06 7.87923e+06 1.84426e+06 2.85556e+06 1.2984e+06 8.66326e+06 +P13646-1 P13646-1 SWISS-PROT:P13646-1 SWISS-PROT:P13646-1 252542 173936 672052 +P13647 P13647;Q5XQN5;Q922U2;F8W0C6;H0YIN9 SWISS-PROT:P13647 SWISS-PROT:P13647 4.03172e+06 1.37444e+06 1.56349e+06 1.15494e+06 2.42375e+06 2.1952e+06 1.76597e+06 710724 1.16268e+06 1.18274e+06 1.45072e+06 6.41573e+06 2.13065e+06 3.66169e+06 798746 3.76906e+06 2.60318e+06 2.24274e+06 825787 6.35674e+06 +P13667 P13667;A0A499FI48;C9JMN9 PDIA4_HUMAN PDIA4 Protein disulfide-isomerase A4 730255 296916 1.01854e+06 443305 671358 338437 958807 418480 649918 491729 926486 409855 687165 356399 795674 556666 751784 528125 689055 374870 +P13693 P13693;Q9HAU6;A0A0B4J2C3;E9PJF7;J3KPG2;Q5W0H4 TCTP_HUMAN TPT1 Translationally-controlled tumor protein 903821 1.04426e+06 913708 926696 758200 795641 858978 516411 709096 983698 898440 677300 760228 709376 495254 934546 1.39758e+06 783859 1.0992e+06 926753 +P13797 P13797 PLST_HUMAN PLS3 Plastin-3 952497 2.58917e+06 766791 2.07978e+06 986939 2.32816e+06 1.28984e+06 1.7691e+06 978219 2.48141e+06 1.14633e+06 1.9002e+06 1.00214e+06 2.36128e+06 1.19562e+06 3.19623e+06 1.13467e+06 2.6774e+06 1.27335e+06 2.90215e+06 +P13798 P13798;C9JIF9 ACPH_HUMAN APEH Acylamino-acid-releasing enzyme 603186 584023 590408 489330 602482 487548 663594 353242 550813 507484 606874 322504 534698 457552 649263 564172 669931 594329 579460 509981 +P13861 P13861;H7C4A9 KAP2_HUMAN PRKAR2A cAMP-dependent protein kinase type II-alpha regulatory subunit 129065 100604 101324 113614 161676 164861 148800 184590 152733 157371 154748 120378 185524 145279 145079 122784 107690 131050 172984 +P13987 P13987;A0A2U3TZL5;E9PNW4;H0YET2 CD59_HUMAN CD59 CD59 glycoprotein 518472 202788 400469 348580 514323 295349 573126 188504 496148 317318 471332 279222 636872 378566 699435 386480 762405 263433 486481 337359 +P14174 P14174 MIF_HUMAN MIF Macrophage migration inhibitory factor 1.47437e+06 1.82091e+06 972328 1.74254e+06 1.19232e+06 1.48435e+06 1.03153e+06 1.03377e+06 1.09133e+06 1.61287e+06 935482 975461 1.05021e+06 1.34263e+06 1.58829e+06 1.77277e+06 981774 1.7653e+06 1.23448e+06 2.06322e+06 +P14314 P14314;K7ELL7 GLU2B_HUMAN PRKCSH Glucosidase 2 subunit beta 341102 257805 572677 298183 365950 259058 445517 291812 423487 389019 472804 318155 432026 312304 335011 392438 551261 422750 335560 377572 +P14324 P14324;A0A087WVN4;A0A087WTP2;A0A087X1D8 FPPS_HUMAN FDPS Farnesyl pyrophosphate synthase 735037 843186 555532 720563 605007 730000 747826 504735 662983 725893 659995 569175 556292 738485 640203 842664 744016 714028 704722 755233 +P14550 P14550 AK1A1_HUMAN AKR1A1 Aldo-keto reductase family 1 member A1 357008 570900 334201 460128 312727 450928 335867 319455 342744 620905 262446 276190 391292 467300 388252 407039 455494 598015 303001 453302 +P14618 P14618;A0A804F6T5;A0A804F729;B4DNK4;A0A8V8TNX9 KPYM_HUMAN PKM Pyruvate kinase PKM 3.78085e+06 3.3872e+06 2.16488e+06 3.40143e+06 3.78622e+06 3.37874e+06 3.30807e+06 3.39448e+06 4.10574e+06 3.83696e+06 3.68333e+06 4.04621e+06 3.92449e+06 4.03213e+06 2.85212e+06 3.54166e+06 4.0816e+06 3.68163e+06 3.75511e+06 2.88466e+06 +P14625 P14625;A0A087WT78;A0A7P0T823;A0A7P0T917;A0A7P0TAE1;A0A7P0TAT8;A0A7P0TAY2;A0A7P0TBC2;H0YIV0;A0A7P0T885;A0A7P0T8R3 ENPL_HUMAN HSP90B1 Endoplasmin 1.36286e+06 558786 1.57573e+06 933236 1.52391e+06 709617 1.44716e+06 858220 1.61176e+06 777167 1.5822e+06 1.01203e+06 1.36935e+06 805160 1.31297e+06 754185 1.08582e+06 790121 998495 550646 +P14678;P63162 P14678;P63162;J3KRY3;J3QLE5;S4R3P3 RSMB_HUMAN;RSMN_HUMAN SNRPB;SNRPN Small nuclear ribonucleoprotein-associated proteins B and B' 164042 239765 250485 270798 648297 608588 429305 510988 687989 453642 414868 410582 587771 587852 410704 426745 447881 465691 415721 268699 +P14735 P14735;A0A3B3ISG5;A0A7I2V2P6;A0A7I2V3E3;A0A7I2V3K9;A0A7I2V610;A0A7I2YQV5;B3KSB8 IDE_HUMAN IDE Insulin-degrading enzyme 180617 131821 127183 156721 155348 110908 135081 123406 118341 141340 151700 135319 154806 141307 137256 114639 141241 145799 104130 +P14866 P14866;M0QXS5;A0A3B3ITJ4;M0QYT0 HNRPL_HUMAN HNRNPL Heterogeneous nuclear ribonucleoprotein L 209421 234249 366043 380892 585966 614871 472463 552003 544695 600430 449010 608622 571838 542483 362669 393832 348830 463410 411650 259281 +P14868 P14868;H7BZ35 SYDC_HUMAN DARS1 Aspartate--tRNA ligase, cytoplasmic 528395 459938 261650 464979 444194 505436 407427 488212 371937 405252 427634 522047 313808 418492 357660 405271 261913 328842 517269 216851 +P14923 P14923 PLAK_HUMAN JUP Junction plakoglobin 1.7215e+06 99824.5 299012 149212 258603 294155 236104 111788 262984 125668 192073 1.2567e+06 399284 408204 128890 710034 202477 256781 83309 465518 +P15121 P15121;E9PCX2 ALDR_HUMAN AKR1B1 Aldo-keto reductase family 1 member B1 389964 857035 422151 912530 552398 964863 507004 835207 587255 1.06887e+06 508127 815618 591348 951824 596739 1.01302e+06 481729 923312 451135 844929 +P15151 P15151;A0A0A0MSA9;A0A0C4DG49 PVR_HUMAN PVR Poliovirus receptor 859497 628630 1.95766e+06 1.08349e+06 1.25117e+06 956327 1.70669e+06 786226 1.7828e+06 1.13411e+06 1.41378e+06 804943 1.51351e+06 1.14285e+06 1.74286e+06 1.14936e+06 1.63864e+06 1.40895e+06 927325 1.18369e+06 +P15170 P15170;Q8IYD1;H3BR35 ERF3A_HUMAN GSPT1 Eukaryotic peptide chain release factor GTP-binding subunit ERF3A 205324 446083 258353 306644 580779 107625 483599 212115 499655 143168 447717 185709 501635 187114 462772 129863 281707 277955 211544 +P15289 P15289;A0A0C4DFZ2 ARSA_HUMAN ARSA Arylsulfatase A 255025 203333 165007 356708 188366 226618 368165 269383 281221 433469 282258 296917 236578 370055 282040 395272 302105 478751 171183 377798 +P15291 P15291 B4GT1_HUMAN B4GALT1 Beta-1,4-galactosyltransferase 1 1.20327e+06 450945 539009 635512 1.68592e+06 905873 1.25597e+06 825533 1.82483e+06 832963 1.69943e+06 1.12897e+06 2.15907e+06 1.09284e+06 1.26387e+06 820551 1.30092e+06 856683 1.17718e+06 564452 +P15311 P15311 EZRI_HUMAN EZR Ezrin 1.487e+06 1.87507e+06 1.37743e+06 1.74494e+06 1.75639e+06 1.6977e+06 1.89602e+06 1.4017e+06 1.55738e+06 1.88912e+06 1.66856e+06 1.43733e+06 1.52255e+06 1.49929e+06 2.06456e+06 1.80768e+06 1.74347e+06 1.33289e+06 1.95298e+06 1.4709e+06 +P15374 P15374;A0A087WTB8;Q5TBK7 UCHL3_HUMAN UCHL3 Ubiquitin carboxyl-terminal hydrolase isozyme L3 50230.8 108449 104699 111528 131988 129813 72798.2 118273 105047 106606 59391.1 92082.5 98310.2 115329 75251.9 103030 88366 +P15497 P15497 SWISS-PROT:P15497 SWISS-PROT:P15497 345794 274219 221968 278792 701592 590348 588610 393339 610869 540142 629873 500033 699665 928902 622132 570332 901280 737503 474070 563802 +P15509 P15509;F2Z3C9;J3JS74 CSF2R_HUMAN CSF2RA Granulocyte-macrophage colony-stimulating factor receptor subunit alpha 44478.8 76743.7 50817.5 43747.6 66645.7 55626.1 77813.9 90702 83640.4 59581.3 +P15531 P15531;P22392;O60361;Q32Q12;E7ERL0;J3KPD9 NDKA_HUMAN NME1 Nucleoside diphosphate kinase A 4.85033e+06 4.56698e+06 3.78019e+06 4.2983e+06 4.79444e+06 4.52264e+06 5.63278e+06 3.6216e+06 5.51525e+06 4.68666e+06 5.00102e+06 3.46407e+06 4.52214e+06 4.03493e+06 6.01316e+06 3.99377e+06 4.70769e+06 3.3243e+06 5.10336e+06 4.43082e+06 +P15559 P15559;B4DLR8;H3BNV2 NQO1_HUMAN NQO1 NAD(P)H dehydrogenase [quinone] 1 281563 419276 256627 380599 452656 300603 389570 213629 411570 287621 345569 472993 333446 364194 334153 273381 347376 487978 307810 +P15586 P15586 GNS_HUMAN GNS N-acetylglucosamine-6-sulfatase 2.81865e+06 1.49103e+06 4.33147e+06 2.57067e+06 2.80136e+06 1.69352e+06 3.60259e+06 1.6873e+06 3.49563e+06 2.07547e+06 3.43378e+06 2.12469e+06 3.52303e+06 2.15034e+06 4.15191e+06 2.49402e+06 4.25608e+06 2.96584e+06 2.25213e+06 3.13125e+06 +P15692 P15692;A0A0A0MSH5;A0A0A0MSH7;A0A0A0MSI7;A0A0A0MTB2;A0A5F9ZH41;A2A2V4;H0YBI8;H0YBZ0;H3BLW8;J3KPA4;A0A0A0MR43;A0A0A0MRQ4 VEGFA_HUMAN VEGFA Vascular endothelial growth factor A 109881 239540 241867 339027 194934 245241 261814 294151 184108 313230 214710 331410 246454 295807 153485 488027 178884 350114 146410 600329 +P15848 P15848 ARSB_HUMAN ARSB Arylsulfatase B 257847 37690.6 175281 48173.4 201416 57906.9 234812 47106.7 210513 71324.8 192283 71708.8 241218 68024.2 264213 86093.3 199537 56643.4 121817 60386.5 +P15880 P15880;E9PM36;E9PQD7;H0YEN5;I3L404 RS2_HUMAN RPS2 40S ribosomal protein S2 2.69724e+06 2.7087e+06 2.69192e+06 2.99057e+06 1.93488e+06 1.97236e+06 2.43153e+06 2.49297e+06 1.52399e+06 1.65533e+06 2.46598e+06 2.4422e+06 1.58865e+06 1.82625e+06 1.75221e+06 2.10068e+06 1.14037e+06 1.58097e+06 2.21746e+06 1.34147e+06 +P15924 P15924 DESP_HUMAN DSP Desmoplakin 1.70365e+06 234089 385826 254430 446584 462021 340455 247788 287800 292296 322203 1.39469e+06 485250 616260 215630 835525 314318 324269 298854 573965 +P16035 P16035;B4DFW2 TIMP2_HUMAN TIMP2 Metalloproteinase inhibitor 2 2.8665e+06 1.40307e+06 3.94635e+06 2.77187e+06 4.58144e+06 2.91459e+06 5.64846e+06 2.46581e+06 4.34314e+06 3.3688e+06 5.04321e+06 3.5488e+06 4.2979e+06 2.84666e+06 6.28431e+06 2.99219e+06 3.74853e+06 1.74676e+06 3.74382e+06 3.41135e+06 +P16070 P16070;H0Y2P0;H0Y5E4;H0YCV9;H0YD13;H0YD17;H0YDW7;H0YDX6 CD44_HUMAN CD44 CD44 antigen 678555 1.11654e+06 952896 1.47088e+06 1.11519e+06 1.33696e+06 1.31841e+06 1.10387e+06 1.29961e+06 1.62578e+06 1.15131e+06 1.25693e+06 919342 1.53919e+06 1.34915e+06 1.64677e+06 1.13657e+06 1.23891e+06 915594 1.35926e+06 +P16083 P16083;Q5TD07 NQO2_HUMAN NQO2 Ribosyldihydronicotinamide dehydrogenase [quinone] 1.90828e+06 2.9093e+06 2.28652e+06 2.35387e+06 1.93696e+06 1.97593e+06 2.23645e+06 1.59457e+06 2.08714e+06 2.50604e+06 1.8459e+06 1.52585e+06 1.73607e+06 2.03042e+06 2.13436e+06 2.37274e+06 2.34718e+06 2.54274e+06 1.76232e+06 2.33144e+06 +P16152 P16152 CBR1_HUMAN CBR1 Carbonyl reductase [NADPH] 1 237237 275816 123142 144863 298222 307884 263130 285435 310991 350968 279342 254638 370962 339493 225451 329863 201517 354220 308358 625929 +P16278 P16278;E7EQ29;C9JF15;C9JWX1;F8WF40 BGAL_HUMAN GLB1 Beta-galactosidase 593919 361313 666675 785406 813467 577270 760546 667719 1.00821e+06 566722 834858 750543 959895 688422 576072 669181 792906 756054 430513 703210 +P16401 P16401 H15_HUMAN H1-5 Histone H1.5 831245 258926 476846 478425 268966 257206 324718 221822 280450 199294 288188 263400 193425 287857 310273 283930 282295 255579 275804 238763 +P16930 P16930 FAAA_HUMAN FAH Fumarylacetoacetase 145581 250272 142828 200304 154016 175633 162883 155042 128985 223152 137379 117473 147818 198094 192250 214984 134391 218696 121851 164276 +P16949 P16949;A2A2D0 STMN1_HUMAN STMN1 Stathmin 693579 1.43206e+06 728583 1.33739e+06 1.04452e+06 1.39377e+06 954965 1.26339e+06 820786 1.37544e+06 747191 1.06332e+06 728202 1.11691e+06 1.10617e+06 1.12291e+06 691035 770068 787974 844527 +P16989 P16989 YBOX3_HUMAN YBX3 Y-box-binding protein 3 122165 68698.7 71785.6 99024.9 56096.3 54078.7 57398.1 45177.1 49238.7 48131.9 72946.9 69690.1 92779.9 62321.9 39854.6 60804.7 63360.8 67626.3 98812 70928.9 +P17050 P17050 NAGAB_HUMAN NAGA Alpha-N-acetylgalactosaminidase 272645 90879.4 411126 192739 299752 122407 359779 104325 370883 152783 326575 159656 449093 146830 364595 190311 491784 196728 208986 216856 +P17066 P17066;P48741 HSP76_HUMAN HSPA6 Heat shock 70 kDa protein 6 4.74318e+06 5.11307e+06 6.01357e+06 6.68693e+06 3.86987e+06 4.55721e+06 4.41493e+06 3.42868e+06 4.79554e+06 6.38919e+06 4.85581e+06 5.04297e+06 4.97643e+06 4.656e+06 3.31934e+06 4.34561e+06 7.92615e+06 4.88595e+06 5.10774e+06 5.90735e+06 +P17096 P17096 HMGA1_HUMAN HMGA1 High mobility group protein HMG-I/HMG-Y 475418 500269 641274 670886 361181 497855 543175 504695 453708 462220 436835 527557 320064 480279 443133 547947 387084 627306 632611 608735 +P17174 P17174 AATC_HUMAN GOT1 Aspartate aminotransferase, cytoplasmic 1.38222e+06 1.18968e+06 1.58454e+06 957952 1.29491e+06 888624 1.49878e+06 652147 1.33766e+06 1.09896e+06 1.25279e+06 611067 1.43502e+06 916464 1.6327e+06 1.0542e+06 1.71047e+06 1.04981e+06 1.20085e+06 979997 +P17342 P17342 ANPRC_HUMAN NPR3 Atrial natriuretic peptide receptor 3 21595.9 49775.1 24406.6 45024 22413.9 65109.8 21018.4 58457 21100.8 25435.6 17434.3 41212.1 14693.9 46432.3 16012.3 +P17405 P17405 ASM_HUMAN SMPD1 Sphingomyelin phosphodiesterase 60163.5 60892.7 11424.5 36721.2 30934.7 68096.2 70878.1 19007.2 31799.7 29337.4 97473.3 56577.5 +P17655 P17655 CAN2_HUMAN CAPN2 Calpain-2 catalytic subunit 99825.4 173960 136060 189388 228024 158357 214934 156293 242428 180328 190236 201445 249465 153417 220300 106625 186611 180325 134464 +P17690 P17690 SWISS-PROT:P17690 SWISS-PROT:P17690 1.36672e+06 1.25324e+06 2.80594e+06 2.33698e+06 1.5292e+06 1.54811e+06 1.89464e+06 1.18316e+06 1.87062e+06 1.69393e+06 2.03172e+06 1.94074e+06 1.43927e+06 1.78617e+06 1.95774e+06 2.11812e+06 1.86809e+06 1.62565e+06 1.27069e+06 1.9231e+06 +P17812 P17812;Q9NRF8;A0A3B3IRI2;A0A3B3IRQ8;A0A3B3ITF6;A0A8Q3WKZ7;A0A8Q3WL40;A0A3B3ITB8;A0A3B3ITH9;A0A8Q3WL00;A0A8Q3WL12 PYRG1_HUMAN CTPS1 CTP synthase 1 355489 343173 112064 269310 361371 364965 299845 379972 286469 266213 334816 331662 303307 351470 375230 353448 186939 283959 408090 204941 +P17844 P17844;A0A0G2JLI4;J3KTA4;A0A7I2V2S0;A0A7I2V507;A0A7I2V5F5;J3KRZ1;J3QSF1 DDX5_HUMAN DDX5 Probable ATP-dependent RNA helicase DDX5 104960 103505 66069.4 62924.1 76465.8 90868.4 55142.2 109890 60878.6 79300.1 71809.2 103075 61456.2 71773.4 61026.9 95380.8 59054.3 48492 153905 70859.9 +P17931 P17931;G3V3R6 LEG3_HUMAN LGALS3 Galectin-3 480608 301515 22972.5 301272 312303 +P17936 P17936;A6XND0;B3KWK7;C9JMX4;H0Y485;H0Y5K2 IBP3_HUMAN IGFBP3 Insulin-like growth factor-binding protein 3 230135 445247 300448 819622 383977 731017 364353 723023 437064 835447 330974 858371 340684 786521 324499 709852 243620 846127 235431 659291 +P17980 P17980;E9PM69;R4GNH3;E9PKD5;E9PLG2;E9PMD8;E9PN50 PRS6A_HUMAN PSMC3 26S proteasome regulatory subunit 6A 222309 282707 129597 194641 285388 346022 182943 319908 174043 263006 209201 230848 282522 257467 117339 255499 191083 164104 335916 165059 +P17987 P17987;E7ERF2;E7EQR6;F5GZ03;F5H282 TCPA_HUMAN TCP1 T-complex protein 1 subunit alpha 1.26373e+06 1.04542e+06 851246 982103 1.31312e+06 975549 1.10882e+06 1.07188e+06 1.25711e+06 904428 1.23746e+06 956705 1.15907e+06 948973 999188 981575 1.0486e+06 956048 1.33696e+06 727784 +P18077 P18077 RL35A_HUMAN RPL35A 60S ribosomal protein L35a 3.62459e+06 2.96345e+06 3.35485e+06 3.79878e+06 2.30181e+06 2.3186e+06 2.29118e+06 2.22461e+06 2.19274e+06 2.29645e+06 2.55746e+06 2.76357e+06 2.63087e+06 2.08901e+06 1.94499e+06 2.41702e+06 2.61049e+06 2.43334e+06 3.13684e+06 1.86055e+06 +P18085;P61204;P84077;P84085 P18085;P61204;P84077;P84085;F5H423;A0A8V8TNZ0;A0A8V8TNZ5;A0A8V8TP92;A0A8V8TP99;A0A8V8TQC0;A0A8V8TQP8;C9J1Z8;C9JPM4;F5H0C7;F5H1V1;F5H6T5;F8WDB3 ARF1_HUMAN;ARF3_HUMAN;ARF4_HUMAN;ARF5_HUMAN ARF1;ARF3;ARF4;ARF5 ADP-ribosylation factor 4 293747 282935 273029 220280 199603 236085 299868 190022 223746 241322 326550 201782 233127 836262 215511 285824 192396 258292 197824 +P18124 P18124;A8MUD9;C9JIJ5 RL7_HUMAN RPL7 60S ribosomal protein L7 3.2132e+06 3.09767e+06 3.53004e+06 3.75937e+06 2.28165e+06 2.44465e+06 2.81239e+06 2.762e+06 2.12056e+06 2.10871e+06 2.90247e+06 2.5321e+06 1.90193e+06 2.20362e+06 2.07605e+06 2.24936e+06 1.42494e+06 2.03389e+06 2.68121e+06 1.55688e+06 +P18206 P18206 VINC_HUMAN VCL Vinculin 678549 932701 501668 715755 666743 735032 834877 611547 643769 790495 803336 666680 733895 801453 960499 1.05996e+06 752078 867454 839164 1.00583e+06 +P18621 P18621;A0A0A6YYL6;A0A087WWH0;A0A087WXM6;A0A087WY81;J3KRB3;J3KRX5;J3KSJ0;J3QQT2;J3QS96 RL17_HUMAN RPL17 60S ribosomal protein L17 2.21228e+06 2.21128e+06 2.05163e+06 2.72189e+06 1.68267e+06 1.66441e+06 1.98762e+06 1.85747e+06 1.2947e+06 1.37526e+06 1.94964e+06 1.77448e+06 983263 1.35753e+06 1.57341e+06 1.43306e+06 1.61891e+06 1.22244e+06 2.1137e+06 807992 +P18669 P18669;Q8N0Y7 PGAM1_HUMAN PGAM1 Phosphoglycerate mutase 1 2.64495e+06 3.2198e+06 2.29175e+06 2.25799e+06 2.78513e+06 2.60318e+06 3.55529e+06 1.78093e+06 3.11067e+06 2.71364e+06 2.96686e+06 1.98746e+06 2.26088e+06 2.55971e+06 2.93156e+06 3.21607e+06 2.96846e+06 2.36218e+06 3.10416e+06 2.66501e+06 +P18754 P18754;C9JMJ4;C9JQZ4;C9JRH2;C9JW69;C9J3R0 RCC1_HUMAN RCC1 Regulator of chromosome condensation 248984 189013 173114 219431 283680 284001 248017 278811 266667 257035 226634 252732 244811 222149 260979 169785 232163 208292 258082 109475 +P18827 P18827;E9PHH3;H7C1K4 SDC1_HUMAN SDC1 Syndecan-1 920669 175606 1.60689e+06 280980 599770 289904 1.0728e+06 181497 872148 374830 1.03016e+06 310565 568445 226295 742138 313471 1.7471e+06 467279 712168 259385 +P18850 P18850;A0A7P0T8R5;A0A7P0T9H5;A0A7P0T9V3;A0A7P0TAF2;A0A7P0TAH1;A0A7P0TB89;A0A7P0Z421;A0A7P0Z4I7 ATF6A_HUMAN ATF6 Cyclic AMP-dependent transcription factor ATF-6 alpha 23016.1 51472 23673 22653.1 33707.6 34801.5 21764.3 20766.2 27333.9 25182.2 11542.4 +P18887 P18887;F5H8D7 XRCC1_HUMAN XRCC1 DNA repair protein XRCC1 17588.2 16842.1 28174.6 52515.8 19076.8 26353.3 26887 22395.8 33293.3 19345.7 27697.9 23526.8 40319.9 +P19013 P19013;F8VX05;F8VZR6 SWISS-PROT:P19013 SWISS-PROT:P19013 65338.5 40287.7 25557.9 167073 +P19021 P19021;A0A804HI59;A0A804HIQ0;A0A804HKV8;A0A8C8KD64;A0A804HKM7 AMD_HUMAN PAM Peptidyl-glycine alpha-amidating monooxygenase 175732 241884 194090 439702 343425 520012 368903 535259 409765 627787 339516 655514 383537 551626 336733 477955 268821 452392 213858 449995 +P19338 P19338;C9JLB1;H7BY16;A0A7I2V3F3;A0A7I2V428;A0A7I2V699 NUCL_HUMAN NCL Nucleolin 8.19049e+06 3.34148e+06 6.32417e+06 4.61604e+06 4.79148e+06 3.09226e+06 6.36882e+06 2.86341e+06 5.01946e+06 3.181e+06 6.4217e+06 3.8378e+06 4.40419e+06 3.27273e+06 4.49376e+06 3.27026e+06 5.16597e+06 3.04976e+06 6.63381e+06 2.62055e+06 +P19367 P19367 HXK1_HUMAN HK1 Hexokinase-1 89992 85828.2 46269 42126.1 24977.1 31760.1 26737.1 50641.1 44058.8 +P19388 P19388;A0A087WVZ9;A0A087WWX0;A0A0A0MQR7;A0A087WVN5 RPAB1_HUMAN POLR2E DNA-directed RNA polymerases I, II, and III subunit RPABC1 59317.3 75653 53801.6 90581.3 96278.1 81345.3 69413.3 101928 76259.1 79542.5 59782.5 47664.6 94848.1 78168 73102.4 60426.3 67876.2 +P19438 P19438;F5H061;F5H8A6;F5H6V7 TNR1A_HUMAN TNFRSF1A Tumor necrosis factor receptor superfamily member 1A 18309.9 44634.9 +P19623 P19623;K7EL89 SPEE_HUMAN SRM Spermidine synthase 158527 149478 89697 173298 145022 126055 175240 144503 195625 146328 123514 202071 139120 168809 107820 116732 71733 155159 46369.6 +P19801 P19801 AOC1_HUMAN AOC1 Amiloride-sensitive amine oxidase [copper-containing] 118221 29972.6 135328 81575.3 26279.6 28913.2 62266.5 59166.7 83411 67282.2 69578.3 73741.2 50226.3 51162.9 186422 77876.5 173409 +P19971 P19971;A0A494C0L6;C9JGI3 TYPH_HUMAN TYMP Thymidine phosphorylase 358186 755626 392405 336783 673777 412015 614689 406705 293189 362488 631187 393185 435671 407888 +P20042 P20042 IF2B_HUMAN EIF2S2 Eukaryotic translation initiation factor 2 subunit 2 93320.8 101516 37167.2 87191.9 65003.8 110744 62558.8 102948 48142.8 105137 67930.2 103762 25190.9 80259.1 45206.5 90969.9 86869.2 87564.8 117340 43857 +P20290 P20290;D6RDG3;A0A7I2V3T6;A0A7I2V5Y3;A0A7I2YQL2;H0Y9Y1 BTF3_HUMAN BTF3 Transcription factor BTF3 316535 425785 276850 364008 380286 382538 357238 413553 376979 395101 309892 274081 289888 393587 371716 220965 312996 322689 439972 227189 +P20618 P20618 PSB1_HUMAN PSMB1 Proteasome subunit beta type-1 1.22465e+06 675762 961847 621090 961135 812158 1.10814e+06 568383 920913 1.10004e+06 997620 635312 960216 777434 654440 1.0319e+06 1.21896e+06 910358 1.01722e+06 810207 +P20674 P20674;H3BNX8 COX5A_HUMAN COX5A Cytochrome c oxidase subunit 5A, mitochondrial 97283.1 47413.3 129857 63835.1 74629.2 50608.7 71270.4 68982.8 47900.8 84604.5 83321.9 49777.2 55583.8 +P20700 P20700;A0A0D9SFE5;E9PBF6 LMNB1_HUMAN LMNB1 Lamin-B1 296595 326761 354369 398989 453248 653636 377158 479748 512404 766475 346543 490473 454549 697463 353611 503487 424676 549689 326723 398556 +P20810 P20810;A0A0C4DGB5;A0A6Q8PFE5;A0A6Q8PFF6;A0A6Q8PFR3;A0A6Q8PG72;A0A6Q8PGG5;A0A6Q8PGN6;A0A6Q8PH20;B7Z574;E7EQ12;E7EQA0;E7ES10;E7EVY3;E9PCH5;E9PDE4;H0Y7F0;H0Y9H6;H0YD33 ICAL_HUMAN CAST Calpastatin 280118 511569 355840 567405 446965 497185 423260 466678 434866 682605 386345 427537 490370 477346 281897 446380 380382 417913 379186 324330 +P20827 P20827 EFNA1_HUMAN EFNA1 Ephrin-A1 951045 833656 1.5462e+06 1.38844e+06 1.35078e+06 1.21064e+06 1.77241e+06 1.06434e+06 1.55149e+06 1.62647e+06 1.53568e+06 1.31859e+06 1.74463e+06 1.65049e+06 2.09598e+06 1.8231e+06 1.74892e+06 1.89379e+06 1.15199e+06 2.00369e+06 +P20839 P20839;C9J381;A0A3B3IRL5;C9K0R9 IMDH1_HUMAN IMPDH1 Inosine-5'-monophosphate dehydrogenase 1 34365.1 35479 41929.9 75808.6 54522.2 +P20851 P20851 C4BPB_HUMAN C4BPB C4b-binding protein beta chain 166468 313859 298313 255561 293705 483130 413657 54977 292710 322851 352884 +P20908 P20908 CO5A1_HUMAN COL5A1 Collagen alpha-1(V) chain 711422 426050 784190 614963 832315 655295 1.02966e+06 743970 1.14208e+06 727715 977422 942695 939273 783105 1.35384e+06 975436 796949 685115 636523 747565 +P20930 P20930 SWISS-PROT:P20930 SWISS-PROT:P20930 145336 32604.3 34935.4 78909.7 65443.4 58967.5 38075.2 49631.2 44478.5 66620.1 310364 97261.6 59135.7 152094 63599.9 66315.7 +P20933 P20933;H0Y9C7 ASPG_HUMAN AGA N(4)-(beta-N-acetylglucosaminyl)-L-asparaginase 207006 143868 366819 254260 238623 225605 292797 205551 288378 271845 277212 205893 293939 207403 210534 288944 323232 219806 129030 278453 +P20962 P20962;F5GXR3 PTMS_HUMAN PTMS Parathymosin 1.52247e+06 1.8705e+06 2.42875e+06 2.0385e+06 1.42916e+06 1.75857e+06 2.03331e+06 1.5713e+06 1.71557e+06 3.08829e+06 2.1015e+06 1.5531e+06 1.46049e+06 1.92785e+06 1.70096e+06 2.49592e+06 2.6057e+06 3.06085e+06 1.78436e+06 1.63129e+06 +P21266 P21266 GSTM3_HUMAN GSTM3 Glutathione S-transferase Mu 3 95853.8 316250 30445.6 223113 74798.7 311615 81573.3 265016 88930.2 268561 106256 214700 64640.2 253987 133743 230863 50099.3 269202 145171 280696 +P21281 P21281;H0YC04 VATB2_HUMAN ATP6V1B2 V-type proton ATPase subunit B, brain isoform 74213 43104.5 55001.3 67099.8 48303.8 55728.3 69086.6 48127.8 50828.2 51515.9 61637.6 58851.8 55708.6 65843.3 47863.8 43581.5 59086.6 58628.9 +P21291 P21291;E9PND2;E9PS42 CSRP1_HUMAN CSRP1 Cysteine and glycine-rich protein 1 116610 107240 112900 192704 207568 126294 89491.7 243569 262573 109587 84006.3 257734 218073 127539 78775.2 188905 107288 143124 66296.8 +P21333 P21333;Q60FE5;A0A7P0NMY4 FLNA_HUMAN FLNA Filamin-A 905382 1.12853e+06 813356 1.22792e+06 1.0047e+06 1.37887e+06 885843 1.28288e+06 925663 1.34212e+06 879741 1.2524e+06 906580 1.28659e+06 813295 1.17252e+06 811033 1.22734e+06 940802 941568 +P21399 P21399 ACOHC_HUMAN ACO1 Cytoplasmic aconitate hydratase 148812 286498 89278.3 200725 161843 258168 132632 221053 111688 255896 151881 187262 134541 265153 152533 241123 78828.8 260228 142025 192374 +P21583 P21583 SCF_HUMAN KITLG Kit ligand 10007.6 16149.2 61991.6 63916 93476.9 27851.6 44383.5 62175.5 77775 +P21796 P21796;C9JI87 VDAC1_HUMAN VDAC1 Voltage-dependent anion-selective channel protein 1 56233.2 +P22059 P22059 OSBP1_HUMAN OSBP Oxysterol-binding protein 1 175172 92224 1.97302e+06 98822.5 140353 88863.1 96372.6 353427 68057.6 108909 103461 90617.1 149392 136775 137050 135573 +P22061 P22061;A0A3F2YNX8;F6S8N6 PIMT_HUMAN PCMT1 Protein-L-isoaspartate(D-aspartate) O-methyltransferase 268954 191267 377021 323006 217886 343399 434183 265908 182371 349133 439270 313653 231423 280004 247958 241401 272753 345391 219794 +P22087 P22087;M0QXL5;M0R0P1;M0R299;M0R2Q4 FBRL_HUMAN FBL rRNA 2'-O-methyltransferase fibrillarin 146157 88982.2 112543 62048.1 92734.6 71091.4 79000 97186.5 84753.9 104508 102681 69826.4 72130.1 74763.7 114226 76738.1 +P22102 P22102 PUR2_HUMAN GART Trifunctional purine biosynthetic protein adenosine-3 550492 765368 284541 595827 673882 701749 588592 648373 525889 690482 576555 507427 619455 649767 686872 628577 579294 649994 675985 526922 +P22234 P22234;D6RF62;E9PBS1 PUR6_HUMAN PAICS Bifunctional phosphoribosylaminoimidazole carboxylase/phosphoribosylaminoimidazole succinocarboxamide synthetase 487192 545361 445638 447876 850523 1.10984e+06 655001 1.02505e+06 499262 916982 694284 929561 667921 932649 688164 875614 464845 738981 1.09435e+06 585319 +P22314 P22314;Q5JRR6 UBA1_HUMAN UBA1 Ubiquitin-like modifier-activating enzyme 1 946758 1.2063e+06 556801 1.01525e+06 1.07985e+06 1.30349e+06 843682 1.27968e+06 875389 1.19735e+06 965116 1.11909e+06 959054 1.25455e+06 891986 1.07998e+06 747304 1.14526e+06 1.05045e+06 823149 +P22466 P22466 GALA_HUMAN GAL Galanin peptides 93758.7 172804 245697 269458 649865 640830 320479 382973 1.06027e+06 783106 273790 403583 1.05825e+06 739519 186768 320415 704577 652181 198712 373361 +P22531;P22532;P35325;P35326;Q96RM1;Q9BYE4 P35326;P22531;P22532;P35325;Q96RM1;Q9BYE4 SPR2A_HUMAN;SPR2B_HUMAN;SPR2D_HUMAN;SPR2E_HUMAN;SPR2F_HUMAN;SPR2G_HUMAN SPRR2A;SPRR2B;SPRR2D;SPRR2E;SPRR2F;SPRR2G Small proline-rich protein 2E 48205.7 +P22626 P22626;A0A024RA28;A0A7I2V323;A0A7I2V3P1;A0A7I2V4I6;A0A7I2V4N1 ROA2_HUMAN HNRNPA2B1 Heterogeneous nuclear ribonucleoproteins A2/B1 556189 1.57801e+06 1.2212e+06 1.53769e+06 4.00269e+06 6.62325e+06 1.55409e+06 1.60164e+06 7.29208e+06 7.89495e+06 683688 2.06268e+06 6.68545e+06 6.40968e+06 1.60405e+06 1.62042e+06 5.5528e+06 4.76661e+06 618500 1.10059e+06 +P22735 P22735 TGM1_HUMAN TGM1 Protein-glutamine gamma-glutamyltransferase K 236902 33021.7 28401.1 35044.4 64722.6 75885.2 31785.3 61290.4 44930 62570.5 +P23142 ENSEMBL:ENSBTAP00000016046;P23142 FBLN1_HUMAN FBLN1 Fibulin-1 4.8199e+06 3.42958e+06 4.89821e+06 6.4508e+06 6.886e+06 5.96904e+06 5.72241e+06 6.07032e+06 7.6494e+06 6.18399e+06 6.69865e+06 8.06214e+06 6.79185e+06 7.55337e+06 8.04223e+06 5.84355e+06 5.94591e+06 6.35621e+06 4.07084e+06 5.62641e+06 +P23193 P23193;A0A1W2PPZ5;A0A1W2PRL9 TCEA1_HUMAN TCEA1 Transcription elongation factor A protein 1 138422 93892.9 112697 104757 87030.5 114701 59115.4 91458.9 68075.3 77783.1 106281 40755.2 78519.4 129686 48079.6 116877 94142.5 +P23246 P23246;A0A8Q3WMA7 SFPQ_HUMAN SFPQ Splicing factor, proline- and glutamine-rich 222954 356566 318736 444987 939397 1.03524e+06 516212 761674 927451 907619 337244 719602 849595 897629 500947 604607 665796 900899 330462 446276 +P23284 P23284;A0A7P0TB45;A0A7P0Z497 PPIB_HUMAN PPIB Peptidyl-prolyl cis-trans isomerase B 6.16078e+06 4.37176e+06 8.00235e+06 6.33882e+06 4.55954e+06 4.92005e+06 5.7528e+06 3.54977e+06 3.98919e+06 6.0122e+06 5.57384e+06 5.34533e+06 4.87376e+06 5.01962e+06 5.07392e+06 5.63883e+06 7.53337e+06 6.67183e+06 5.04182e+06 7.62614e+06 +P23381 P23381;H0YJP3 SYWC_HUMAN WARS1 Tryptophan--tRNA ligase, cytoplasmic 217992 228932 141766 182892 238979 204807 329735 158965 193738 202144 237061 148930 231720 195136 309882 266242 187689 227993 321564 356815 +P23396 P23396;E9PL09;E9PPU1;E9PSF4;H0YEU2 RS3_HUMAN RPS3 40S ribosomal protein S3 3.46426e+06 3.67302e+06 3.51368e+06 4.66033e+06 2.50284e+06 2.70131e+06 3.11893e+06 3.51082e+06 2.21182e+06 2.61144e+06 3.29656e+06 3.33169e+06 2.12184e+06 2.49902e+06 2.15965e+06 2.88354e+06 2.25446e+06 2.46981e+06 3.37491e+06 2.51906e+06 +P23434 P23434;A0A1W2PQV2;H3BUH9 GCSH_HUMAN GCSH Glycine cleavage system H protein, mitochondrial 37659.7 16539 23413.4 258391 +P23526 P23526 SAHH_HUMAN AHCY Adenosylhomocysteinase 709896 521514 364277 614627 709788 677655 565529 632727 601856 657062 661694 710590 631096 664451 567731 615660 631888 534020 628656 526933 +P23528 P23528;E9PK25;E9PLJ3;E9PP50;E9PQB7;E9PS23;G3V1A4 COF1_HUMAN CFL1 Cofilin-1 4.51103e+06 3.39079e+06 3.7486e+06 4.56491e+06 4.30977e+06 3.56018e+06 4.49914e+06 4.18524e+06 4.15333e+06 4.72008e+06 4.38142e+06 4.74766e+06 3.40454e+06 3.92397e+06 4.93048e+06 4.40413e+06 4.8224e+06 3.04687e+06 4.59244e+06 5.02093e+06 +P23588 P23588;E7EX17 IF4B_HUMAN EIF4B Eukaryotic translation initiation factor 4B 1.08103e+06 925393 702445 972514 1.72187e+06 1.83376e+06 1.72958e+06 1.35889e+06 1.83479e+06 1.59566e+06 1.40801e+06 1.31565e+06 1.73245e+06 1.52959e+06 1.67031e+06 1.20034e+06 1.28726e+06 1.4438e+06 1.43094e+06 794391 +P23921 P23921;E9PL69 RIR1_HUMAN RRM1 Ribonucleoside-diphosphate reductase large subunit 482342 577989 227126 468193 524694 628549 392527 557662 350258 528351 457302 525416 428001 550742 474769 551807 414774 469178 623606 384224 +P24534 P24534;C9JZW3;F2Z2G2;F8WF65 EF1B_HUMAN EEF1B2 Elongation factor 1-beta 2.21627e+06 1.55568e+06 1.98421e+06 1.45025e+06 3.21554e+06 1.74342e+06 2.75469e+06 1.93894e+06 2.98463e+06 1.86787e+06 2.71661e+06 1.77871e+06 3.15247e+06 1.79967e+06 3.5825e+06 1.82467e+06 2.9637e+06 1.27953e+06 2.79616e+06 1.75489e+06 +P24592 P24592;A0A3B3IUE0;F8VYK9 IBP6_HUMAN IGFBP6 Insulin-like growth factor-binding protein 6 18082 89634.5 116834 32519.3 178641 26618.9 128511 173098 53140.8 167149 179051 62724 154829 114793 140466 +P24666 P24666;D3YTI2;G5E9R5;F2Z2Q9 PPAC_HUMAN ACP1 Low molecular weight phosphotyrosine protein phosphatase 114579 105997 51044.2 65460 40129.2 31662.8 85110.1 48983.2 46048.4 92529.1 65745.3 85224.1 +P24941 P24941;E7ESI2;G3V5T9 CDK2_HUMAN CDK2 Cyclin-dependent kinase 2 55878.5 108570 57490 70719.3 94790.7 36600.1 97351 102140 44943.5 88260 41833.8 91583.7 62002.6 86694.4 39763.3 81616.8 +P25205 P25205;A0A499FHX9;J3KQ69 MCM3_HUMAN MCM3 DNA replication licensing factor MCM3 370979 399079 267386 384329 361563 412875 353565 437378 308295 367212 348205 400116 315627 384268 309206 360911 267542 297869 402417 245839 +P25311 P25311 ZA2G_HUMAN AZGP1 Zinc-alpha-2-glycoprotein 182539 178485 84832.5 18886.5 78925.6 69994.8 320187 90956.7 59938.9 140862 124943 +P25391 P25391 LAMA1_HUMAN LAMA1 Laminin subunit alpha-1 1.43457e+06 1.52473e+06 1.98056e+06 2.63301e+06 1.68813e+06 2.25107e+06 2.46897e+06 2.43098e+06 1.93378e+06 2.34034e+06 2.55796e+06 3.19242e+06 1.79182e+06 2.72542e+06 2.44669e+06 3.73354e+06 1.97332e+06 2.96652e+06 1.73843e+06 4.22817e+06 +P25398 P25398 RS12_HUMAN RPS12 40S ribosomal protein S12 984087 1.17688e+06 857120 1.6255e+06 922699 1.02683e+06 945209 1.02931e+06 724621 937725 984172 1.10913e+06 729976 919196 768763 1.0789e+06 606424 787525 939201 1.15755e+06 +P25685 P25685;A0A7I2V3K7;A0A7I2YQW1 DNJB1_HUMAN DNAJB1 DnaJ homolog subfamily B member 1 24114.8 226838 272716 157719 235446 201652 251263 214635 251875 226093 222809 177108 184857 236895 238871 127160 +P25705 P25705;K7EK77;K7ENJ4 ATPA_HUMAN ATP5F1A ATP synthase subunit alpha, mitochondrial 222340 32299.9 13399.2 +P25786 P25786;B4DEV8;F5GX11 PSA1_HUMAN PSMA1 Proteasome subunit alpha type-1 2.60992e+06 1.86699e+06 2.3633e+06 1.51754e+06 2.33696e+06 2.12563e+06 2.82991e+06 1.62539e+06 2.39564e+06 2.44872e+06 2.54636e+06 1.58618e+06 2.29165e+06 2.095e+06 2.46968e+06 2.61097e+06 2.84053e+06 2.41953e+06 2.36959e+06 1.90954e+06 +P25787 P25787;A0A024RA52;A0A7I2V2H3 PSA2_HUMAN PSMA2 Proteasome subunit alpha type-2 2.20749e+06 1.53399e+06 1.87631e+06 1.23688e+06 1.72378e+06 1.55413e+06 2.42999e+06 1.30196e+06 1.89119e+06 2.02913e+06 1.87009e+06 1.19807e+06 1.81115e+06 1.53717e+06 2.16344e+06 1.8565e+06 2.11496e+06 1.6899e+06 1.84109e+06 1.53002e+06 +P25788 P25788;G3V3W4;G3V5N4 PSA3_HUMAN PSMA3 Proteasome subunit alpha type-3 1.45267e+06 1.13397e+06 1.46597e+06 1.08185e+06 1.42072e+06 1.39306e+06 1.81349e+06 1.07009e+06 1.37326e+06 1.7773e+06 1.62355e+06 1.24664e+06 1.40655e+06 1.3912e+06 1.37201e+06 1.81609e+06 1.79553e+06 1.81266e+06 1.53756e+06 1.28023e+06 +P25789 P25789;H0YKT8;H0YL69;H0YMA1;H0YMZ1;H0YMI6;H0YN18;H0YKS0 PSA4_HUMAN PSMA4 Proteasome subunit alpha type-4 3.02588e+06 2.08266e+06 2.91397e+06 2.11392e+06 2.29587e+06 1.94339e+06 3.03578e+06 1.80176e+06 2.30257e+06 2.76799e+06 2.94153e+06 1.90297e+06 2.37074e+06 1.96154e+06 2.4611e+06 2.66508e+06 3.76044e+06 2.74083e+06 3.07717e+06 2.13311e+06 +P25815 P25815 S100P_HUMAN S100P Protein S100-P 55274.3 132524 64212 148934 31449.2 103257 36569.3 79745.9 46974.5 158283 66107.4 127657 31446.6 109774 132175 132793 111287 50733.5 156256 +P26038 P15311;P26038;P35241;A0A2R8Y5S7;A0A2R8Y7M3;E9PNP4 MOES_HUMAN MSN Moesin 2.3334e+06 2.60514e+06 2.1028e+06 2.21073e+06 2.75254e+06 2.32879e+06 3.35729e+06 1.93998e+06 2.14085e+06 2.28544e+06 2.84822e+06 2.16567e+06 2.29457e+06 2.32652e+06 3.31264e+06 3.29094e+06 2.33925e+06 2.21887e+06 3.18513e+06 2.64157e+06 +P26196 P26196 DDX6_HUMAN DDX6 Probable ATP-dependent RNA helicase DDX6 74718.3 104393 100012 81346.3 129313 74074.4 131716 77802 119933 100306 110468 52824 99585.6 86483.2 89058.6 75537.3 87764.6 121682 121214 +P26358 P26358;A0A7I2V490;A0A7I2V5D0;A0A7I2V5F1;A0A7I2YQA8;A0A7I2YQQ0;A0A7I2V311 DNMT1_HUMAN DNMT1 DNA (cytosine-5)-methyltransferase 1 512750 716441 715336 718649 571512 749167 587377 736852 540280 684208 708042 645329 640758 695913 628089 647234 380886 704720 746182 469293 +P26368 P26368 U2AF2_HUMAN U2AF2 Splicing factor U2AF 65 kDa subunit 108627 104634 90874.5 132083 179189 150537 113867 202555 101410 105522 124256 181371 107382 139163 156606 165491 94919.5 91060.1 151339 37457.3 +P26373 P26373 RL13_HUMAN RPL13 60S ribosomal protein L13 2.20356e+06 1.8365e+06 2.39558e+06 2.37493e+06 1.4443e+06 1.43152e+06 1.67879e+06 1.62539e+06 1.45873e+06 1.20844e+06 1.92329e+06 1.51799e+06 1.31881e+06 1.25519e+06 1.32355e+06 1.37416e+06 1.0103e+06 1.21673e+06 1.66338e+06 1.35674e+06 +P26447 P26447 S10A4_HUMAN S100A4 Protein S100-A4 2.16233e+06 5.26309e+06 2.73967e+06 4.81235e+06 2.0013e+06 4.57451e+06 2.84773e+06 3.5301e+06 2.31171e+06 4.30306e+06 2.21054e+06 3.66687e+06 1.7718e+06 4.6294e+06 2.22268e+06 5.328e+06 2.12203e+06 3.76594e+06 1.9118e+06 5.30491e+06 +P26572 P26572 MGAT1_HUMAN MGAT1 Alpha-1,3-mannosyl-glycoprotein 2-beta-N-acetylglucosaminyltransferase 26704.9 42453.8 34570 20989.8 43051.9 64871 25993.6 37102.6 54484.3 73554 39802.7 36488.8 31174 68497.5 18766.3 +P26583 P26583;D6R9A6 HMGB2_HUMAN HMGB2 High mobility group protein B2 52222.7 42744.4 30334.1 35982.4 35530.5 22969.3 33875.3 34613.6 36196.7 29537.2 9991.6 24192.2 65250.8 +P26599 O95758;P26599;A0A0U1RRM4;A0A6Q8PEX4;A0A7I2V4H6;A0A7I2V621;A6NLN1;K7EKJ7;K7ELW5;K7ES59;X6R242 PTBP1_HUMAN PTBP1 Polypyrimidine tract-binding protein 1 125271 62528.6 56315.3 133585 186786 223430 156606 251923 181554 230055 167376 253373 230744 222183 141613 170699 116434 143735 229014 141948 +P26639 P26639 SYTC_HUMAN TARS1 Threonine--tRNA ligase 1, cytoplasmic 1.55125e+06 1.93243e+06 1.31267e+06 2.07586e+06 1.61463e+06 1.81102e+06 1.51682e+06 1.82303e+06 1.55387e+06 1.64761e+06 1.5867e+06 1.77994e+06 1.37415e+06 1.66066e+06 1.30645e+06 1.74642e+06 1.41563e+06 1.73147e+06 1.30322e+06 1.72829e+06 +P26640 P26640;A0A140T936 SYVC_HUMAN VARS1 Valine--tRNA ligase 215520 299295 101446 223303 256150 311795 244264 343259 184848 268200 237004 245517 211202 252136 186793 290300 138587 244997 326234 202808 +P26641 P26641 EF1G_HUMAN EEF1G Elongation factor 1-gamma 2.23915e+06 1.82867e+06 1.37652e+06 1.42584e+06 2.4757e+06 2.09543e+06 2.13162e+06 1.97228e+06 2.03932e+06 1.86972e+06 2.16501e+06 1.67106e+06 1.90569e+06 1.90335e+06 2.08993e+06 1.67084e+06 1.72284e+06 1.65179e+06 2.35449e+06 924545 +P26885 P26885;A0A494C1N0 FKBP2_HUMAN FKBP2 Peptidyl-prolyl cis-trans isomerase FKBP2 87284.8 108306 158993 113463 88636.2 67806.3 107339 71013.1 68427.3 76620 91208.7 90678 67024 105442 110531 75527 78772.1 110774 +P27105 P27105 STOM_HUMAN STOM Stomatin 75355.6 101642 68895.8 119818 63006.3 90770.4 35709.1 167065 157364 69579.8 108755 52277.7 101958 52308.9 109676 29875 116349 81079.3 +P27348 P27348;E9PG15 1433T_HUMAN YWHAQ 14-3-3 protein theta 1.51065e+06 862654 880515 799344 1.08892e+06 872242 1.07213e+06 729665 986688 906170 1.17921e+06 889085 953091 807199 1.07751e+06 932504 1.14485e+06 695470 1.25834e+06 642809 +P27482 P27482 CALL3_HUMAN CALML3 Calmodulin-like protein 3 193215 17844.5 +P27635 P27635;Q96L21;F8W7C6;X1WI28;H7C123;H7C2C5 RL10_HUMAN RPL10 60S ribosomal protein L10 2.44024e+06 2.42432e+06 2.56653e+06 2.66421e+06 1.7773e+06 2.04191e+06 2.07647e+06 2.1782e+06 1.61338e+06 1.63644e+06 1.97773e+06 2.06534e+06 1.38224e+06 1.60025e+06 1.50546e+06 1.90524e+06 1.22436e+06 1.43176e+06 2.05375e+06 1.36842e+06 +P27694 P27694 RFA1_HUMAN RPA1 Replication protein A 70 kDa DNA-binding subunit 132344 180165 40817.6 170989 170434 158229 161098 205704 141603 151587 142559 201273 144157 132750 137063 155663 81704 214003 209311 162185 +P27695 P27695;G3V359;G3V3C7;G3V3M6;G3V5M0 APEX1_HUMAN APEX1 DNA-(apurinic or apyrimidinic site) endonuclease 643438 553206 667627 656390 570312 453300 548957 436618 533580 539961 515952 499855 465782 546094 575111 623690 568556 566848 540225 396710 +P27707 P27707;D6RCP9;D6RFG8;D6RG38 DCK_HUMAN DCK Deoxycytidine kinase 52313.5 33641.3 45070.4 87166.6 39898.5 75124.5 45852.6 61148.7 81654.3 51916.5 40628.8 46834.7 41807 48962.3 +P27708 P27708;F8VPD4 PYR1_HUMAN CAD CAD protein 350635 250097 154792 215377 367623 254632 278602 252699 304469 197863 310722 249556 255721 231368 270893 254444 203740 223527 321928 219160 +P27797 P27797;K7EL50;A0A7P0T861;K7EJB9 CALR_HUMAN CALR Calreticulin 1.48674e+06 397404 1.36698e+06 533971 824187 556218 1.19085e+06 402194 990042 908815 1.20258e+06 695069 934225 463055 921675 622628 1.7564e+06 680646 1.07728e+06 603547 +P27816 P27816;A0A804HKE7;E7EVA0;H7C4C5;B5MEG9 MAP4_HUMAN MAP4 Microtubule-associated protein 4 236284 247839 271844 319004 342016 309921 317178 282613 294448 321012 296177 250147 378292 255564 290552 246401 298761 259522 407755 245948 +P27824 P27824;A0A7P0T840;A0A7P0T8G1;A0A7P0T937;A0A7P0T9L0;A0A7P0TAE9;A0A7P0TAR9;A0A7P0TB65;A0A7P0Z484;A0A7P0Z4D6;D6RAU8;D6RGY2;A0A7P0T8N1;A0A7P0TAN9;A0A7P0Z4H2;D6RFL1 CALX_HUMAN CANX Calnexin 54630.4 44363 77360.2 41770.8 99591.5 60883.6 25237.6 83513.1 114425 77812.3 110263 72864.1 47486.2 90723.7 139995 83889.9 66987.9 86147.2 +P28065 P28065;A0A0G2JJQ0;A2ACR0;A0A0G2JJA7;A2ACR1 PSB9_HUMAN PSMB9 Proteasome subunit beta type-9 61813.6 45686.4 50086 59044.5 57846.1 55584.3 45469.3 67807.5 104160 64492.1 +P28066 P28066 PSA5_HUMAN PSMA5 Proteasome subunit alpha type-5 1.87924e+06 1.46005e+06 1.66982e+06 1.30891e+06 1.74623e+06 1.54939e+06 2.19728e+06 1.22081e+06 1.7539e+06 1.94062e+06 1.96597e+06 1.2036e+06 1.71086e+06 1.75708e+06 1.91971e+06 1.85111e+06 2.27009e+06 1.78234e+06 1.76492e+06 1.3202e+06 +P28070 P28070 PSB4_HUMAN PSMB4 Proteasome subunit beta type-4 1.07645e+06 729810 982870 632066 927546 731316 1.1555e+06 527986 850804 975738 1.02877e+06 580771 1.17528e+06 769729 729188 1.12616e+06 1.49212e+06 918111 899103 819757 +P28072 P28072;A0A087X2I4;I3L3X7 PSB6_HUMAN PSMB6 Proteasome subunit beta type-6 1.39636e+06 923452 1.70506e+06 920107 1.34909e+06 1.09634e+06 1.65101e+06 914281 1.42521e+06 1.55366e+06 1.46712e+06 853159 1.52668e+06 1.29803e+06 1.65786e+06 1.36902e+06 1.80701e+06 1.55687e+06 1.47148e+06 946876 +P28074 P28074;H0YJM8 PSB5_HUMAN PSMB5 Proteasome subunit beta type-5 1.26969e+06 746951 1.22013e+06 780837 881761 762047 1.23491e+06 627320 893518 1.05389e+06 1.13054e+06 684536 978486 724039 884058 1.03393e+06 1.50991e+06 1.04037e+06 1.12585e+06 978560 +P28482 P27361;P28482;E9PJF0;E9PQW4;B3KR49 MK01_HUMAN MAPK1 Mitogen-activated protein kinase 1 128235 155091 55414.1 156694 147941 163137 180098 211962 67971.3 139227 178843 160014 110702 138850 187193 232321 111109 133787 240265 186987 +P28799 P28799;K7EKL3;K7EQ05 GRN_HUMAN GRN Progranulin 1.68683e+06 1.65517e+06 1.82243e+06 3.12186e+06 1.95452e+06 2.2589e+06 1.80474e+06 2.05058e+06 1.91431e+06 2.9755e+06 1.89437e+06 3.32385e+06 1.97598e+06 3.23203e+06 2.17284e+06 2.68392e+06 1.7432e+06 2.37998e+06 1.51542e+06 2.86731e+06 +P28800 P28800 SWISS-PROT:P28800 SWISS-PROT:P28800 224638 209239 228783 185024 216140 167353 200828 130248 270526 212330 191328 161017 311955 182403 180905 124100 357770 161239 122438 152173 +P28827 P28827;E7EPS8;J3KRV2 PTPRM_HUMAN PTPRM Receptor-type tyrosine-protein phosphatase mu 17044.4 96694.8 39222.7 57928.7 30848 90456.8 25571.5 61900.5 54547.1 17513.9 93400.2 45462.2 35929.1 58984.4 16880.2 34173.9 +P28838 P28838;H0Y9Q1 AMPL_HUMAN LAP3 Cytosol aminopeptidase 3.50623e+06 1.5061e+06 2.25583e+06 1.27124e+06 1.59857e+06 929041 1.70599e+06 702502 1.53885e+06 1.19645e+06 1.67879e+06 849158 1.66171e+06 960681 1.53821e+06 1.14043e+06 2.46647e+06 1.33039e+06 1.73746e+06 1.22833e+06 +P29122 P29122;A0A087WY68;A0A087WZR0;A0A7I2V3M4;A0A7I2V3T2;A0A7I2YQH5;H0Y3Q0;H0YKZ4;H7BXT3 PCSK6_HUMAN PCSK6 Proprotein convertase subtilisin/kexin type 6 11178.1 58732.9 41611.5 34049.4 45374.5 52489.7 10787.6 70071.6 41974.6 52938.3 32828.4 30592.1 35964.6 17088 +P29144 P29144;A0A494C0U1;A0A494C1B8;Q5VZU9;A0A494C0X4 TPP2_HUMAN TPP2 Tripeptidyl-peptidase 2 209000 161453 158119 197393 278758 200559 247028 231390 234142 190303 258268 238938 257358 211216 208943 168241 189675 227604 229140 140039 +P29218 P29218;E5RGY4;E5RHE9;E5RIP7;H0YBL1;E5RG13;E5RG94;E5RI82;E5RIF4 IMPA1_HUMAN IMPA1 Inositol monophosphatase 1 170036 218178 185950 151792 219119 210102 188234 164335 191785 218321 203967 158872 163471 203340 287411 201055 224422 219225 206510 162374 +P29279 P29279 CCN2_HUMAN CCN2 CCN family member 2 320146 100130 516360 206419 610329 170075 521221 143743 583052 181876 513421 180270 715263 181207 617670 171619 549116 206056 428439 185427 +P29373 P29373;Q5SYZ4 RABP2_HUMAN CRABP2 Cellular retinoic acid-binding protein 2 210906 184160 25856.4 179293 125706 242817 26691 297133 143843 137917 277769 +P29401 P29401;E9PFF2;F8W888 TKT_HUMAN TKT Transketolase 7.99763e+06 7.61918e+06 1.25459e+07 7.29541e+06 8.51993e+06 6.1281e+06 9.74586e+06 4.57789e+06 8.65626e+06 6.5753e+06 8.02055e+06 4.30522e+06 8.34739e+06 5.72259e+06 9.10965e+06 6.29519e+06 8.58317e+06 5.93244e+06 6.95485e+06 6.31477e+06 +P29508;P48594 P29508;P48594;C9JZ65;H0Y5H9 SPB3_HUMAN;SPB4_HUMAN SERPINB3;SERPINB4 Serpin B3 46648.7 51092.8 32913 20507.1 113398 68951.2 85123.4 +P29692 P29692;A0A087X1X7;E9PK01;E9PRY8;E9PI39;E9PIZ1;E9PK06;E9PK72;E9PKK3;E9PL12;E9PL71;E9PMW7;E9PN91;E9PPR1;E9PQ49;E9PQZ1;H0YCK7 EF1D_HUMAN EEF1D Elongation factor 1-delta 819834 826501 734496 947945 2.01677e+06 1.96086e+06 1.34986e+06 1.44272e+06 2.11915e+06 1.92217e+06 1.48288e+06 1.24078e+06 2.29448e+06 1.92045e+06 1.33693e+06 1.39108e+06 1.61479e+06 1.82783e+06 1.43413e+06 845669 +P29966 P29966 MARCS_HUMAN MARCKS Myristoylated alanine-rich C-kinase substrate 80998 66592.3 56458.1 98033 75372.6 98868.9 60613 108599 108202 99702.2 82331.1 113732 72620.8 87046.9 74432.5 157188 39514.5 79734.9 21961.3 +P30040 P30040;F8VY02 ERP29_HUMAN ERP29 Endoplasmic reticulum resident protein 29 595446 598564 1.68671e+06 845451 783851 510758 1.26175e+06 578460 723274 627871 985434 659242 669752 535696 997961 907568 727780 608898 690169 915871 +P30041 P30041 PRDX6_HUMAN PRDX6 Peroxiredoxin-6 3.30047e+06 2.58912e+06 3.21531e+06 2.56725e+06 3.10249e+06 2.18556e+06 3.14519e+06 2.09703e+06 2.68746e+06 2.33648e+06 2.94647e+06 2.28834e+06 2.95784e+06 2.34773e+06 3.51234e+06 2.01149e+06 3.08946e+06 2.27414e+06 2.91411e+06 1.92371e+06 +P30044 P30044 PRDX5_HUMAN PRDX5 Peroxiredoxin-5, mitochondrial 47852.1 55532.3 50743.3 43759.6 13333.1 +P30046 P30046;A6NHG4;B5MC82 DOPD_HUMAN DDT D-dopachrome decarboxylase 684227 790533 659469 707268 638116 546887 535438 297909 453987 748386 637647 441407 550723 610011 586106 707295 785289 690792 828702 867128 +P30048 P30048 PRDX3_HUMAN PRDX3 Thioredoxin-dependent peroxide reductase, mitochondrial 284877 217751 460127 314008 322366 268397 369487 219484 321029 243900 305664 257487 305905 263344 390596 211578 166055 116045 149999 180592 +P30050 P30050 RL12_HUMAN RPL12 60S ribosomal protein L12 3.66101e+06 3.62829e+06 3.26834e+06 3.6194e+06 3.04691e+06 3.24912e+06 3.517e+06 3.08038e+06 2.53863e+06 3.94782e+06 3.25412e+06 3.34073e+06 2.90112e+06 3.58557e+06 3.28072e+06 4.12625e+06 2.96591e+06 3.91342e+06 4.10034e+06 3.05891e+06 +P30085 P30085;A0A8V8TMN5 KCY_HUMAN CMPK1 UMP-CMP kinase 199635 274893 241641 197010 297238 122078 191186 88626.8 226197 166002 200160 124194 250039 261128 438164 202751 192352 156711 +P30086 P30086 PEBP1_HUMAN PEBP1 Phosphatidylethanolamine-binding protein 1 3.43447e+06 3.08141e+06 2.97389e+06 2.81733e+06 2.36327e+06 2.21793e+06 2.74741e+06 1.53826e+06 2.33204e+06 2.93736e+06 2.62242e+06 2.02455e+06 2.29051e+06 2.19021e+06 2.96863e+06 2.71272e+06 3.63257e+06 2.96093e+06 3.0097e+06 3.40529e+06 +P30101 P30101;A0A8I5KNU9;A0A8I5KS72;A0A8I5KT88;A0A8I5KW00;A0A8I5KW33;A0A8I5QKQ5 PDIA3_HUMAN PDIA3 Protein disulfide-isomerase A3 2.30066e+06 2.01419e+06 4.12756e+06 2.71359e+06 2.37139e+06 1.69526e+06 2.98284e+06 1.54249e+06 2.40897e+06 2.11941e+06 2.44795e+06 1.85198e+06 2.10579e+06 1.8525e+06 2.4805e+06 2.18494e+06 2.90174e+06 2.2574e+06 2.00866e+06 2.13922e+06 +P30153 P30153;B3KQV6;C9J9C1 2AAA_HUMAN PPP2R1A Serine/threonine-protein phosphatase 2A 65 kDa regulatory subunit A alpha isoform 287525 299685 144971 189532 330507 299009 189740 285919 322678 235035 231452 277445 284700 271641 193319 225566 168501 176010 334131 219486 +P30405 P30405 PPIF_HUMAN PPIF Peptidyl-prolyl cis-trans isomerase F, mitochondrial 124329 104454 93984.9 46158.3 104920 95501.6 75224.6 49169 117060 82987 139869 131777 63320.7 119929 +P30419 O60551;P30419;Q5VUC6;A0A7I2V3U5;A0A7I2V3X4;A0A7I2V539 NMT1_HUMAN NMT1 Glycylpeptide N-tetradecanoyltransferase 1 231453 214554 97196.2 157006 233290 235095 193924 255447 168184 187315 197904 212918 222104 186766 232554 236060 99623.4 160938 275174 115139 +P30520 P30520;Q8N142;G3V232;G3V5D8 PURA2_HUMAN ADSS2 Adenylosuccinate synthetase isozyme 2 1.09988e+06 1.56491e+06 1.03608e+06 1.16053e+06 1.25869e+06 1.39725e+06 1.46838e+06 1.11233e+06 1.12517e+06 1.44467e+06 1.39158e+06 1.06288e+06 1.14882e+06 1.32104e+06 1.46793e+06 1.45996e+06 1.34826e+06 1.49133e+06 1.31226e+06 1.42676e+06 +P30530 P30530 UFO_HUMAN AXL Tyrosine-protein kinase receptor UFO 578699 631487 1.05099e+06 946400 819478 793770 1.06867e+06 757265 1.2076e+06 1.17181e+06 884825 687780 1.08285e+06 999803 1.04479e+06 1.05154e+06 1.06643e+06 1.22373e+06 517201 1.32636e+06 +P30566 P30566;A0A096LNY6;A0A0A6YY92;A0A1B0GTJ7;A0A1B0GWJ0;A0A7P0T8E4;A0A7P0Z472;A0A1B0GTG9;A0A1B0GWF8;A0A7P0T9A7;A0A096LNY4;A0A096LNY5;A0A096LP72;A0A096LP92;A0A1W2PRX2;B4DEP1 PUR8_HUMAN ADSL Adenylosuccinate lyase 357941 289179 274449 308463 377991 334205 380974 315180 360749 343008 392533 302196 359935 348224 403857 365424 348028 323753 414567 260840 +P30622 P30622;F5H0N7;F5H1T5;F5H6A0;J3KP58 CLIP1_HUMAN CLIP1 CAP-Gly domain-containing linker protein 1 42687 41046.6 34418.8 31757.3 31963.8 +P30740 P30740 ILEU_HUMAN SERPINB1 Leukocyte elastase inhibitor 277378 486495 268750 357325 265554 410666 307153 294007 212930 471757 285841 316379 266018 396032 384434 441085 329369 396740 303255 430557 +P31025;Q5VSP4 P31025;Q5VSP4 LC1L1_HUMAN;LCN1_HUMAN LCN1;LCN1P1 Lipocalin-1 15039.9 30702.5 19572.7 26970.4 29461.6 +P31150 P31150 GDIA_HUMAN GDI1 Rab GDP dissociation inhibitor alpha 221505 255161 209167 206806 201621 196853 266559 169377 226090 243922 220717 139917 183236 203399 182798 256304 357318 223518 222300 254858 +P31151 P31151 S10A7_HUMAN S100A7 Protein S100-A7 46312.3 55974.5 40580.6 26845.7 81750.1 23606.4 +P31153 P31153 METK2_HUMAN MAT2A S-adenosylmethionine synthase isoform type-2 309272 464318 302210 443728 529074 544066 469193 420783 513952 600609 396277 409165 500643 533646 463172 512649 486330 541349 358682 442223 +P31327 P31327;A0A669KBF2 CPSM_HUMAN CPS1 Carbamoyl-phosphate synthase [ammonia], mitochondrial 54262.4 33170.7 27720 50988.4 21219.8 48165.7 13914.8 48182.9 37023.6 61757.4 68285.3 +P31350 P31350;A0A286YFD6;A0A7P0SBL1 RIR2_HUMAN RRM2 Ribonucleoside-diphosphate reductase subunit M2 95968 82928.9 76272 128482 121730 88968.7 131954 79563.6 104859 81378.1 100105 390986 65146.1 93249.6 86647.6 173375 131377 161952 +P31431 P31431 SDC4_HUMAN SDC4 Syndecan-4 442972 226486 522813 312876 433779 365775 600539 272936 590934 513749 558219 408486 349175 412519 532569 492994 863694 622039 359314 431189 +P31689 P31689 DNJA1_HUMAN DNAJA1 DnaJ homolog subfamily A member 1 82111.9 159335 122442 132214 157790 182550 141720 219219 136635 142975 137154 137114 134297 140770 98448.9 150245 109012 91005.7 217910 86553 +P31939 P31939;C9JLK0 PUR9_HUMAN ATIC Bifunctional purine biosynthesis protein ATIC 2.66647e+06 2.46361e+06 2.26863e+06 2.37206e+06 3.12858e+06 2.48976e+06 2.47834e+06 2.14917e+06 3.0758e+06 2.34201e+06 2.77194e+06 2.35216e+06 2.85091e+06 2.34924e+06 1.98275e+06 2.36824e+06 2.72052e+06 2.35698e+06 2.58862e+06 2.03137e+06 +P31942 P31942 HNRH3_HUMAN HNRNPH3 Heterogeneous nuclear ribonucleoprotein H3 130495 60069 69115.7 19193.8 97961 132729 24810.5 114537 96543.6 46246.2 71799.8 86698.8 16143.9 +P31943 P31943;E9PCY7;G8JLB6;H0YB39;H0YBD7;H0YBG7 HNRH1_HUMAN HNRNPH1 Heterogeneous nuclear ribonucleoprotein H 275287 229519 183085 241125 518015 647270 239943 269380 611877 760731 192779 299537 508046 564655 301958 242871 513069 600250 300785 174897 +P31944 P31944 CASPE_HUMAN CASP14 Caspase-14 130086 20908.1 38512.4 33546 82148.6 51128.4 59491.5 78732.5 55539.3 92269.3 101394 104621 78141.5 49680.7 231743 72159.5 201745 +P31946 P31946;P61981;A0A0J9YWE8;Q4VY19;Q4VY20 1433B_HUMAN YWHAB 14-3-3 protein beta/alpha 1.21679e+06 1.27186e+06 959467 996392 888511 1.24951e+06 978920 821348 719819 1.14932e+06 935047 998255 860073 1.10098e+06 1.35337e+06 1.22742e+06 1.21801e+06 1.09525e+06 1.27689e+06 1.13017e+06 +P31947 P31947 1433S_HUMAN SFN 14-3-3 protein sigma 5.38577e+06 3.11761e+06 998103 2.96757e+06 1.62585e+06 2.61668e+06 1.33471e+06 2.51481e+06 1.81738e+06 3.34954e+06 1.51427e+06 2.46258e+06 1.76531e+06 2.86622e+06 1.12263e+06 3.47337e+06 1.29177e+06 3.12874e+06 1.24014e+06 3.44888e+06 +P31948 P31948 STIP1_HUMAN STIP1 Stress-induced-phosphoprotein 1 308472 372302 249767 451328 374303 410452 308148 326150 391130 486095 351505 432189 458894 388199 300068 360760 546368 318777 470939 475116 +P31949 P31949 S10AB_HUMAN S100A11 Protein S100-A11 422773 588335 430301 590373 529261 364896 425613 404148 315054 523963 486708 379703 463006 362825 520913 584296 399115 472471 566489 754390 +P32004 P32004;H0Y5C3 L1CAM_HUMAN L1CAM Neural cell adhesion molecule L1 2.38105e+06 1.31084e+06 2.83362e+06 2.06331e+06 2.97758e+06 2.01147e+06 3.85486e+06 1.56047e+06 3.33188e+06 2.37566e+06 3.55105e+06 2.22689e+06 3.46542e+06 2.56243e+06 4.3228e+06 2.81069e+06 3.4354e+06 2.58583e+06 2.23669e+06 2.46904e+06 +P32119 P32119 PRDX2_HUMAN PRDX2 Peroxiredoxin-2 949185 1.01395e+06 620296 973329 860774 911023 809478 849510 795261 1.02992e+06 804924 932451 734599 963302 721335 886047 594209 695542 718823 642070 +P32320 P32320 CDD_HUMAN CDA Cytidine deaminase 21263.2 182770 115363 25520.3 121829 32540 120277 127407 71625.5 93265.9 157780 52445 176714 59227.7 159030 57619.2 135470 +P32754 P32754;A0A0B4J1R4 HPPD_HUMAN HPD 4-hydroxyphenylpyruvate dioxygenase 1.13882e+06 815411 1.40532e+06 659552 1.07857e+06 658420 1.29201e+06 509361 1.1498e+06 783773 1.10335e+06 461125 1.08135e+06 639296 1.17301e+06 720476 1.55429e+06 812605 1.17731e+06 760307 +P32929 P32929 CGL_HUMAN CTH Cystathionine gamma-lyase 484360 151176 474065 165773 389442 119909 428024 95738.7 381996 151456 442651 133156 381948 134908 504863 117638 671805 156628 421463 141967 +P32969 P32969;A0A2R8Y5Y7;D6RAN4;H0Y9R4 RL9_HUMAN RPL9P9 60S ribosomal protein L9 585202 667211 541822 542872 349091 420069 373841 575710 396718 216139 653626 508265 400484 432129 347280 442964 334544 278761 853792 309685 +P33176 O60282;P33176;Q12840;A0A0G2JMZ6;A0A6Q8PEZ8;A0A6Q8PGJ3;A0A7I2V3N5;A0A7I2V3V4;A0A7I2V492;A0A7I2V4K6;A0A7I2V581;J3KNA1 KINH_HUMAN KIF5B Kinesin-1 heavy chain 195612 239191 146524 290995 300660 197813 310229 185580 251158 213758 216869 336609 238431 267955 282068 142247 234013 307936 140245 +P33240 P33240;E7EWR4;A0A0A0MT56;E9PID8 CSTF2_HUMAN CSTF2 Cleavage stimulation factor subunit 2 114850 181338 189018 119687 115970 116405 132346 108140 127692 129002 122640 347474 166412 138142 94240.3 165394 86170.6 118310 681864 +P33316 P33316;H0YKC5;H0YNW5;H0YKI0;H0YMM5;H0YNJ9 DUT_HUMAN DUT Deoxyuridine 5'-triphosphate nucleotidohydrolase, mitochondrial 282457 583471 359372 561103 304233 529900 288655 434382 204010 577460 249839 441782 364349 586378 392970 674615 269211 366327 252657 513077 +P33764 P33764 S10A3_HUMAN S100A3 Protein S100-A3 1.19182e+06 38628.1 12213.6 13083.8 +P33908 P33908 MA1A1_HUMAN MAN1A1 Mannosyl-oligosaccharide 1,2-alpha-mannosidase IA 129168 52819.3 64603.9 116804 176664 104981 194868 75948.5 124258 163111 227758 170483 163448 169934 280054 192643 112379 180890 183839 110379 +P33991 P33991;A0A3B3IT92;A0A3B3ITP6;A0A3B3IU45;A0A8V8TKP2 MCM4_HUMAN MCM4 DNA replication licensing factor MCM4 301614 325534 264137 338901 320988 318583 254115 356846 260764 299589 288364 310413 292817 307482 175402 280072 252796 239998 287806 314374 +P33992 P33992;B1AHB1 MCM5_HUMAN MCM5 DNA replication licensing factor MCM5 241469 340280 229327 295200 291842 393285 252522 376968 200584 265921 247622 314842 288636 304985 225893 315145 147616 257653 325407 174931 +P33993 P33993;C9J8M6 MCM7_HUMAN MCM7 DNA replication licensing factor MCM7 200008 311835 216939 302754 313088 370825 241325 399559 257602 298293 284169 320673 269627 330489 251530 317717 198498 262163 350500 221170 +P34059 P34059 GALNS_HUMAN GALNS N-acetylgalactosamine-6-sulfatase 293593 209336 455653 384938 358027 317560 406834 286028 401856 496431 328378 355810 292807 395192 266952 443556 441499 224724 232262 276184 +P34741 P34741;E7ESK6;E9PBI9;E5RHU3;E5RJB8 SDC2_HUMAN SDC2 Syndecan-2 28982 13309.4 25751.6 40439.1 18805.7 23248.4 33865.2 60762.4 30381.2 23734.3 +P34896 P34896 GLYC_HUMAN SHMT1 Serine hydroxymethyltransferase, cytosolic 72946.6 147194 37957.1 127948 62610.4 118356 65795.9 145321 78521.5 91273.6 85527.8 115952 98763.2 109821 98669.7 166380 49636.6 144501 89567.2 130839 +P34897 P34897;G3V2Y4;H0YIZ0 GLYM_HUMAN SHMT2 Serine hydroxymethyltransferase, mitochondrial 283724 217184 205551 170664 254445 165367 224922 207615 225275 187153 228224 244085 274760 266272 246182 207882 237479 204364 198417 198726 +P34932 P34932 HSP74_HUMAN HSPA4 Heat shock 70 kDa protein 4 1.89582e+06 1.7494e+06 2.34598e+06 1.66783e+06 1.86545e+06 1.5208e+06 2.10594e+06 1.25137e+06 1.87148e+06 1.80809e+06 1.96153e+06 1.32109e+06 2.03575e+06 1.58669e+06 1.7875e+06 1.87489e+06 2.77062e+06 1.86834e+06 2.07929e+06 1.8696e+06 +P34955 P34955 SWISS-PROT:P34955 SWISS-PROT:P34955 1.25817e+06 1.85939e+06 964092 1.03019e+06 1.00451e+06 872165 784478 489716 1.51035e+06 1.00795e+06 936570 750304 2.24401e+06 917301 758913 700131 3.63213e+06 1.00987e+06 718036 1.14152e+06 +P35052 P35052;H7C024;H7C410;C9J4Y6;H7BZE9 GPC1_HUMAN GPC1 Glypican-1 704106 156041 833580 247444 695572 295644 968239 247575 770007 366766 889490 311175 701384 369213 897485 450829 774575 413282 575839 315125 +P35080 P35080;C9J0J7;C9J712;C9JQ45;G5E9Q6 PROF2_HUMAN PFN2 Profilin-2 59722.7 42287.6 38837.5 56815.8 53189.8 +P35221 P35221;G3XAM7 CTNA1_HUMAN CTNNA1 Catenin alpha-1 57040.8 63551.3 87505.3 61682.6 57088.1 48919.3 28990.3 59894 20916.6 53054.1 27612.1 52311.4 40304.3 74414.7 34603.7 +P35222 P35222;A0A2R8Y543;A0A2R8Y5A3;A0A2R8Y5C3;A0A2R8Y5Z1;A0A2R8Y750;A0A2R8Y7Z0;A0A2R8Y804;A0A2R8YCH5;B4DGU4 CTNB1_HUMAN CTNNB1 Catenin beta-1 75064.9 50731 128577 142862 163205 136063 125221 193325 89699.8 364697 162691 58060.6 +P35237 P35237;A0A024QZX5;A0A087X1N8;A0A2R8YD12 SPB6_HUMAN SERPINB6 Serpin B6 933891 1.78105e+06 1.56127e+06 1.57875e+06 1.18366e+06 1.35075e+06 1.35469e+06 1.16275e+06 1.14905e+06 1.84994e+06 1.26976e+06 1.14792e+06 1.39979e+06 1.5087e+06 1.00972e+06 1.91354e+06 1.68687e+06 1.97617e+06 1.19349e+06 2.05458e+06 +P35241 P35241;A0A2R8Y5S7;A0A2R8Y7M3;E9PKN5 RADI_HUMAN RDX Radixin 416914 361766 386067 358859 322046 329163 382463 277404 300380 368597 326484 384358 369123 300223 403255 388935 426652 323133 431558 300524 +P35249 P35249;C9JZI1;C9J8M3 RFC4_HUMAN RFC4 Replication factor C subunit 4 50043.5 83782.4 41513.8 82465.6 67213.2 41926 47535.6 88511.3 77292.7 +P35250 P35250;F8WC37;H7C5A0;H7C5G4;H7C5P1;H7C5Q7;H7C5S7 RFC2_HUMAN RFC2 Replication factor C subunit 2 18956.5 23673.6 51332.1 29742.8 30038.9 29248.3 19587.7 36842.8 +P35268 P35268;K7EKS7;K7ELC4;K7EMH1;K7EP65;K7ERI7 RL22_HUMAN RPL22 60S ribosomal protein L22 764897 507744 834284 732761 591082 471822 640226 575036 750636 385533 809052 575922 474923 446526 450562 529647 477311 528811 674533 329618 +P35318 P35318;E9PL83;E9PML4;E9PQT2 ADML_HUMAN ADM Pro-adrenomedullin 23857.1 85030.7 175424 46507.5 93128.4 22039.5 89084.3 131410 31437.4 80378 61808.1 93160 36798.3 111733 149299 46934.1 106294 +P35520 P35520 CBS_HUMAN CBS Cystathionine beta-synthase 354031 407392 256373 320076 359225 378924 391519 356727 265190 383788 328617 337173 260951 375846 404000 483776 320951 414373 335566 370201 +P35527 P35527;K7EQQ3 SWISS-PROT:P35527 SWISS-PROT:P35527 8.30138e+06 3.15981e+06 5.4998e+06 3.51736e+06 7.01659e+06 6.40952e+06 6.00658e+06 1.07605e+06 5.53432e+06 4.59116e+06 5.85042e+06 2.47927e+07 3.69933e+06 5.6406e+06 3.40045e+06 1.45974e+07 5.13008e+06 5.47012e+06 2.06825e+06 7.39821e+06 +P35573 P35573 GDE_HUMAN AGL Glycogen debranching enzyme 246257 163914 186092 138917 221937 148048 174656 156567 170873 170030 215561 174442 211900 149594 161163 127492 150438 118171 183638 97417.6 +P35579 P35579;A0A8I5KWT8 MYH9_HUMAN MYH9 Myosin-9 565778 638510 403340 633230 648711 809191 599373 782245 592381 787555 653939 765552 579897 766560 583963 861817 490054 752815 746608 516775 +P35580 P35580;A0A8I5KZ38 MYH10_HUMAN MYH10 Myosin-10 46392.9 54756.2 48420.3 65946.9 43417.9 68589.5 40913.3 54976.4 +P35606 P35606;A0A7I2V258;H0Y938;H0YAC7 COPB2_HUMAN COPB2 Coatomer subunit beta' 491476 441490 268069 457898 380138 448539 364809 405329 268344 447791 413495 464345 326811 386378 418243 424119 396631 398886 449904 258573 +P35609 P35609;A0A494C1A0;A0A804HI95 ACTN2_HUMAN ACTN2 Alpha-actinin-2 78490.4 124515 60333 107339 113151 124349 92696.5 91485 127460 98533.1 81327.7 142045 168392 799855 72227.7 96512.1 90232.7 63006.9 +P35613 P35613;A0A087WUV8;A0A7P0T8A7;A0A7P0TA85;A0A7P0TB04;I3L192;R4GMX5;R4GN83 BASI_HUMAN BSG Basigin 430197 485468 640387 588355 657717 538994 666850 455188 930369 571247 769166 542064 736765 693518 570468 569716 709501 627110 576239 487735 +P35625 P35625 TIMP3_HUMAN TIMP3 Metalloproteinase inhibitor 3 32662.5 28584.8 14196.8 40093.6 11148.8 56743.3 18426.2 +P35637 P35637;H3BPE7 FUS_HUMAN FUS RNA-binding protein FUS 32712.4 133890 171140 145803 458606 441046 270984 205813 829229 811380 115336 338902 767650 733529 287364 358216 695338 663094 125505 150453 +P35659 P35659;A0A494C028;A0A494C198;D6R9L5;H0Y8X0;H0Y993 DEK_HUMAN DEK Protein DEK 347346 255082 289541 406040 335643 250398 312297 343997 315593 217464 311157 335882 261170 292208 223736 331773 197906 265825 277706 192375 +P35908v2 P35908;P35908v2 SWISS-PROT:P35908 SWISS-PROT:P35908 2.19456e+07 6.32617e+06 8.03304e+06 3.66587e+06 1.03373e+07 6.36281e+06 7.79038e+06 5.05325e+06 3.79866e+06 4.51608e+06 6.91656e+06 1.95604e+07 1.07332e+07 5.45554e+06 2.95168e+06 1.65588e+07 5.56579e+06 7.04017e+06 4.01641e+06 3.34335e+07 +P35998 P35998;C9JX88 PRS7_HUMAN PSMC2 26S proteasome regulatory subunit 7 370294 426076 212172 328435 381624 432667 297533 403725 238103 381736 338697 351037 288175 336357 276960 307392 228042 335499 407905 176681 +P36578 P36578;H3BM89;H3BTP7 RL4_HUMAN RPL4 60S ribosomal protein L4 2.47771e+06 2.32647e+06 2.35869e+06 2.7341e+06 1.79455e+06 1.81609e+06 2.12917e+06 2.06657e+06 1.45862e+06 1.36697e+06 2.18693e+06 1.74479e+06 1.35687e+06 1.37024e+06 1.66011e+06 1.67856e+06 987981 1.40592e+06 2.17847e+06 1.27288e+06 +P36639 P36639 8ODP_HUMAN NUDT1 Oxidized purine nucleoside triphosphate hydrolase 49278.8 40271.6 28683.9 37458 35340.8 19983.9 36735.8 22076 21052.8 19138.3 35095.4 51411.4 35213.4 83693.4 51069.4 42597.9 58180.5 +P36871 P36871;A0A3B3ITK7 PGM1_HUMAN PGM1 Phosphoglucomutase-1 3.10058e+06 3.58098e+06 2.20285e+06 3.05961e+06 3.02672e+06 3.49123e+06 3.12074e+06 3.23961e+06 2.74345e+06 3.82722e+06 2.9864e+06 3.4077e+06 2.8638e+06 3.54139e+06 3.05859e+06 4.06779e+06 2.80961e+06 3.97626e+06 3.03725e+06 3.97792e+06 +P36873 P36873;F8VR82;F8VYE8;F8W0W8;A0A087WYY5 PP1G_HUMAN PPP1CC Serine/threonine-protein phosphatase PP1-gamma catalytic subunit 134868 155582 242162 232344 222233 192413 243305 217199 213137 168822 230033 208315 296925 224100 298283 170292 195304 213222 233840 204889 +P36952 P36952;C9JLM5 SPB5_HUMAN SERPINB5 Serpin B5 215405 260421 64422.9 201514 60082.4 231364 67884.6 237239 56502.5 182177 56670.9 226901 56051.4 259282 69359.7 255631 35784.2 185826 75945.8 219083 +P37108 P37108;H0YLA2 SRP14_HUMAN SRP14 Signal recognition particle 14 kDa protein 58547.3 25801.3 31663.3 43777.6 30371.8 64497.2 45673.4 30204.2 46657.6 41188.1 42705.3 27086.7 43004.8 51436.3 +P37802 P37802;X6RJP6 TAGL2_HUMAN TAGLN2 Transgelin-2 111599 102665 127770 134868 273695 211678 159106 231353 374262 293637 255628 243347 402642 206739 183285 197954 357072 189795 309049 180903 +P37837 P37837 TALDO_HUMAN TALDO1 Transaldolase 623268 1.53929e+06 885956 1.2277e+06 781377 1.36578e+06 786407 1.28947e+06 745063 1.29597e+06 718669 1.03262e+06 865246 1.20003e+06 822289 1.50942e+06 767070 1.27814e+06 628939 1.41572e+06 +P38159 P38159;Q96E39;A0A1B0GUK8;H0Y6E7;H3BNC1;H3BR27;H3BT71 RBMX_HUMAN RBMX RNA-binding motif protein, X chromosome 309883 376859 442139 459371 437909 492941 426518 343202 411781 421251 370286 450383 349353 401175 374979 380488 602290 342236 434857 344816 +P38571 P38571;A0A0A0MT32 LICH_HUMAN LIPA Lysosomal acid lipase/cholesteryl ester hydrolase 353383 99308.3 200611 247485 322380 163647 277777 130325 252769 172685 305960 339157 187123 680172 159435 171614 165055 +P38606 P38606 VATA_HUMAN ATP6V1A V-type proton ATPase catalytic subunit A 161582 127142 74539.5 108083 170293 136820 129646 173721 161023 137091 122477 110235 138814 126139 153590 124174 56878.9 125942 161297 72174.4 +P38646 P38646;A0A7I2V2G2;A0A7I2V2S7;A0A7I2YQB2;D6RCD7;A0A3B3ITI4;A0A7I2V3F7;H0Y8K0 GRP75_HUMAN HSPA9 Stress-70 protein, mitochondrial 273743 213730 398278 410819 396838 217317 336457 269191 373080 268628 360054 309868 481546 303846 350903 233170 292401 229620 232477 220902 +P38919 P38919;I3L3H2 IF4A3_HUMAN EIF4A3 Eukaryotic initiation factor 4A-III 333208 458595 231355 387159 343755 442070 304030 329586 256683 390618 319770 460080 283251 356342 266659 358569 300291 351235 574206 287407 +P39019 P39019;A0A075B6E2 RS19_HUMAN RPS19 40S ribosomal protein S19 254493 306943 235257 377257 220832 339692 260770 381176 283192 347936 328693 334234 252010 340146 247092 361645 178215 341845 229954 278254 +P39023 P39023;B5MCW2;G5E9G0;H7C3M2;H7C422 RL3_HUMAN RPL3 60S ribosomal protein L3 1.47888e+06 1.44449e+06 1.3153e+06 1.59084e+06 893224 984063 1.22455e+06 1.22154e+06 698583 796166 1.18591e+06 1.21182e+06 818525 806572 739301 1.06518e+06 573572 755868 1.29373e+06 702810 +P39687 P39687;O43423;H0YN26 AN32A_HUMAN ANP32A Acidic leucine-rich nuclear phosphoprotein 32 family member A 769613 467306 605886 626304 535144 483502 640341 388399 575536 553445 680706 552864 561764 493919 566139 572316 390544 530074 809441 310738 +P39748 P39748;I3L3E9 FEN1_HUMAN FEN1 Flap endonuclease 1 19172.3 87941.6 43632.1 53525.3 108083 40075.8 89178.4 59739.6 20688.2 59527.7 32991.8 61345.6 50598.5 44093.7 77641.2 62923.8 36212.1 +P39900 P39900 MMP12_HUMAN MMP12 Macrophage metalloelastase 262155 201334 503524 374518 26134.2 543591 382152 581012 357593 343639 274956 +P40121 P40121 CAPG_HUMAN CAPG Macrophage-capping protein 66685.2 131351 130139 99741.2 107273 85410.6 107991 111104 124639 109031 137662 112006 131678 86986.3 129431 73792.3 75574.4 99459.6 71742.1 +P40222 P40222 TXLNA_HUMAN TXLNA Alpha-taxilin 41081.2 40782.6 51471.1 42163 18783.3 49956.6 41440 45171.6 24101.8 48366.8 18048.6 20009 34445.7 13086.9 21463.1 12502.2 66720.9 36843.3 +P40227 P40227 TCPZ_HUMAN CCT6A T-complex protein 1 subunit zeta 744027 735004 535704 623331 985135 821598 767448 800874 853976 687340 829670 682802 897804 769295 679930 710142 685435 645683 957160 546442 +P40261 P40261 NNMT_HUMAN NNMT Nicotinamide N-methyltransferase 166471 110965 148592 133774 122144 149575 180191 94184.4 150446 89937.7 81249.5 304341 +P40429 P40429;A0A7I2V2L9;A0A7I2V5L3;M0QYS1;Q6NVV1;Q8J015 RL13A_HUMAN RPL13A 60S ribosomal protein L13a 2.48794e+06 2.2736e+06 3.16945e+06 2.97594e+06 1.93456e+06 1.69995e+06 2.23274e+06 2.17321e+06 1.60448e+06 1.5166e+06 2.33721e+06 1.6665e+06 1.43774e+06 1.40656e+06 1.4038e+06 1.59767e+06 1.30869e+06 1.34014e+06 1.98551e+06 1.30488e+06 +P40763 P40763;A0A7I2V2G1;A0A7I2V395;A0A7I2V3V0;A0A7I2V444;A0A7I2V4C8;A0A7I2V4R2;A0A7I2V4R3;A0A7I2V552;A0A7I2V5N9;A0A7I2YQD2;A0A7I2YQI1;A0A7I2YQR5;G8JLH9 STAT3_HUMAN STAT3 Signal transducer and activator of transcription 3 52556.9 71338.5 65772.5 31848.2 76231.1 78414.8 78877.9 43975.9 74876.7 63801.5 72042.5 96954 78608.6 +P40925 P40925;A0A5K1VW95;B8ZZ51;B9A041 MDHC_HUMAN MDH1 Malate dehydrogenase, cytoplasmic 3.44853e+06 3.79149e+06 4.2591e+06 3.32914e+06 3.01714e+06 3.05067e+06 3.79697e+06 2.41544e+06 3.48819e+06 3.46692e+06 3.11431e+06 2.2689e+06 3.47456e+06 3.18117e+06 5.24207e+06 4.07063e+06 4.46482e+06 4.23782e+06 3.23946e+06 4.22504e+06 +P40926 P40926;G3XAL0 MDHM_HUMAN MDH2 Malate dehydrogenase, mitochondrial 1.43784e+06 841478 2.97224e+06 1.16709e+06 1.2579e+06 681898 1.61398e+06 548854 1.45473e+06 891552 1.30858e+06 758963 1.37274e+06 866162 1.24742e+06 894203 1.76677e+06 992361 943860 1.10471e+06 +P40937 P40937;E9PEP3;F5H5S0 RFC5_HUMAN RFC5 Replication factor C subunit 5 52789.7 23833 59300.8 61561.8 39889.1 57379.8 49505.3 33221.1 62474.9 +P41091 P41091;Q2VIR3 IF2G_HUMAN EIF2S3 Eukaryotic translation initiation factor 2 subunit 3 596741 741696 330030 609010 563299 752196 528714 783876 441267 583788 512274 662234 460366 669153 463969 644003 380845 518522 689257 496706 +P41221 P41221 WNT5A_HUMAN WNT5A Protein Wnt-5a 147356 127001 150739 153943 111556 110501 187976 +P41223 P41223;C9JCD9 BUD31_HUMAN BUD31 Protein BUD31 homolog 25481.7 54688.7 67570.1 30845.7 72165.4 41920.1 +P41227;Q9BSU3 P41227;Q9BSU3;A8MWP7;C9JN83;C9JW55;F8W808 NAA10_HUMAN;NAA11_HUMAN NAA10;NAA11 N-alpha-acetyltransferase 10 73454 118515 68978.4 75865.9 123560 120488 80190.3 129502 88907.5 87355.9 141834 89757.8 107184 116098 72287.7 117831 55019.4 104157 125873 83352.7 +P41236 P41236;Q6NXS1;E7EMN6;E7EUI7 IPP2_HUMAN PPP1R2 Protein phosphatase inhibitor 2 28365.2 26170.9 43003.3 44820 29477 41174.3 40371.6 39179.4 41917.2 75099.6 25314.5 74509.1 58827 42644 70175.2 +P41240 P41240 CSK_HUMAN CSK Tyrosine-protein kinase CSK 39301.7 35936.8 44857.1 46654.2 44316.6 50384.6 55295.6 44026.6 52779.6 30233.3 52920.2 40330.1 38438.8 56140.9 51215.4 54638.4 +P41250 P41250;A0A6Q8PFZ6;A0A6Q8PGZ8;A0A6Q8PHI7;H7C443;A0A6Q8PF45;A0A6Q8PGA8;A0A6Q8PGI6;A0A6Q8PGW4;A0A6Q8PH49;A0A6Q8PHH9 GARS_HUMAN GARS1 Glycine--tRNA ligase 479124 481259 163408 419205 415660 485690 317765 469384 358219 397790 370535 446267 365198 451402 396435 387058 279148 391962 398832 382674 +P41252 P41252;A0A0A0MSX9;A0A804HHW9;A0A804HIG2;A0A804HIV9;A0A804HJ36;A0A804HJN6;A0A804HK69;A0A804HKU1;A0A804HL54;J3KR24;A0A804HKW6 SYIC_HUMAN IARS1 Isoleucine--tRNA ligase, cytoplasmic 182312 244658 96461.8 247007 199852 279790 155829 301378 141716 240116 185483 261621 182207 238836 169656 268113 101933 217166 226368 154357 +P41271 P41271;A0A087WTY6;E5RFZ1;A3KFI1;A3KFI2;A3KFI3;A3KFI4;A3KFI5 NBL1_HUMAN NBL1 Neuroblastoma suppressor of tumorigenicity 1 296050 237918 213582 474572 345936 455594 352926 301827 285003 627775 332916 637788 443665 476837 298342 576218 400667 325710 343486 493454 +P41273 P41273 TNFL9_HUMAN TNFSF9 Tumor necrosis factor ligand superfamily member 9 34837.1 64801 28589.8 74385.8 74604.4 63555.2 99653.3 17537.3 65811.2 41436.4 41203.7 26677.4 +P41361 P41361 SWISS-PROT:P41361 SWISS-PROT:P41361 562548 672879 1.27518e+06 784911 659788 468881 757787 463202 879200 616139 721992 489549 875597 563159 358356 618169 945870 730413 340494 706266 +P42126 P42126 ECI1_HUMAN ECI1 Enoyl-CoA delta isomerase 1, mitochondrial 255920 257680 593299 337517 307144 184725 398321 187427 299052 242723 337792 224511 308462 242262 205299 324242 347829 376175 176205 281887 +P42166 P42166;P42167;G5E972;H0YJH7 LAP2A_HUMAN TMPO Lamina-associated polypeptide 2, isoform alpha 182148 267929 245938 383665 229163 397781 220646 462273 205128 382871 183943 382533 183508 363929 231321 349399 228957 329929 213973 244467 +P42224 P42224;J3KPM9;A0A669KB53;A0A669KB56;A0A669KB68;A0A669KBA4;A0A669KBI6;A0A8V8TN81 STAT1_HUMAN STAT1 Signal transducer and activator of transcription 1-alpha/beta 36950.8 29995.5 49474.6 42124.6 20914.5 38271 38872.1 27746.6 18098 19711.7 72036.6 34381.2 21613.5 77951.6 +P42285 P42285;H0YAC4 MTREX_HUMAN MTREX Exosome RNA helicase MTR4 124329 118757 129108 146565 148050 124690 114242 164960 120148 113405 128878 138669 106680 129041 88219.3 88742 91839.2 99594 127494 115996 +P42677;Q71UM5 P42677;Q71UM5;C9JLI6;H0YMV8 RS27L_HUMAN;RS27_HUMAN RPS27;RPS27L 40S ribosomal protein S27 320247 498834 342324 674558 331061 520965 354343 557935 336476 574891 410949 569607 505023 436030 177013 585248 401637 608704 446183 710865 +P42702 P42702 LIFR_HUMAN LIFR Leukemia inhibitory factor receptor 54040.9 56147.4 92245.2 93225.9 90164 94575.6 11280.5 65751.4 35291.3 116507 84067.3 45031.2 +P42765 P42765;A0A0B4J2A4 THIM_HUMAN ACAA2 3-ketoacyl-CoA thiolase, mitochondrial 191879 170020 211540 254195 164926 139097 204939 137770 145853 163979 159840 156344 188501 195713 184607 199213 172420 173815 111618 207524 +P42766 P42766;F2Z388 RL35_HUMAN RPL35 60S ribosomal protein L35 626298 496548 660829 603045 276564 247375 356374 416684 233599 157120 458090 442102 204994 241028 286112 395076 283243 178432 524267 270280 +P42771 P42771;J3QRG6 CDN2A_HUMAN CDKN2A Cyclin-dependent kinase inhibitor 2A 93894.3 176828 125119 121669 99732.1 133366 100549 91439.5 126406 84224.3 72355.5 104858 141115 95877.5 86388.6 121442 132025 +P42785 P42785;A0A7P0Z451;B3KR26;B7Z7Q6;E9PNF7 PCP_HUMAN PRCP Lysosomal Pro-X carboxypeptidase 497511 564140 553258 1.04288e+06 582160 964373 584223 721235 653658 1.1154e+06 556077 1.19863e+06 663052 992347 550324 1.06112e+06 753987 1.09627e+06 377463 1.32262e+06 +P43034 P43034;A0A6Q8PFU3;A0A6Q8PFT2;A0A6Q8PG63;I3L3N5 LIS1_HUMAN PAFAH1B1 Platelet-activating factor acetylhydrolase IB subunit beta 301203 342934 274367 319706 311919 277421 313297 280172 296237 313953 305485 305385 321320 311599 295939 354908 279037 300205 281628 355153 +P43121 P43121 MUC18_HUMAN MCAM Cell surface glycoprotein MUC18 61626.7 80589.3 165001 194150 103667 186207 169513 148407 211179 268726 140759 174623 137858 243381 157212 215749 156349 277826 116382 200298 +P43243 P43243;A0A0R4J2E8;A8MXP9;D6R991;D6REM6 MATR3_HUMAN MATR3 Matrin-3 178457 275712 189840 432270 170514 271999 227718 334722 106101 290335 221884 287694 91810.3 313437 155546 297794 94876.9 305876 205403 253462 +P43246 P43246;A0A2R8Y6P0;A0A2R8YFH0;A0A2R8YG02;E9PHA6;A0A2R8Y7S8 MSH2_HUMAN MSH2 DNA mismatch repair protein Msh2 77210.6 144030 80998.6 118585 111034 140176 100228 156424 110083 124659 98772 116374 104468 125010 63893.7 116308 57275.5 143194 163292 +P43251 P43251;A0A2R8Y5J9;A0A5F9ZH15;A0A5F9ZH57;A0A5F9ZH80;A0A5F9ZH97;A0A5F9ZHE9;A0A5F9ZHF5;A0A5F9ZHR1;C9JSN9 BTD_HUMAN BTD Biotinidase 378666 372638 610502 546007 627905 535194 749274 382857 742547 721398 652866 517794 968049 611882 695388 778062 924032 801629 513920 784153 +P43487 P43487;F6WQW2;C9JDM3;C9JJ34 RANG_HUMAN RANBP1 Ran-specific GTPase-activating protein 409299 422301 340549 591782 527194 529099 493424 416826 408032 581009 525472 507240 447288 539061 532938 433442 948479 478940 560067 448199 +P43490 P43490;A0A7P0T895;A0A7P0T8L3;A0A7P0T9I9;A0A0C4DFS8;A0A7P0T862;A0A7P0T8D2;A0A7P0T931;A0A7P0T941;A0A7P0T973;A0A7P0TA73;A0A7P0TAS5;A0A7P0TBB7;A0A7P0Z466;B7Z8W6 NAMPT_HUMAN NAMPT Nicotinamide phosphoribosyltransferase 736963 735561 378234 689005 716149 698389 544706 630564 558118 673427 660088 695468 611120 690202 669944 617553 576562 697305 613194 512220 +P43686 P43686 PRS6B_HUMAN PSMC4 26S proteasome regulatory subunit 6B 333732 301874 176172 290857 283665 288372 241690 302294 224185 243331 247278 264808 268740 263767 161470 274568 194578 248528 321483 191300 +P45880 P45880;A0A0A0MR02;Q5JSD1;Q5JSD2 VDAC2_HUMAN VDAC2 Voltage-dependent anion-selective channel protein 2 145848 90674.3 86183.2 80965.4 42218.5 +P45973 P45973 CBX5_HUMAN CBX5 Chromobox protein homolog 5 141492 175901 154703 286261 125009 179945 138527 225033 174061 243247 174684 247790 112323 197841 153588 131200 111646 159603 167558 +P45974 P45974 UBP5_HUMAN USP5 Ubiquitin carboxyl-terminal hydrolase 5 251356 255053 108326 207520 222137 269320 169385 238398 135120 234152 171350 200222 225400 233073 229347 170575 147771 222672 235548 113189 +P46013 P46013 KI67_HUMAN MKI67 Proliferation marker protein Ki-67 21551.4 90838.4 123079 21890.2 138237 22646.4 132529 35276 128581 137566 23680.7 126874 15038 97602.5 107013 18709.5 98834 +P46060 P46060;H0Y4Q3 RAGP1_HUMAN RANGAP1 Ran GTPase-activating protein 1 204520 227659 84142.9 155860 203113 278816 151932 272517 170509 222987 164053 218033 175625 239775 143790 198550 110121 210639 235259 101321 +P46063 P46063 RECQ1_HUMAN RECQL ATP-dependent DNA helicase Q1 404151 431933 311069 451331 435719 427049 330554 432890 353317 401703 395225 474472 241149 381199 259910 480097 285338 459606 310972 +P46087 P46087;F5GYR3 NOP2_HUMAN NOP2 Probable 28S rRNA (cytosine(4447)-C(5))-methyltransferase 146523 278115 170523 392917 146829 312183 146463 333147 150238 286559 148361 281414 134392 287418 104743 258740 78987.9 289653 120467 178274 +P46108 P46108 CRK_HUMAN CRK Adapter molecule crk 88541.6 138087 79575.4 126226 123078 110949 158716 90064.2 122493 119282 144997 119862 121375 92582.2 95257.6 139087 91380.5 97628.1 158841 136706 +P46109 P46109 CRKL_HUMAN CRKL Crk-like protein 273278 363596 230338 339953 346335 374949 397138 362875 275437 338822 362176 236997 266042 297310 306735 449699 276251 337978 392904 265703 +P46379 P46379;A0A024RCR6;A0A0G2JJM1;A0A0G2JK23;A0A0G2JL47;A0A7I2V508;A0A7P0MQS5;F6S6P2;X6REW1 BAG6_HUMAN BAG6 Large proline-rich protein BAG6 74602.6 96607.9 85033.8 80469.3 78098 131187 64591 60742.5 74157.1 102070 +P46459 P46459;I3L0N3;I3L2G1;K7EQD6 NSF_HUMAN NSF Vesicle-fusing ATPase 74116.8 59210.8 36837.3 50987.4 85932.3 90957.8 57466.1 30051.5 58799.3 39236.4 60130 99400.9 51577.6 +P46776 P46776;E9PLL6 RL27A_HUMAN RPL27A 60S ribosomal protein L27a 7.15075e+06 6.03039e+06 7.84064e+06 8.33217e+06 3.96865e+06 3.75205e+06 4.8837e+06 5.20614e+06 3.19915e+06 3.21664e+06 5.83894e+06 4.95284e+06 2.7108e+06 3.28881e+06 3.92353e+06 4.23596e+06 2.58081e+06 4.21599e+06 5.64674e+06 4.0947e+06 +P46777 P46777;A0A2R8Y6J3 RL5_HUMAN RPL5 60S ribosomal protein L5 1.07446e+06 1.05788e+06 1.05309e+06 1.02355e+06 1.01004e+06 1.09348e+06 1.1695e+06 935162 1.00435e+06 982514 1.04951e+06 1.05297e+06 872033 1.0082e+06 961243 1.11707e+06 861525 997387 1.12618e+06 815569 +P46778 P46778;G3V1B3;M0R181 RL21_HUMAN RPL21 60S ribosomal protein L21 877071 779619 690248 832632 542878 518736 622568 641496 481070 363552 675775 630972 389344 389112 424058 539407 221327 331578 739183 422397 +P46779 P46779;H0YKD8;H0YLP6;H0YMF4 RL28_HUMAN RPL28 60S ribosomal protein L28 1.29811e+06 1.1089e+06 1.22862e+06 1.29288e+06 797184 770092 1.00608e+06 940854 667310 578328 1.08642e+06 860205 610366 626113 605012 766486 634331 582841 1.10184e+06 593441 +P46781 P46781;B5MCT8;C9JM19 RS9_HUMAN RPS9 40S ribosomal protein S9 4.39954e+06 4.09806e+06 4.54261e+06 5.68777e+06 2.62935e+06 2.93832e+06 3.50816e+06 3.66398e+06 2.55568e+06 2.53141e+06 4.10929e+06 3.83817e+06 2.20238e+06 2.7118e+06 2.35612e+06 3.1745e+06 2.15471e+06 2.43194e+06 3.89897e+06 2.76336e+06 +P46782 P46782;M0R0F0;M0R0R2 RS5_HUMAN RPS5 40S ribosomal protein S5 672645 631543 362071 655470 592317 631281 597839 775392 390568 582993 616584 644047 511581 672781 588839 615516 302519 555211 650458 377760 +P46783 P46783;Q9NQ39 RS10_HUMAN RPS10 40S ribosomal protein S10 241178 267252 305125 283439 219141 277051 237912 283276 217897 276338 250143 295910 148930 289129 224168 252596 142994 313086 210475 157223 +P46926 P46926;D6R9P4;D6RAY7;D6RFF8 GNPI1_HUMAN GNPDA1 Glucosamine-6-phosphate isomerase 1 470159 573874 615240 362709 471421 374397 574996 307505 422058 416951 471396 287894 649966 373262 393317 532536 703529 444932 539227 734893 +P46940 P46940;A0A0J9YXZ5 IQGA1_HUMAN IQGAP1 Ras GTPase-activating-like protein IQGAP1 330709 321149 148462 295094 274711 328521 264132 296127 190642 287574 280404 299879 265111 284416 268725 298998 156845 280085 378606 216406 +P46976 P46976;C9JQ42;G5E9W8 GLYG_HUMAN GYG1 Glycogenin-1 634591 726282 693190 586724 640377 454589 750464 479904 647737 565435 614082 378878 698673 497484 577167 669906 761677 542797 637884 754526 +P47756 P47756;A0A6I8PRV6;B1AK85;B1AK87;B1AK88 CAPZB_HUMAN CAPZB F-actin-capping protein subunit beta 200950 201997 103400 224820 262545 199099 214768 279004 234428 239602 240336 263325 286426 211815 146962 227367 168906 199345 191271 236318 +P47813 P47813;X6RAC9 IF1AX_HUMAN EIF1AX Eukaryotic translation initiation factor 1A, X-chromosomal 845479 790375 589470 585692 627652 685547 876596 651795 550925 682967 723860 581645 365418 664705 620656 989307 884207 628179 868407 748785 +P47895 O94788;P00352;P05091;P30837;P47895;H0Y2X5;H0YMG7 AL1A3_HUMAN ALDH1A3 Aldehyde dehydrogenase family 1 member A3 50056.9 64488 26452.5 36305.6 51671.4 60638.9 60886.6 51410.7 75130 39653.4 42004.7 74035.6 69965.6 +P47897 P47897;A0A1B0GVU9;B4DDN1;H7C0R3 SYQ_HUMAN QARS1 Glutamine--tRNA ligase 288398 151940 83758.7 144217 195092 216837 152682 215107 158626 196142 173290 192336 105446 221551 108545 198639 104748 161345 220727 47609.8 +P47914 P47914;A0A3B3ITT5 RL29_HUMAN RPL29 60S ribosomal protein L29 3.89889e+06 3.12297e+06 5.37512e+06 4.08487e+06 3.58376e+06 2.51837e+06 3.88498e+06 2.70299e+06 3.65198e+06 2.40001e+06 3.49434e+06 2.35671e+06 3.83451e+06 2.6046e+06 3.4549e+06 2.44788e+06 2.92961e+06 2.82655e+06 2.87297e+06 1.99322e+06 +P47929 P47929 LEG7_HUMAN LGALS7B Galectin-7 848037 92149.7 96580.2 108428 569343 117036 790072 179396 305022 332792 +P48147 P48147;A0A499FJL1 PPCE_HUMAN PREP Prolyl endopeptidase 227167 136814 75289.9 134071 152524 137774 115983 138571 127450 133556 156920 197753 106904 162172 152798 136214 139936 139305 138731 82139.7 +P48163 P48163 MAOX_HUMAN ME1 NADP-dependent malic enzyme 476279 505187 434723 491903 345147 356003 400659 238439 366046 392477 421999 321338 401501 375666 424877 441244 520479 472125 484739 510437 +P48444 P48444;B0YIW6 COPD_HUMAN ARCN1 Coatomer subunit delta 232423 338977 121844 267316 265191 326634 203393 375568 276549 340515 267026 294768 279066 323010 195650 275503 208966 278436 329849 196174 +P48506 P48506;A0A2R8Y648;A0A2R8YEL6;B4E2I4;E1CEI4 GSH1_HUMAN GCLC Glutamate--cysteine ligase catalytic subunit 86467.1 125808 119394 89348.8 149660 139064 171626 102126 142825 95078.6 157020 109078 106933 137525 177005 160348 84096.5 86546.6 143992 136662 +P48507 P48507 GSH0_HUMAN GCLM Glutamate--cysteine ligase regulatory subunit 205972 285604 163835 220724 276031 246685 275189 282792 348527 224037 265494 209940 317867 302398 297043 250878 242799 247203 266897 169489 +P48556 P48556;A0A8V8TQ44;R4GMR5 PSMD8_HUMAN PSMD8 26S proteasome non-ATPase regulatory subunit 8 184164 115365 154906 154439 157511 115495 180683 117642 160950 119965 145666 161237 131942 138422 99352.6 83149.2 128103 158242 +P48637 P48637;A0A2R8Y430;A0A2R8YF34 GSHB_HUMAN GSS Glutathione synthetase 363744 903160 306589 841450 317800 739675 380273 585725 371916 854447 301839 538762 356631 703869 450795 917508 509208 855822 324412 897955 +P48643 P48643;B7ZAR1;E9PCA1;E7ENZ3 TCPE_HUMAN CCT5 T-complex protein 1 subunit epsilon 1.04311e+06 972701 670418 806371 1.17009e+06 949570 983596 938639 1.10518e+06 797000 1.01562e+06 852638 1.07929e+06 831580 747670 865377 847042 721184 1.10673e+06 610318 +P48681 P48681 NEST_HUMAN NES Nestin 66626.4 61473.9 44922.4 51580.4 56488.4 62219.7 36774.8 69232.2 66034 +P48723 P48723 HSP13_HUMAN HSPA13 Heat shock 70 kDa protein 13 499566 387803 419802 618221 752396 653790 716221 578758 779184 627237 784128 756110 836896 729598 593613 728888 591436 692489 453988 748904 +P48729 P48729;A0A5F9W493;U3KPX3;A0A590UJ43;D6REM4;E7ETM0;H0Y9X2;U3KQK7 KC1A_HUMAN CSNK1A1 Casein kinase I isoform alpha 123816 77764.1 82084.2 77321.8 71661.7 74378.4 79286 92606 82587.3 29745.6 66058.4 70797 104962 +P48730;P49674 P48730;P49674;A0A669KBC0;H7BYT1;B0QY34;B0QY35;H0Y645;H7C0Y5;H7C3J2 KC1D_HUMAN;KC1E_HUMAN CSNK1D;CSNK1E Casein kinase I isoform delta 132921 +P48739 P48739;B3KYB6 PIPNB_HUMAN PITPNB Phosphatidylinositol transfer protein beta isoform 58348 38809.4 53475.3 48275.8 38783 29345.9 71805.7 39324.1 52573.7 482373 66129.3 30981.5 64837.8 +P48745 P48745 CCN3_HUMAN CCN3 CCN family member 3 1.09175e+06 555009 1.76315e+06 1.09446e+06 1.47508e+06 772112 1.70137e+06 775286 1.59416e+06 826574 1.66739e+06 957945 1.41632e+06 790726 2.24511e+06 1.03348e+06 1.15345e+06 696385 1.19963e+06 998300 +P49005 P49005;A0A087WWF6;A0A8V8TMJ0;A0A8V8TMJ5;A0A8V8TMK0;A0A8V8TN07;A0A8V8TNY7;A0A8V8TPA1;A0A8V8TPA6;A0A8V8TPB6;C9IZD2;C9JLE1;H7C1B3 DPOD2_HUMAN POLD2 DNA polymerase delta subunit 2 22933.8 29511.5 21505.9 24328.8 +P49006 P49006 MRP_HUMAN MARCKSL1 MARCKS-related protein 48076.8 97341.1 109941 84714.6 120904 66971.2 93326.5 92903.5 135279 95662.5 109137 89377.9 160957 83388.8 171600 70445.8 96866.2 85744.1 74976.8 92453.8 +P49189 P49189 AL9A1_HUMAN ALDH9A1 4-trimethylaminobutyraldehyde dehydrogenase 414718 360496 291640 480543 455667 487801 307984 474991 404012 507510 472671 510700 485276 460058 324179 475685 498045 494703 413199 389024 +P49207 P49207 RL34_HUMAN RPL34 60S ribosomal protein L34 2.49838e+06 1.88855e+06 2.40361e+06 2.07857e+06 1.49042e+06 1.33476e+06 2.01961e+06 1.93697e+06 1.23598e+06 972726 2.1589e+06 1.80588e+06 1.19114e+06 1.22907e+06 1.44201e+06 1.52255e+06 1.21754e+06 1.29845e+06 1.95147e+06 1.26756e+06 +P49247 P49247 RPIA_HUMAN RPIA Ribose-5-phosphate isomerase 80171 101028 82468.6 117537 69692.2 86992.2 73329.1 115195 48413.8 70182.5 73119.6 +P49321 P49321;E9PRH9;H0YF33;Q5T624;H0YDS9 NASP_HUMAN NASP Nuclear autoantigenic sperm protein 357870 604850 309029 493930 417253 585263 451881 501136 307342 645919 379806 532191 409941 538921 479284 528455 377974 445365 420655 292988 +P49327 A0A0U1RQF0;P49327 FAS_HUMAN FASN Fatty acid synthase 1.28456e+06 1.34552e+06 595821 1.14543e+06 1.40432e+06 1.44489e+06 1.16903e+06 1.35315e+06 1.2458e+06 1.29681e+06 1.3259e+06 1.14927e+06 1.43165e+06 1.32147e+06 1.20564e+06 1.29047e+06 1.15546e+06 1.25651e+06 1.67541e+06 1.01062e+06 +P49354 P49354;B3KVN2;E9PQP6 FNTA_HUMAN FNTA Protein farnesyltransferase/geranylgeranyltransferase type-1 subunit alpha 27570.9 39255.3 38802.8 53354.4 22926.8 47486.1 28187 40416.6 31700.2 35986.9 32900.8 33881.7 +P49368 P49368;B4DUR8;E9PM09;E9PRC8;Q5SZW8;Q5SZX6;Q5SZX9 TCPG_HUMAN CCT3 T-complex protein 1 subunit gamma 1.15042e+06 1.00253e+06 805902 948433 1.41863e+06 1.10642e+06 1.09441e+06 1.12016e+06 1.21415e+06 988777 1.24245e+06 986979 1.28138e+06 991031 1.0802e+06 1.00474e+06 955080 973226 1.28387e+06 822381 +P49419 P49419;A0A1B0GTJ4;A0A1B0GUA1;A0A1B0GUY0;A0A1B0GV49;A0A1B0GW77;A0A1B0GW82;A0A0J9YWF7;A0A1B0GTG2;A0A1B0GTY9;A0A1B0GVU0;F8VVF2;H0YHM6 AL7A1_HUMAN ALDH7A1 Alpha-aminoadipic semialdehyde dehydrogenase 100367 102175 69633.8 100802 102783 67553.5 104215 69629.6 110961 93246.2 96707.3 103295 93099.2 17144.1 90517.4 38763.4 73252.4 66554 +P49458 P49458;Q659G3;Q6P2S0;E9PE20 SRP09_HUMAN SRP9 Signal recognition particle 9 kDa protein 301418 147399 294456 285169 211611 133202 214468 149883 161862 156338 218038 178664 152346 356144 204836 166177 136678 193979 252512 +P49459 P49459;A0A0D9SG71;A0A0R4J2E5;A0A8Q3SIL6 UBE2A_HUMAN UBE2A Ubiquitin-conjugating enzyme E2 A 29806.6 45084.1 26686.5 32441.9 28132 23061.7 253221 65889.4 35775.2 +P49588 P49588;A0A6Q8PF33;A0A6Q8PF77;A0A6Q8PFK3;A0A6Q8PFY2;A0A6Q8PGE8;A0A6Q8PGN5;A0A6Q8PGR9;A0A6Q8PH44;A0A6Q8PHJ2;A0A6Q8PHP3;A0A6Q8PHP7;H3BPK7;A0A6Q8PG28 SYAC_HUMAN AARS1 Alanine--tRNA ligase, cytoplasmic 376603 478872 170808 416988 420279 484230 320935 507538 359908 466420 386109 496607 405004 492352 294449 459470 383155 505671 436709 280224 +P49589 P49589;B4DKY1 SYCC_HUMAN CARS1 Cysteine--tRNA ligase, cytoplasmic 471359 416305 260664 300236 309752 304783 376535 335230 132284 312253 362733 327594 301181 294503 244676 509182 300840 389307 353236 612484 +P49591 P49591;Q5T5C7 SYSC_HUMAN SARS1 Serine--tRNA ligase, cytoplasmic 124893 183139 58758.3 130608 142586 194327 128275 205167 90628.2 132779 174796 169994 128424 161016 114269 148980 142949 138820 247723 104259 +P49720 P49720;A0A087WUL2;A0A087WXQ8;A0A087WY10 PSB3_HUMAN PSMB3 Proteasome subunit beta type-3 1.20294e+06 736097 1.04456e+06 768411 829498 818266 1.04282e+06 579965 903621 1.0478e+06 1.03543e+06 630592 1.00964e+06 631095 835269 922215 1.44959e+06 903494 923536 942190 +P49721 P49721 PSB2_HUMAN PSMB2 Proteasome subunit beta type-2 1.32362e+06 874303 1.21875e+06 801046 1.08283e+06 974633 1.43729e+06 801696 1.21489e+06 1.36879e+06 1.2424e+06 650747 1.31801e+06 1.03289e+06 1.49172e+06 1.07017e+06 1.72829e+06 1.27783e+06 1.13627e+06 934092 +P49736 P49736;H0Y8E6 MCM2_HUMAN MCM2 DNA replication licensing factor MCM2 634372 589599 557049 659575 560220 476992 453723 634536 443803 484214 599027 662873 487780 478689 441463 497936 392014 439149 544483 472027 +P49748 P49748 ACADV_HUMAN ACADVL Very long-chain specific acyl-CoA dehydrogenase, mitochondrial 149265 +P49773 P49773 HINT1_HUMAN HINT1 Adenosine 5'-monophosphoramidase HINT1 60638.9 54997.7 62132.5 107624 83103.4 140256 88713 72409.8 76468.6 130927 89676.8 97363.3 95655.9 145945 102753 65579 101846 +P49903 P49903;Q5T5U6 SPS1_HUMAN SEPHS1 Selenide, water dikinase 1 123689 97249.7 133616 103192 112994 105925 138757 102814 81275.3 101347 104867 113966 62755.6 97045 95564.3 108841 +P49915 P49915 GUAA_HUMAN GMPS GMP synthase [glutamine-hydrolyzing] 285742 330127 240517 318044 375252 380824 325872 355714 341206 313040 368631 331580 413055 388145 336390 322759 309261 347038 360902 247480 +P49959 P49959;F8W7U8;F5GXT0 MRE11_HUMAN MRE11 Double-strand break repair protein MRE11 40227.2 37402.9 45847.3 35682.2 47116.8 17360.4 50318.5 45571.8 30841.5 47497 39919.2 34267.1 22048.5 59129.6 +P50395 P50395;Q5SX86;Q5SX87 GDIB_HUMAN GDI2 Rab GDP dissociation inhibitor beta 1.84174e+06 2.21356e+06 1.71918e+06 1.74387e+06 1.65002e+06 1.47831e+06 2.12895e+06 1.26375e+06 1.53484e+06 1.59711e+06 1.97679e+06 1.40097e+06 1.64755e+06 1.55341e+06 2.1212e+06 2.17418e+06 2.17867e+06 2.04398e+06 2.31678e+06 2.38625e+06 +P50448 P50448;P05155;A0A087WUD9;A0A7I2V2D2;E9PGN7;H9KV48 SWISS-PROT:P50448 SWISS-PROT:P50448 400251 265935 346714 399978 330569 231449 376097 217594 371272 328685 367730 278974 329605 274311 305396 268721 478091 288973 220264 265831 +P50454 P50454;E9PIG2;E9PJH8;E9PK86;E9PMI5;E9PNX1;E9PPV6;E9PR70;E9PRS3;E9PLA6 SERPH_HUMAN SERPINH1 Serpin H1 1.09395e+06 524760 1.21395e+06 743686 1.05631e+06 490714 915152 563585 1.1467e+06 514103 961898 694867 918573 549440 713620 422439 744661 473275 651947 471312 +P50502 P50502;Q8NFI4 F10A1_HUMAN ST13 Hsc70-interacting protein 363668 391561 240715 365332 588873 652253 430303 580346 556310 622785 479598 555428 630546 649107 394111 473926 503656 531598 611753 349030 +P50552 P50552;K7ENL7;K7ENR7 VASP_HUMAN VASP Vasodilator-stimulated phosphoprotein 22478.9 59519.3 45111.1 75311.6 36473.5 75357.5 78732.3 24953.1 66670.3 93597.4 88370.4 59596.4 85959 57114.3 42512.1 +P50570 P50570;Q9UQ16;A0A8I5KXV5;E5RHK8;H0YBC6 DYN2_HUMAN DNM2 Dynamin-2 39449.7 42854.6 227546 58315.1 92467.4 58089.9 76518.5 55722.9 77898.3 240644 54637.7 71960.4 78095.4 49036.2 65654.6 40951.8 49829.6 82195.8 +P50579 P50579;F8VQZ7;F8VRR3 MAP2_HUMAN METAP2 Methionine aminopeptidase 2 59427.2 41834.2 57583.4 40263.8 71504.5 42590.9 51660.4 30094.3 66214 29474.6 29893.8 59423.9 56400.9 36651.9 77368.7 +P50583 P50583 AP4A_HUMAN NUDT2 Bis(5'-nucleosyl)-tetraphosphatase [asymmetrical] 291191 45204.2 47182.1 56781.8 61184.4 38691.8 48616.6 53213.8 69425.5 86475.9 74669.9 31734.3 66118.1 43712.8 +P50613 P50613;D6R9G1;D6RAD4 CDK7_HUMAN CDK7 Cyclin-dependent kinase 7 19042.3 7950.28 29293.2 36259.9 25646.1 26390.1 18446.2 11132.6 34783.6 27111 39401.4 29487.9 +P50895 P50895;A0A087WXM8;K7ENU8 BCAM_HUMAN BCAM Basal cell adhesion molecule 38401.3 91007.8 69744.9 94085.4 107126 75195 61852.7 77950.1 83939.9 49011.1 +P50897 P50897;A0A286YFF7;A0A2C9F2P4;A0A286YFE3;A0A286YFL8;E9PK48;Q5T0S4 PPT1_HUMAN PPT1 Palmitoyl-protein thioesterase 1 623747 307624 521490 584916 777127 470550 747515 575661 945219 568707 878674 666755 943493 561563 813234 533574 609701 553512 534077 443403 +P50914 P50914;E7EPB3 RL14_HUMAN RPL14 60S ribosomal protein L14 2.73206e+06 2.62597e+06 3.16347e+06 2.94346e+06 2.057e+06 2.09367e+06 2.59469e+06 2.35831e+06 1.73042e+06 1.45191e+06 2.53928e+06 2.07762e+06 1.43392e+06 1.46664e+06 1.3783e+06 2.03329e+06 700973 1.49597e+06 2.11875e+06 1.41924e+06 +P50990 P50990;H7C4C8 TCPQ_HUMAN CCT8 T-complex protein 1 subunit theta 4.2768e+06 3.31518e+06 3.19284e+06 3.29683e+06 4.12844e+06 3.28228e+06 3.34929e+06 3.16419e+06 4.04105e+06 3.10705e+06 3.82001e+06 3.23257e+06 4.3409e+06 3.08528e+06 3.63885e+06 3.0273e+06 3.72823e+06 3.04003e+06 4.36874e+06 2.53355e+06 +P50991 P50991 TCPD_HUMAN CCT4 T-complex protein 1 subunit delta 912732 796185 586673 676855 1.12305e+06 858498 824239 930816 1.01249e+06 757304 1.00964e+06 781398 1.01847e+06 837170 847749 809959 742090 668450 1.04034e+06 619429 +P50995 P50995;H0Y6E1 ANX11_HUMAN ANXA11 Annexin A11 68800.1 26337.2 19590.5 72293.7 46190.4 61970.5 30572.6 58300.3 60913.5 95684.1 42689.3 36453.2 72209.8 102775 48311.1 73124.8 61065.6 68572.9 42096.9 +P51003 P51003;G3XAH6;H0YJL4 PAPOA_HUMAN PAPOLA Poly(A) polymerase alpha 19800 14255.9 25565.4 37906.4 +P51114 P51114;P51116;Q06787;A8MQB8;G3V0J0;A0A087WVL8;A0A087WXI3;A0A087WY29;A0A8I5KS01;A0A8I5KT56;A0A8I5KTT6;A0A8I5KTZ9;A0A8I5KUC2;A0A8I5KWH0;A0A8I5KZ21;B4DXZ6;E7EU85;E9PFF5;A0A8V8TMQ1;A0A8V8TNP6 FXR1_HUMAN FXR1 RNA-binding protein FXR1 258864 263970 140145 217021 270855 275764 218433 269373 175429 309390 258057 249491 243984 260147 242621 235774 140680 268650 296620 193602 +P51148 P51148;K7ENY4;K7ERI8;K7ERQ8 RAB5C_HUMAN RAB5C Ras-related protein Rab-5C 281625 117908 179395 158739 151382 170604 151814 158684 200938 183595 155687 113280 153066 +P51149 P51149;A0A6Q8PG52;A0A6Q8PH84;C9IZZ0;C9J4S4;C9J4V0;C9J592;C9J7D1;C9J8S3 RAB7A_HUMAN RAB7A Ras-related protein Rab-7a 355161 313578 265214 386370 358462 379068 323579 310267 377444 440545 337205 363567 363161 393752 336604 323033 257303 386527 324938 391205 +P51178 P51178 PLCD1_HUMAN PLCD1 1-phosphatidylinositol 4,5-bisphosphate phosphodiesterase delta-1 156201 +P51532 P51531;P51532;A0A2R8Y4P4;A0A2R8Y526;A0A2R8Y6N0;A0A2R8Y6V2;A0A2R8Y7F3;A0A2R8Y7S2;A0A2R8Y7Y7;A0A2R8YF58;A0A2R8YF80;F6VDE0;Q9HBD4;A0A0U1RQZ9;A0A2R8Y440;A0A2R8Y4C5;A0A2R8Y523;A0A2R8Y5K3;A0A2R8YDA1;A0A2R8YG32;A0A2R8YGG3 SMCA4_HUMAN SMARCA4 Transcription activator BRG1 34298.6 31498.7 30820.4 61666.4 51944 78974.1 57218 81927 72435.3 56552.1 79908.2 16324.7 74752.1 41908.6 53755.1 50365.6 52986.3 +P51570 P51570;K7EII7;K7ERJ9;K7ERN9 GALK1_HUMAN GALK1 Galactokinase 56465.9 59069.5 54888 32843.3 107170 40278.2 66251.2 74837.3 20838.1 104397 99326.5 54708.1 88596.8 58432.1 62029 +P51572 P51572;A0A2R8Y5M6 BAP31_HUMAN BCAP31 B-cell receptor-associated protein 31 48439.8 29093.9 16664.2 +P51610 P51610;A6NEM2 HCFC1_HUMAN HCFC1 Host cell factor 1 87928.2 111835 212968 310964 345945 194496 431610 255021 312175 170529 293568 310068 326579 164009 274120 116260 295844 241460 192285 +P51659 P51659;A0A2R8YD50;A0A2R8YDT8;A0A2R8YEG2;A0A2R8YF45;A0A804HIR1;A0A804HK65;A0A804HKT2;A0A804HKU2;A0A804HLJ0;E7EPL9;E7ER27 DHB4_HUMAN HSD17B4 Peroxisomal multifunctional enzyme type 2 273445 55385.3 66628.6 60496.4 71817.1 46977.8 96401.9 16952.4 98077.7 98465.3 65388.4 127621 66737.8 84366.1 55805 49104.3 +P51665 P51665;H3BNT7;H3BQV2;H3BTM8 PSMD7_HUMAN PSMD7 26S proteasome non-ATPase regulatory subunit 7 114297 117484 102006 129927 136096 79487.9 150467 79837.8 82338 72724.9 93987 103174 110576 65299 101766 52843.9 93089.6 120132 37883.5 +P51688 P51688;I3NI22 SPHM_HUMAN SGSH N-sulphoglucosamine sulphohydrolase 396874 317241 534308 494051 445893 348940 540801 371947 529530 440190 542467 355159 573117 377368 627135 428895 642970 420979 298068 498559 +P51692 P51692;A0A8V8TM84;A0A8V8TMP4;A0A8V8TP00;A0A8V8TP16 STA5B_HUMAN STAT5B Signal transducer and activator of transcription 5B 24697.5 19633.2 32043 11683.9 32231.4 +P51858 P51858 HDGF_HUMAN HDGF Hepatoma-derived growth factor 1.13086e+06 1.08298e+06 1.30955e+06 991990 967171 1.15962e+06 1.29844e+06 948265 902900 1.2507e+06 1.17621e+06 999735 846963 1.13346e+06 1.46778e+06 1.44203e+06 1.07003e+06 1.42074e+06 1.23654e+06 970304 +P51965 P51965;Q969T4;Q96LR5;A0A1B0GX65;C9J2P0;R4GND1;A0A087X283;B7Z306;C9J180;H7C061;R4GN77 UB2E1_HUMAN UBE2E1 Ubiquitin-conjugating enzyme E2 E1 90550.1 64471.7 88503.5 51689.3 86251.6 58313 207367 46114.7 101865 +P51991 P51991;A0A7I2V2R3;A0A7I2V402;A0A7I2V4G0;A0A7I2V4J6;A0A7I2V5D8 ROA3_HUMAN HNRNPA3 Heterogeneous nuclear ribonucleoprotein A3 41948.4 28635.4 32075.2 163583 189845 80477.6 51684.6 239426 173875 27676.5 90469.9 122088 189351 87314.5 50592.7 226559 156986 27778.9 +P52209 P52209;K7EM49;K7EPF6;K7EMN2 6PGD_HUMAN PGD 6-phosphogluconate dehydrogenase, decarboxylating 781890 709651 579838 695353 757504 748048 673863 744668 835214 790585 789366 732093 796091 761941 720254 745138 803454 765344 696690 689060 +P52272 P52272;A0A087X0X3;M0R019;M0R0N3;M0R2T0 HNRPM_HUMAN HNRNPM Heterogeneous nuclear ribonucleoprotein M 63340.4 57303.5 72887.9 48203 107927 111366 83916.7 72685.8 108261 85591.8 73881.4 40846.6 65552.8 44698.5 27684.1 192464 54818.9 +P52292 P52292;A0A7I2V351;A0A7I2V487;A0A7I2YQE1;A0A7I2V3Z3 IMA1_HUMAN KPNA2 Importin subunit alpha-1 170755 231248 125940 169106 227965 315876 186669 257358 233234 256663 210229 197523 240817 288848 229159 237389 176236 252319 295765 182551 +P52434 P52434;C9JLU1 RPAB3_HUMAN POLR2H DNA-directed RNA polymerases I, II, and III subunit RPABC3 70074.2 104088 45275.7 72075.9 85279.2 84936.5 115638 87164.7 85033.9 102308 99081.4 78796.7 72097.5 111708 179602 116392 84312.6 97944.2 96601.2 137068 +P52565 P52565;J3KRE2;J3KTF8;J3QQX2 GDIR1_HUMAN ARHGDIA Rho GDP-dissociation inhibitor 1 1.20824e+06 891134 933827 973054 1.02766e+06 809813 1.01758e+06 817880 796102 974233 1.04922e+06 878361 800009 759975 1.04385e+06 1.00936e+06 925714 661652 1.02851e+06 883375 +P52597 P52597 HNRPF_HUMAN HNRNPF Heterogeneous nuclear ribonucleoprotein F 205405 259794 144690 383961 388729 657251 246297 508086 408743 818365 221495 510545 379444 604877 288615 322039 362863 454522 377069 283547 +P52701 P52701;A0A494C0M1;A0A8V8TQJ0;A0A8V8TQW9 MSH6_HUMAN MSH6 DNA mismatch repair protein Msh6 166127 153729 170865 146882 110589 163922 118788 188983 205705 155966 125975 114143 165778 136077 155600 +P52788 P52788 SPSY_HUMAN SMS Spermine synthase 222598 305167 198061 347983 283778 276567 201323 276412 207258 281791 261096 271925 333895 304730 209283 237624 189361 272629 197188 242760 +P52789 P19367;P52789;E9PB90 HXK2_HUMAN HK2 Hexokinase-2 67088.1 47263.6 86148.5 54817.7 36790.8 45890.2 49693.7 47367.7 43006.2 37463 39225 35232.2 40116.9 33039.1 46488.2 50029.9 +P52888 P52888;K7EP46 THOP1_HUMAN THOP1 Thimet oligopeptidase 644498 928879 491402 766999 580855 704437 659566 467902 585951 797246 584444 554547 614153 690239 661021 811528 976493 708464 660336 838961 +P52907 P52907 CAZA1_HUMAN CAPZA1 F-actin-capping protein subunit alpha-1 198474 254110 136839 273599 295454 337830 210770 312870 291420 323215 281423 319920 301850 287785 239603 254235 221485 276175 265958 197515 +P53004 P53004 BIEA_HUMAN BLVRA Biliverdin reductase A 113954 168039 140644 138957 214968 120335 205313 110529 189756 117467 171743 126664 187232 129428 158271 78018 143684 144372 121291 +P53041 P53041;A8MU39;H0YDU8 PPP5_HUMAN PPP5C Serine/threonine-protein phosphatase 5 187473 177877 173820 233781 215548 161831 196059 112316 224382 116983 183139 181388 231242 213526 204284 78001.7 129918 157762 235970 +P53384 P53384 NUBP1_HUMAN NUBP1 Cytosolic Fe-S cluster assembly factor NUBP1 25484.7 59590.3 50642.4 39155.5 70468.7 40113.8 33948 38603.5 +P53396 P53396 ACLY_HUMAN ACLY ATP-citrate synthase 977845 971009 728785 884966 978407 894510 914997 845465 906535 809141 950416 814809 908230 901150 756649 970815 864672 942836 900976 915348 +P53602 P53602;H3BP35 MVD1_HUMAN MVD Diphosphomevalonate decarboxylase 71020.5 81656.1 61468.4 84938.7 30204.9 64370.6 73615.3 94002.2 66045 62530.3 77389.4 34341.9 69192.5 65596.4 85609.4 +P53611 P53611 PGTB2_HUMAN RABGGTB Geranylgeranyl transferase type-2 subunit beta 263068 271553 193054 258369 267366 331200 155555 258101 233489 298721 227294 237976 220744 271271 151805 182686 257277 241734 232893 230937 +P53618 P53618 COPB_HUMAN COPB1 Coatomer subunit beta 761918 964786 252173 742511 867442 1.07841e+06 672628 1.01782e+06 803084 934118 743020 909637 898497 941080 661963 895509 873091 1.16058e+06 843615 561671 +P53621 P53621;A0A3B3IS84;A0A3B3ISC6;A0A3B3IT15;A0A3B3IU89 COPA_HUMAN COPA Coatomer subunit alpha 290024 352079 166634 256567 289705 365005 246337 378878 239263 329649 253997 327908 246763 343043 229235 377978 203059 329406 371733 221033 +P53634 P53634;A0A7I2V2Q8;A0A7I2V466;A0A7I2V6E3;A0A7I2YQT5 CATC_HUMAN CTSC Dipeptidyl peptidase 1 1.55065e+06 798118 1.38806e+06 1.32161e+06 1.39136e+06 1.21421e+06 1.54085e+06 967474 1.76756e+06 1.5848e+06 1.5206e+06 1.58017e+06 1.61316e+06 1.46789e+06 1.40069e+06 1.38017e+06 2.0048e+06 1.63718e+06 1.12167e+06 1.69942e+06 +P53992 P53992;G5EA31 SC24C_HUMAN SEC24C Protein transport protein Sec24C 24055.7 28866 23293.5 35542.7 10534.8 19607.1 +P53999 P53999 TCP4_HUMAN SUB1 Activated RNA polymerase II transcriptional coactivator p15 318181 294134 323091 382562 191458 230386 241386 195854 159824 270644 252258 257531 272315 220580 66432.6 339716 245585 297152 313191 388795 +P54105 P54105;E9PJF4;E9PMI6;J3KN38;E9PQ83 ICLN_HUMAN CLNS1A Methylosome subunit pICln 287126 342002 193851 318359 402458 370598 355670 415456 399843 420514 357520 293967 355909 366508 481067 363171 303488 378175 381419 273633 +P54136 P54136 SYRC_HUMAN RARS1 Arginine--tRNA ligase, cytoplasmic 430304 525951 343536 521640 494233 616592 381429 681463 352445 529882 381148 539962 447423 569548 417393 583466 319850 440661 535838 410040 +P54577 P54577;A0A0C4DGZ5;A0A6Q8PF15;A0A6Q8PFX2;A0A6Q8PFX4;A0A6Q8PFC1 SYYC_HUMAN YARS1 Tyrosine--tRNA ligase, cytoplasmic 166925 281320 107265 185493 204799 369523 131088 325038 161611 310626 163488 244039 161922 305709 138323 265742 147549 282490 249396 179341 +P54578 P54578;A6NJA2 UBP14_HUMAN USP14 Ubiquitin carboxyl-terminal hydrolase 14 216144 318674 212059 243138 363098 260149 285367 303122 305924 250349 303814 248177 321634 270816 188906 335216 277341 229772 324635 189636 +P54652 P54652 HSP72_HUMAN HSPA2 Heat shock-related 70 kDa protein 2 297932 313110 150771 236095 +P54709 P54709;C9JA36;C9JXZ1 AT1B3_HUMAN ATP1B3 Sodium/potassium-transporting ATPase subunit beta-3 46072.3 72792 60974.9 12915.8 77579.4 68119.1 40740.2 85953.5 50979.7 98780.4 38565.4 72263.7 +P54727 P54727 RD23B_HUMAN RAD23B UV excision repair protein RAD23 homolog B 191722 206747 177635 265902 182560 162146 172475 171153 140540 233303 161305 210350 192975 180574 196956 204440 283902 150868 206404 206822 +P54760 P54760;Q96L35 EPHB4_HUMAN EPHB4 Ephrin type-B receptor 4 33707.3 21809.4 58930.1 53114.6 47673.7 46357.7 27932.2 64987.9 43824.6 47322.3 31685.5 55881.4 41817.6 59455.1 33130.6 50053.4 46367.4 28059.2 +P54802 P54802 ANAG_HUMAN NAGLU Alpha-N-acetylglucosaminidase 235491 322297 222406 442425 251382 463646 326718 326480 317332 636432 246949 465331 304876 541572 227243 564990 439957 656093 183160 655694 +P54819 P54819;F8W1A4;A0A5F9ZHP2;F8VZG5;G3V213 KAD2_HUMAN AK2 Adenylate kinase 2, mitochondrial 369017 349477 899960 474818 531886 330614 745802 322652 480029 355842 602499 361757 476745 458766 704026 549484 433548 438637 436811 524933 +P54826 P54826 GAS1_HUMAN GAS1 Growth arrest-specific protein 1 204208 52994.9 224826 130412 253271 95238.6 239629 110503 260785 128489 255915 188383 266812 174622 271365 125595 206962 92813.7 155714 89085.9 +P54868 P54868 HMCS2_HUMAN HMGCS2 Hydroxymethylglutaryl-CoA synthase, mitochondrial 106045 50718.3 40207.6 15450.9 9758.81 +P54920 P54920;M0R027;M0R058;M0R0I4;M0R0Y2;M0R2M1 SNAA_HUMAN NAPA Alpha-soluble NSF attachment protein 58300.9 26435.4 28663.3 29628 27773.7 39586.3 66194.5 44248.5 30421.3 32960.1 34325.9 55795.6 36322.7 44298.1 +P55010 P55010 IF5_HUMAN EIF5 Eukaryotic translation initiation factor 5 191329 166498 73729.2 122392 203125 200182 191897 157005 207747 145784 210335 183776 177760 189431 213502 200418 181149 212969 248845 94108.9 +P55036 P55036;Q5VWC4;H0Y3Y9;H0Y561 PSMD4_HUMAN PSMD4 26S proteasome non-ATPase regulatory subunit 4 141280 160086 87643.1 201616 178786 153316 162306 152412 103103 187289 133058 156737 175036 149816 148621 133346 113655 125746 179821 217709 +P55060 P55060 XPO2_HUMAN CSE1L Exportin-2 545890 579945 228251 398519 587776 620863 433418 617531 564366 540508 527911 494521 548808 546321 429740 500063 462347 539905 659666 357386 +P55072 P55072;C9JUP7;A0A7I2V540;A0A7P0T8A3;A0A7P0T8Q5;A0A7P0TAW3;A0A7P0TA22 TERA_HUMAN VCP Transitional endoplasmic reticulum ATPase 2.44723e+06 2.59262e+06 2.07217e+06 2.23056e+06 2.1946e+06 2.43001e+06 2.14892e+06 2.17066e+06 1.96158e+06 2.76041e+06 2.19796e+06 2.33784e+06 1.92094e+06 2.64816e+06 1.75967e+06 2.4192e+06 1.91365e+06 2.36948e+06 2.09082e+06 2.24066e+06 +P55145 P55145 MANF_HUMAN MANF Mesencephalic astrocyte-derived neurotrophic factor 334175 258690 746528 370157 442172 174626 693552 270606 393979 266523 603173 246685 339666 250083 735845 367119 426832 401771 481433 295919 +P55209 P55209;B7Z9C2;F5H4R6;F8VUX1;F8VV59;F8VXI6;F8VY35;F8W020;F8W0J6;F8W118;F8W543;H0YH88;H0YHC3;H0YIV4 NP1L1_HUMAN NAP1L1 Nucleosome assembly protein 1-like 1 261412 254741 143489 191284 308956 286691 284468 292354 266112 208142 324744 263975 249768 231267 235534 258952 232616 189728 386536 92256.6 +P55212 P55212 CASP6_HUMAN CASP6 Caspase-6 35132.2 63120.9 48057 76578.6 72452 62456.5 67652.8 47714 45822.4 57256 62047.1 105157 49406.5 28447.3 +P55263 P55263;A0A5F9ZHJ1 ADK_HUMAN ADK Adenosine kinase 13883.5 59366 17317.3 18368.2 16919.6 +P55268 P55268 LAMB2_HUMAN LAMB2 Laminin subunit beta-2 137413 220969 227003 383968 207245 338973 239632 354338 314850 401134 299560 497273 247377 448552 225832 509247 175976 424370 149054 431689 +P55327 P55327;E5RK35;H0YC42 TPD52_HUMAN TPD52 Tumor protein D52 183647 265295 463716 506239 400188 296321 438012 437596 435597 406041 366052 383855 301348 352792 490080 404468 311507 306537 311325 346931 +P55735 P55735;A8MXL6 SEC13_HUMAN SEC13 Protein SEC13 homolog 115827 96107.7 72531.6 107408 112527 122723 94260.6 146085 90211.4 143603 101001 134589 97757.9 121096 98381.4 93061.8 99407.5 97201.7 72800.8 +P55786 P55786;A6NEC2;A0A7I2V389;A0A7I2V3W8;E9PLK3;A0A7I2V604;A0A8V8TQ95;E9PPZ2 PSA_HUMAN NPEPPS Puromycin-sensitive aminopeptidase 558810 422963 393629 382544 492634 398449 471797 346310 476632 389191 458139 375132 439821 402868 488195 409587 558795 392457 450481 396746 +P55809 P55809;E9PDW2 SCOT1_HUMAN OXCT1 Succinyl-CoA:3-ketoacid coenzyme A transferase 1, mitochondrial 169748 245305 620396 328223 274639 192724 345150 170720 337773 273155 293889 184039 265348 233889 220503 263010 284699 258310 165720 195501 +P55884 P55884 EIF3B_HUMAN EIF3B Eukaryotic translation initiation factor 3 subunit B 919988 723001 515402 687589 800794 775077 747724 672014 706739 608311 755087 659097 713707 655052 590941 664337 551853 537925 797424 510367 +P55957 P55957 BID_HUMAN BID BH3-interacting domain death agonist 33548 26948.6 59075.3 17992.9 27873.3 15730.4 52660 +P56192 P56192;H0YIP0 SYMC_HUMAN MARS1 Methionine--tRNA ligase, cytoplasmic 145549 199764 209847 142083 258633 138806 210194 192305 202440 133520 220577 142734 223903 115890 211391 156786 195727 192621 +P56537 P56537;A0A0U1RQV5 IF6_HUMAN EIF6 Eukaryotic translation initiation factor 6 283681 674448 439523 1.09157e+06 387975 626847 373526 602948 389303 991881 306496 708605 406189 613714 406805 621764 482351 457336 262889 639969 +P57678 P57678;I3L2C7 GEMI4_HUMAN GEMIN4 Gem-associated protein 4 62709.2 87534 29315.4 111632 51520 93380.7 100450 65248.5 40609.3 77276.6 41355.4 83208 47975.7 75733.1 50916.5 41009 68828.8 43730.2 +P58107 P58107;A0A075B730 EPIPL_HUMAN EPPK1 Epiplakin 33134 41609.3 46158.5 24528.4 41196.7 39392.5 33929.5 54805.6 52681.2 31339.4 30739.7 +P58546 P58546;C9JL85 MTPN_HUMAN MTPN Myotrophin 148056 393155 123034 354703 157475 313977 186222 258861 144075 469478 169813 215540 147665 286173 213541 414844 216897 416873 192045 436523 +P59990 P59990;P60328 KR121_HUMAN KRTAP12-1 Keratin-associated protein 12-1 220716 +P59998 P59998;A0A0A6YYG9;F8WCF6;F8WDD7;H7C0A3 ARPC4_HUMAN ARPC4 Actin-related protein 2/3 complex subunit 4 616554 468029 474820 492763 518202 399740 581585 337923 522491 418433 621813 399793 540481 417940 586740 522637 615638 415185 633888 506720 +P60014 P60014 KR10A_HUMAN KRTAP10-10 Keratin-associated protein 10-10 902621 +P60174 P60174 TPIS_HUMAN TPI1 Triosephosphate isomerase 7.95236e+06 8.03609e+06 8.06744e+06 7.01433e+06 6.60804e+06 6.68447e+06 7.79703e+06 4.14917e+06 5.85598e+06 7.62337e+06 6.47113e+06 5.2872e+06 5.93269e+06 6.59056e+06 9.53783e+06 7.67458e+06 8.42144e+06 6.74645e+06 6.43115e+06 6.97854e+06 +P60228 P60228;A0A7I2V2H9;A0A7I2V2W0;A0A7I2V3I0;A0A7I2V3I2;A0A7I2V3S3;A0A7I2V4B4;A0A7I2V4G3;A0A7I2V5Z6;B3KW56;E5RGA2;H0YBP5;A0A161SXE1;A0A7I2V2Q9;A0A7I2V3W9;A0A7I2V429;A0A7I2V570;A0A7I2V5K7;H0YBR5 EIF3E_HUMAN EIF3E Eukaryotic translation initiation factor 3 subunit E 438523 448484 267747 316441 469801 512470 451990 431569 467315 432282 402537 364322 449258 508002 298465 386346 320611 464039 562839 206301 +P60369 P60369 KR103_HUMAN KRTAP10-3 Keratin-associated protein 10-3 162500 +P60372 P60372 KR104_HUMAN KRTAP10-4 Keratin-associated protein 10-4 152042 +P60411 P60411 KR109_HUMAN KRTAP10-9 Keratin-associated protein 10-9 253542 +P60412 P60412 KR10B_HUMAN KRTAP10-11 Keratin-associated protein 10-11 525702 +P60413 P60413 SWISS-PROT:P60413 SWISS-PROT:P60413 682577 +P60660 P60660;B7Z6Z4;F8VPF3;F8VZU9;F8W180;F8W1R7;G3V1V0;G3V1Y7;G8JLA2;J3KND3 MYL6_HUMAN MYL6 Myosin light polypeptide 6 1.07784e+06 1.30168e+06 1.1822e+06 1.38013e+06 1.26953e+06 1.51165e+06 1.18975e+06 1.14703e+06 1.17872e+06 1.5224e+06 1.31436e+06 1.35416e+06 1.40607e+06 1.30924e+06 1.02942e+06 1.39659e+06 1.01684e+06 1.13789e+06 1.44034e+06 1.04691e+06 +P60842 P60842;J3KS25;J3KT12;J3KTB5;J3QKZ9;J3QL43;J3QLN6;J3QR64;J3QS69;J3KSZ0 IF4A1_HUMAN EIF4A1 Eukaryotic initiation factor 4A-I 1.83857e+06 2.11518e+06 618738 1.40829e+06 1.81136e+06 2.26824e+06 1.34536e+06 1.90009e+06 1.53352e+06 1.92412e+06 1.54107e+06 1.57392e+06 1.90158e+06 1.9309e+06 1.56379e+06 1.82785e+06 1.51433e+06 1.71336e+06 2.61376e+06 1.42601e+06 +P60866 P60866;A0A7P0S5H5;E5RJX2 RS20_HUMAN RPS20 40S ribosomal protein S20 969470 895230 778004 975665 836055 888516 913033 909187 669151 761311 945565 906683 604397 824882 742373 752386 513017 716266 889119 510873 +P60891 P11908;P60891;A0A2R8Y7H4;A0A6Q8PEU0;A0A6Q8PG82;B1ALA9 PRPS1_HUMAN PRPS1 Ribose-phosphate pyrophosphokinase 1 363034 482355 235409 296831 551010 609214 395525 598841 301436 416630 295307 475032 331504 379662 415108 387006 364936 374937 616134 266339 +P60900 P60900;G3V295;G3V3I1;G3V5Z7 PSA6_HUMAN PSMA6 Proteasome subunit alpha type-6 2.96173e+06 1.81868e+06 2.90493e+06 1.79181e+06 2.36986e+06 2.07835e+06 2.79555e+06 1.68305e+06 2.54661e+06 2.67435e+06 2.75933e+06 1.61901e+06 2.46289e+06 2.1919e+06 3.19064e+06 2.47957e+06 2.97443e+06 2.94759e+06 2.49772e+06 2.10654e+06 +P60953 P60953 CDC42_HUMAN CDC42 Cell division control protein 42 homolog 59523.7 61979.6 55803 43745.5 66145.9 57470.1 76542.1 60282.8 63436.8 73869.8 90546.1 75495.7 19580.9 78910.3 41689.2 +P60981 P60981;F6RFD5 DEST_HUMAN DSTN Destrin 339561 309039 417066 377972 377639 242524 453082 275468 339095 362685 512338 280795 313900 285950 499964 278725 397497 285199 341378 335585 +P61019;Q8WUD1 P61019;Q8WUD1;A0A3B3ITL1;E9PE37;E9PKL7;H0YD31 RAB2A_HUMAN;RAB2B_HUMAN RAB2A;RAB2B Ras-related protein Rab-2A 142102 72560.6 60735.2 47866.4 79338.8 59132 20203.9 44763.8 81378.2 103732 35446.8 76305.9 52652.3 53180.3 57315.2 65197.7 +P61024 P61024;Q5T179 CKS1_HUMAN CKS1B Cyclin-dependent kinases regulatory subunit 1 21386.6 15957.4 12233.6 24315.6 24690.5 +P61026 O95716;P20336;P20337;P20338;P61006;P61018;P61026;P61106;P62820;Q15286;Q15771;Q6IQ22;Q86YS6;Q92930;Q96AX2;Q96DA2;Q96E17;Q9H0U4;Q92928;X6RFL8;A0A3B3ITT1;A8MSP2;A8MTC6;A8MZI4;B7Z3L0;C9JFM7;E9PI18;E9PJQ5;E9PMJ1;E9PNB9;E9PRF7;E9PS06;F5H157;H0YDK7;H0YMN7;H0YNE9;M0R0X1;M0R257;Q6PIK3 RAB10_HUMAN RAB10 Ras-related protein Rab-10 480793 171939 152085 204671 220609 218923 179433 168035 243681 171461 299745 250821 413170 244072 351782 246522 174676 184469 254041 200239 +P61077;P62837 P61077;P62837;A0A087WY85;D6RAH7;D6RFM0;H9KV45 UB2D2_HUMAN;UB2D3_HUMAN UBE2D2;UBE2D3 Ubiquitin-conjugating enzyme E2 D3 159651 94303.2 108485 188166 136584 199968 137952 150435 164392 132000 139585 511174 139574 123434 +P61081 P61081 UBC12_HUMAN UBE2M NEDD8-conjugating enzyme Ubc12 49988.8 39655.8 45319 56922.7 54001.4 51893.3 69881.9 36878.3 55879.8 45875.9 56220.4 66766.8 37284 43303 81487.7 95740 113128 54063 +P61086 P61086;D6RDM7 UBE2K_HUMAN UBE2K Ubiquitin-conjugating enzyme E2 K 188452 92572.6 155581 51617.7 136115 71814.9 154296 74721.8 94453.7 61705.4 290784 149665 58578.6 311687 29405.1 151101 62939.3 +P61088 P61088;F8VSD4;F8VV71;F8VZ29 UBE2N_HUMAN UBE2N Ubiquitin-conjugating enzyme E2 N 257909 246555 82331.9 191970 170721 183732 197137 179369 135993 177374 206432 149038 139280 160925 217089 194423 185987 121603 394058 142490 +P61106 P61106;X6RFL8 RAB14_HUMAN RAB14 Ras-related protein Rab-14 43013 20850.1 13355.4 14976.9 18570.9 +P61158 P61158;B4DXW1 ARP3_HUMAN ACTR3 Actin-related protein 3 472441 321253 307998 315114 413850 294631 409406 281745 352825 281598 432935 340846 369011 321185 333446 371943 374201 331321 484203 408015 +P61160 P61160 ARP2_HUMAN ACTR2 Actin-related protein 2 816777 624940 472641 564715 778477 587278 775771 617579 715702 517163 740313 535864 623608 509424 469863 662916 641428 609174 854403 556450 +P61163 P61163;A0A1B0GVS3;R4GMT0 ACTZ_HUMAN ACTR1A Alpha-centractin 120459 132340 133370 195761 240646 187550 224494 103948 151785 112917 148006 293093 190921 164867 187246 100560 172237 212690 201248 +P61201 P61201;B4DIH5;H0YKU5;H0YM03 CSN2_HUMAN COPS2 COP9 signalosome complex subunit 2 54535.5 35745.1 36951.2 37036.7 25387.6 31739.1 37039.7 45507 39861.2 47549.6 48755.9 28124.4 39842.2 33285.1 67815.4 +P61218 P61218;U3KQB0;F8WC47;U3KPY1;U3KQS8;B0QYL8;B0QYL9 RPAB2_HUMAN POLR2F DNA-directed RNA polymerases I, II, and III subunit RPABC2 58084.9 32339.3 37312.8 65083.9 48262.9 80848.8 62707.2 53287.9 71500.6 76258.4 51345.6 59225.8 66921.8 59011.2 +P61221 P61221;D6R9I9;D6RGF4 ABCE1_HUMAN ABCE1 ATP-binding cassette sub-family E member 1 34956.2 50965.4 40243.5 47815.5 34844.7 52461.5 19100.2 47216.2 35106.4 34433.1 44214.3 48938.8 61432.5 +P61224;P62834 P61224;P62834;A6NIZ1;E7ESV4;F5GWU8;F5GX62;F5GYB5;F5GYH7;F5H004;F5H077;F5H0B7;F5H491;F5H4H0;F5H500;F5H6R7;F5H7Y6 RAP1A_HUMAN;RAP1B_HUMAN RAP1A;RAP1B Ras-related protein Rap-1b 115674 279440 176495 350106 164763 351781 202595 511624 199418 422320 226866 549139 305193 365755 252820 362245 321391 308903 288058 +P61247 P61247;D6RAT0;E9PFI5;D6RAS7;D6RB09;D6RED7;D6RG13;H0Y8L7 RS3A_HUMAN RPS3A 40S ribosomal protein S3a 2.89708e+06 2.69814e+06 2.65519e+06 3.2352e+06 2.0002e+06 2.06133e+06 2.2051e+06 2.38879e+06 1.81426e+06 1.85694e+06 2.31371e+06 2.31873e+06 1.53355e+06 1.8984e+06 1.3647e+06 2.24661e+06 1.35561e+06 1.74755e+06 2.36817e+06 1.69342e+06 +P61254 P61254;J3QQQ9;Q9UNX3;E5RHH1;E5RIT6;J3KSS0;J3QQV1;J3QRI7 RL26_HUMAN RPL26 60S ribosomal protein L26 1.47119e+06 1.31155e+06 1.48823e+06 1.67052e+06 1.03784e+06 1.07404e+06 1.13672e+06 1.16371e+06 872791 947021 1.2803e+06 1.10204e+06 948694 981237 639059 1.02731e+06 751437 1.02656e+06 1.24803e+06 680260 +P61289 P61289;B3KQ25;K7ESG5;K9J957 PSME3_HUMAN PSME3 Proteasome activator complex subunit 3 344899 339412 99786.6 369864 316727 365738 278911 349272 257635 360053 277598 296142 234471 338895 295325 229296 155340 268957 331047 243304 +P61313 P61313;A0A2R8Y738;A0A2R8YEM3;E7ENU7;E7EQV9;E7EX53 RL15_HUMAN RPL15 60S ribosomal protein L15 3.27525e+06 2.68521e+06 3.28365e+06 3.65853e+06 2.06347e+06 2.15423e+06 2.53983e+06 2.55918e+06 1.57437e+06 1.87068e+06 2.74288e+06 2.61473e+06 1.8082e+06 2.01996e+06 2.1682e+06 2.05292e+06 1.45392e+06 1.79243e+06 2.41554e+06 1.20781e+06 +P61326;Q96A72 P61326;Q96A72;F5H3U9;F5H6N1;F5H6P7 MGN2_HUMAN;MGN_HUMAN MAGOH;MAGOHB Protein mago nashi homolog 138795 211075 151420 153141 129140 235110 149899 193022 84469.2 215781 149785 187530 109647 185881 75566.2 267853 161223 170895 183654 291892 +P61353 P61353;K7ELC7 RL27_HUMAN RPL27 60S ribosomal protein L27 5.92926e+06 4.58777e+06 4.08649e+06 5.45669e+06 3.51521e+06 3.19155e+06 4.03299e+06 3.93752e+06 2.31831e+06 2.30546e+06 3.95979e+06 4.15074e+06 2.29007e+06 3.14586e+06 2.64708e+06 3.86536e+06 1.75877e+06 2.6072e+06 3.96005e+06 2.37758e+06 +P61457 P61457 PHS_HUMAN PCBD1 Pterin-4-alpha-carbinolamine dehydratase 417767 389134 545743 357823 523203 342320 581961 285741 564940 429748 508324 255747 519719 439871 874747 410692 523056 365641 436281 321504 +P61513 P61513;A6NKH3;C9J4Z3;E9PEL3;G5E9R3;M0R0A1;M0R2L6;Q6P4E4 RL37A_HUMAN RPL37A 60S ribosomal protein L37a 429113 327359 580747 450309 250199 184694 459110 373813 226842 142906 542160 336529 155574 379413 326688 106867 185259 401518 159912 +P61586 P61586;A0A7I2YQV1 RHOA_HUMAN RHOA Transforming protein RhoA 182364 169994 162054 210320 215819 174884 215681 186229 247573 230260 283787 229116 221168 211961 167687 216906 207963 202912 250847 183316 +P61599 P61599 NAA20_HUMAN NAA20 N-alpha-acetyltransferase 20 55392.2 99246.5 74766.2 35051.7 45157.2 49259.8 47948 28456.2 60102.4 65706.3 32110.3 48325.3 68340.2 113090 90103.1 90128.5 +P61604 P61604;B8ZZL8;S4R3N1 CH10_HUMAN HSPE1 10 kDa heat shock protein, mitochondrial 508229 321187 1.09083e+06 480311 800351 377799 867048 338125 781246 417060 704922 421860 741023 416005 847249 358394 517019 229330 482162 358570 +P61626 P61626 LYSC_HUMAN LYZ Lysozyme C 254279 26725.7 88356.2 33291.4 45022.2 76338.2 40675.8 195145 41257.8 146788 91253.6 51070.4 249665 43714.1 51152.3 64454.2 20986.2 +P61758 P61758 PFD3_HUMAN VBP1 Prefoldin subunit 3 106021 105892 73146.5 97697.8 134950 98172.4 137661 101985 85973.6 80567.8 129316 83498.3 117932 102010 196340 94086.6 98552 129043 +P61764 P61764;A0A0D9SG72 STXB1_HUMAN STXBP1 Syntaxin-binding protein 1 89574.6 32132 51120.2 30477.2 51251 53625.8 19636.6 121299 82530.8 +P61769 P61769;F5H6I0;H0YLF3 B2MG_HUMAN B2M Beta-2-microglobulin 1.04172e+07 1.05492e+07 4.74759e+06 1.89481e+07 4.30414e+06 1.1905e+07 5.93511e+06 7.13529e+06 6.26339e+06 2.23111e+07 7.41801e+06 2.54314e+07 5.70367e+06 1.46368e+07 3.30404e+06 2.0233e+07 1.39219e+07 1.602e+07 9.89366e+06 2.76295e+07 +P61812 P61812 TGFB2_HUMAN TGFB2 Transforming growth factor beta-2 proprotein 18529.9 61976.7 64643.4 68612.5 57676.8 65892.2 55492.3 75666.5 54085.8 70903.3 48254.9 85770 57528.7 69539.4 42413.9 95291.3 47427.9 56594.1 +P61916 P61916;E7EMS2;G3V2V8;G3V3D1;G3V3E8;J3KMY5;H0YIZ1 NPC2_HUMAN NPC2 NPC intracellular cholesterol transporter 2 1.18988e+06 1.16269e+06 1.73843e+06 1.96259e+06 1.21231e+06 1.57009e+06 1.4482e+06 1.39609e+06 1.41458e+06 2.26921e+06 1.51594e+06 1.92418e+06 1.31554e+06 1.84296e+06 1.64614e+06 2.31785e+06 2.16117e+06 2.46898e+06 1.27653e+06 2.74092e+06 +P61923 P61923;F8VUC5;F8VVA7;F8VXB1;F8VXR1;F8VYZ4;F8W156 COPZ1_HUMAN COPZ1 Coatomer subunit zeta-1 143684 179124 259381 88108.7 116264 117770 124414 168958 157900 157650 98666.6 94405.5 72876.8 100789 76527 122960 161937 +P61956 P55854;P61956;Q6EEV6;A8MU27;A8MUA9 SUMO2_HUMAN SUMO2 Small ubiquitin-related modifier 2 3.31117e+06 2.90587e+06 4.10574e+06 2.03881e+06 2.21702e+06 1.71463e+06 2.80852e+06 1.80519e+06 2.40357e+06 2.86556e+06 2.96803e+06 1.87792e+06 2.63827e+06 2.23568e+06 2.62864e+06 2.46354e+06 3.55289e+06 2.36715e+06 3.03734e+06 2.99614e+06 +P61960 P61960;H0Y614 UFM1_HUMAN UFM1 Ubiquitin-fold modifier 1 132957 73012.5 100215 109899 70108.8 112753 88727.5 95370.8 113063 93683.5 173631 89297.5 110642 108947 106696 +P61964 P61964 WDR5_HUMAN WDR5 WD repeat-containing protein 5 86434.5 77136.8 100903 73252 63900.4 68201.6 43701.3 68327.8 57563.9 81325.8 85503.7 73861.7 64920.5 66357.6 104570 67730.4 71916.3 78364.5 76341.9 54976.1 +P61966 P61966;A0A2R8YGH5;H7C1E4 AP1S1_HUMAN AP1S1 AP-1 complex subunit sigma-1A 31074 24814.5 40317 39338.1 28423.6 43842.6 20084.5 +P61970 P61970;A0A8V8TPY3;A0A8V8TR25;H3BRV9 NTF2_HUMAN NUTF2 Nuclear transport factor 2 230241 153056 114431 119500 215735 99154.3 200395 84898.3 191267 97857.7 146852 141178 186407 117295 197744 89813.4 157133 152853 141243 +P61978 P61978;Q5T6W2;S4R457 HNRPK_HUMAN HNRNPK Heterogeneous nuclear ribonucleoprotein K 787245 878693 1.20215e+06 1.56356e+06 2.34296e+06 3.06472e+06 1.73011e+06 2.45795e+06 2.89671e+06 3.69863e+06 1.87576e+06 2.21222e+06 3.18137e+06 2.55958e+06 1.57579e+06 1.82756e+06 2.6287e+06 2.26523e+06 2.24895e+06 1.35367e+06 +P61981 P61981 1433G_HUMAN YWHAG 14-3-3 protein gamma 2.39334e+06 2.99491e+06 2.75311e+06 2.50596e+06 2.43242e+06 2.84074e+06 2.71891e+06 2.05116e+06 2.42869e+06 3.28971e+06 2.1182e+06 1.99808e+06 1.87192e+06 2.93353e+06 2.89643e+06 3.21717e+06 3.08999e+06 3.24477e+06 2.58379e+06 2.57825e+06 +P62070 P10301;P62070;E9PQC5;E9PQK5 RRAS2_HUMAN RRAS2 Ras-related protein R-Ras2 206238 299265 221836 254781 286412 191637 294655 266549 291561 214054 342942 301545 243994 302526 253928 287160 262378 209754 +P62081 P62081;A0A2R8Y623;B5MCP9 RS7_HUMAN RPS7 40S ribosomal protein S7 342140 472281 235893 582873 370181 474001 333730 569864 282495 383507 381121 499796 273815 453046 284052 436572 188269 441646 320578 343776 +P62136 P62136;A0A7I2V4F7;E9PMD7 PP1A_HUMAN PPP1CA Serine/threonine-protein phosphatase PP1-alpha catalytic subunit 170492 166603 120122 155721 217845 232602 194927 207388 274411 222567 177058 190829 207632 231152 205090 107463 170219 153266 156345 160536 +P62140 P62140;C9J9S3;C9JP48;A0A8V8TQ71;A0A8V8TR83;A0A8V8TRH9;H0Y3Y6 PP1B_HUMAN PPP1CB Serine/threonine-protein phosphatase PP1-beta catalytic subunit 269793 244872 226463 254566 269707 249997 205357 226490 202752 213999 221108 236657 274239 206257 188196 262859 266457 196076 288027 204208 +P62191 P62191 PRS4_HUMAN PSMC1 26S proteasome regulatory subunit 4 444273 430354 231174 322842 397590 413783 284037 421430 258411 344910 332323 336968 342988 339449 247831 346974 289239 353216 430876 229485 +P62195 P62195 PRS8_HUMAN PSMC5 26S proteasome regulatory subunit 8 282635 312866 172880 240762 293487 277172 257818 307751 213700 237284 257824 260532 262428 244104 150002 289025 164590 215874 310418 169331 +P62241 P62241;Q5JR95 RS8_HUMAN RPS8 40S ribosomal protein S8 6.54486e+06 5.42945e+06 6.89657e+06 7.55998e+06 3.86132e+06 3.82522e+06 4.64595e+06 4.80828e+06 3.12739e+06 3.41569e+06 5.85082e+06 5.8516e+06 2.80618e+06 3.89463e+06 4.42401e+06 4.72333e+06 3.07324e+06 3.72696e+06 5.23449e+06 4.19009e+06 +P62244 P62244 RS15A_HUMAN RPS15A 40S ribosomal protein S15a 1.44102e+06 1.62332e+06 930816 1.80269e+06 1.3397e+06 1.53549e+06 1.2117e+06 1.92734e+06 1.2991e+06 1.41994e+06 1.27701e+06 1.91837e+06 1.65813e+06 1.60585e+06 1.26956e+06 1.44785e+06 1.16815e+06 1.18083e+06 1.48078e+06 1.2536e+06 +P62249 P62249;M0R1M5;M0R3H0;Q6IPX4 RS16_HUMAN RPS16 40S ribosomal protein S16 1.98228e+06 1.75112e+06 1.7436e+06 2.18269e+06 1.25277e+06 1.53826e+06 1.58505e+06 1.78145e+06 1.0477e+06 1.17891e+06 1.76378e+06 1.61924e+06 1.10743e+06 1.24589e+06 1.20601e+06 1.49089e+06 870553 1.15118e+06 1.77279e+06 1.16612e+06 +P62258 P62258;B4DJF2;I3L3T1;K7EIT4 1433E_HUMAN YWHAE 14-3-3 protein epsilon 4.91433e+06 5.16779e+06 4.33049e+06 4.33622e+06 3.73029e+06 3.85179e+06 4.46687e+06 3.11151e+06 3.8387e+06 4.89386e+06 4.3862e+06 3.40183e+06 3.87803e+06 4.15237e+06 3.35251e+06 4.75536e+06 6.47065e+06 4.81571e+06 5.254e+06 5.04059e+06 +P62263 P62263;A0A2R8Y811;E5RH77 RS14_HUMAN RPS14 40S ribosomal protein S14 566124 645759 535507 831111 552820 760283 517666 721966 583470 670998 503527 721188 599606 663570 382604 594395 487757 758325 602272 432278 +P62266 P62266 RS23_HUMAN RPS23 40S ribosomal protein S23 1.46002e+06 1.41293e+06 877004 1.39089e+06 1.13497e+06 1.21637e+06 1.20613e+06 1.37243e+06 1.00743e+06 918435 1.30466e+06 1.2266e+06 900478 1.0188e+06 899827 988732 366335 947733 1.30154e+06 905207 +P62269 P62269 RS18_HUMAN RPS18 40S ribosomal protein S18 2.00542e+06 1.85508e+06 1.73926e+06 2.23322e+06 1.51892e+06 1.80715e+06 1.50296e+06 1.70932e+06 1.29667e+06 1.42703e+06 1.68243e+06 1.7441e+06 1.17637e+06 1.57809e+06 1.26651e+06 1.57103e+06 1.27475e+06 1.62539e+06 1.82162e+06 1.44232e+06 +P62273 P62273;A0A087WTT6;A0A2R8Y6P7;A0A2R8Y851 RS29_HUMAN RPS29 40S ribosomal protein S29 24696.8 55823.8 23174.3 20185.4 32110 61630.3 65898.8 49361.5 59065.7 55349.2 38334.5 39762.8 19309.8 +P62277 P62277 RS13_HUMAN RPS13 40S ribosomal protein S13 1.67087e+06 1.52281e+06 1.04999e+06 1.85642e+06 1.40684e+06 1.75702e+06 1.13698e+06 1.63379e+06 996998 1.40865e+06 1.42039e+06 1.90402e+06 1.08794e+06 1.71872e+06 1.13962e+06 1.49293e+06 603789 1.43392e+06 1.38283e+06 913436 +P62280 P62280;M0R1H5;M0QZC5 RS11_HUMAN RPS11 40S ribosomal protein S11 1.63639e+06 1.46037e+06 1.44482e+06 1.95305e+06 934070 1.01449e+06 1.28279e+06 1.44451e+06 678914 821256 1.34568e+06 1.3823e+06 641605 856874 811688 1.14857e+06 659334 871680 1.48817e+06 902695 +P62304 P62304 RUXE_HUMAN SNRPE Small nuclear ribonucleoprotein E 1.29283e+06 1.56248e+06 2.95268e+06 3.73955e+06 3.26641e+06 3.3754e+06 2.17983e+06 3.47501e+06 3.96266e+06 3.17057e+06 2.32586e+06 2.72704e+06 3.25926e+06 3.72932e+06 2.99087e+06 3.13639e+06 2.10066e+06 3.82418e+06 2.07085e+06 4.70067e+06 +P62306 P62306 RUXF_HUMAN SNRPF Small nuclear ribonucleoprotein F 56031.4 74842.9 72853.9 66787 255910 253342 197383 218480 374529 289926 205546 212515 212941 235073 194041 205640 155155 275003 182073 135189 +P62310 P62310 LSM3_HUMAN LSM3 U6 snRNA-associated Sm-like protein LSm3 326034 412681 378332 389424 465107 361292 496497 352404 410160 356712 379967 295261 479771 319219 368583 368951 321165 260015 340879 322715 +P62312 P62312 LSM6_HUMAN LSM6 U6 snRNA-associated Sm-like protein LSm6 58514.3 52438.3 78638.3 42298.5 63504.6 55534.3 84514.5 54507.5 49536.6 59397.5 61602.4 53346.3 50304.2 57010.2 66141.5 49472.1 71474.4 53990.8 67410.9 64473.3 +P62314 P62314 SMD1_HUMAN SNRPD1 Small nuclear ribonucleoprotein Sm D1 165334 103470 75149.9 126111 435860 262375 294669 249600 504587 307731 275769 282638 342277 338388 224215 250358 310363 215196 398500 203329 +P62316 P62316;K7ERG4 SMD2_HUMAN SNRPD2 Small nuclear ribonucleoprotein Sm D2 210499 269736 246267 381568 685229 670729 571209 549112 887086 654863 569379 559801 752923 663752 497377 444366 702725 664996 538446 390131 +P62318 P62318 SMD3_HUMAN SNRPD3 Small nuclear ribonucleoprotein Sm D3 185633 307181 332030 234658 995751 1.06138e+06 545284 668715 1.14555e+06 720207 567753 552841 1.41638e+06 871879 518222 690045 556082 887958 577462 342883 +P62328 P62328 TYB4_HUMAN TMSB4X Thymosin beta-4 909521 586191 409396 556760 326624 332815 431646 190876 330873 413057 395966 313132 345829 365584 375989 402052 1.06784e+06 527979 835104 487198 +P62333 P62333;A0A087X2I1 PRS10_HUMAN PSMC6 26S proteasome regulatory subunit 10B 185285 205448 95117.8 147521 211889 216745 142883 206795 114099 146970 159335 170887 152494 164582 118892 171953 78541.4 160853 232044 101340 +P62424 P62424;Q5T8U2 RL7A_HUMAN RPL7A 60S ribosomal protein L7a 3.48316e+06 3.31054e+06 3.78549e+06 4.0912e+06 2.44331e+06 2.43867e+06 2.90518e+06 2.89605e+06 2.37023e+06 2.10627e+06 3.06302e+06 2.71199e+06 1.94609e+06 2.24821e+06 1.95803e+06 2.64957e+06 1.44896e+06 2.21653e+06 2.78918e+06 2.15168e+06 +P62487 P62487 RPB7_HUMAN POLR2G DNA-directed RNA polymerase II subunit RPB7 36351.3 20746.8 19812.5 27569.8 29703.5 37120.4 33248.1 19833.7 38986.9 34902.7 22291.3 15139 46128.7 43320.5 +P62491;Q15907 P62491;Q15907;B4DQU5;H3BSC1;H3BMH2 RB11A_HUMAN;RB11B_HUMAN RAB11A;RAB11B Ras-related protein Rab-11A 357199 351647 271297 361858 274762 303679 273213 226649 276553 370088 261249 260455 265268 324395 291328 299537 310831 378620 300515 334563 +P62495 P62495;B7Z7P8 ERF1_HUMAN ETF1 Eukaryotic peptide chain release factor subunit 1 213613 182621 99027.6 158833 197534 204594 202716 186342 183942 146628 196673 166941 165503 218772 214666 223991 104304 137146 265886 200585 +P62633 P62633 CNBP_HUMAN CNBP CCHC-type zinc finger nucleic acid binding protein 150394 264649 221460 351778 255135 316847 230625 309263 263507 415562 203148 311048 198498 128170 367594 241636 179638 133152 322042 343769 +P62701 P62701 RS4X_HUMAN RPS4X 40S ribosomal protein S4, X isoform 3.65632e+06 3.33579e+06 3.40435e+06 3.91456e+06 2.09647e+06 2.30925e+06 2.4902e+06 2.60246e+06 1.88869e+06 2.23435e+06 2.87301e+06 2.90548e+06 1.77387e+06 2.01852e+06 1.78296e+06 2.36226e+06 2.4147e+06 2.02464e+06 3.25896e+06 2.05605e+06 +P62714;P67775 P62714;P67775;B3KQ51;E5RFI3;E5RHC1;E7ESG8;A0A8V8TRA6;A0A8V8TRB3 PP2AA_HUMAN;PP2AB_HUMAN PPP2CA;PPP2CB Serine/threonine-protein phosphatase 2A catalytic subunit beta isoform 200870 117464 132221 111474 155095 97584 100621 91869.9 140754 91032.8 164014 124037 94801.3 83913.1 90089 126707 88973.9 177499 87565.2 +P62736;P63267;P68032;P68133 P62736;P63267;P68032;P68133;A0A804HKV3;A6NL76;B8ZZJ2;C9JFL5;F6QUT6;F6UVQ4;F8WB63 ACTA_HUMAN;ACTC_HUMAN;ACTH_HUMAN;ACTS_HUMAN ACTA1;ACTA2;ACTC1;ACTG2 Actin, aortic smooth muscle 167083 65099.5 24489.6 70167.1 99422.8 48324.9 50368.4 120955 59152 148169 120311 79726.5 87338.4 93316 104975 +P62750 P62750;H7BY10;K7EJV9;K7ERT8;A8MUS3 RL23A_HUMAN RPL23A 60S ribosomal protein L23a 611593 499324 596569 601944 462825 430254 452997 386075 413343 416316 493327 430913 404222 369037 275800 431753 389052 429002 395169 379077 +P62753 P62753;A2A3R7 RS6_HUMAN RPS6 40S ribosomal protein S6 3.10885e+06 3.29153e+06 2.34736e+06 3.3111e+06 2.64806e+06 2.73319e+06 3.08656e+06 3.14504e+06 2.20234e+06 2.19324e+06 3.18478e+06 3.07801e+06 2.3648e+06 2.65659e+06 2.13076e+06 3.1017e+06 1.54311e+06 2.27666e+06 2.6409e+06 1.88183e+06 +P62805 P62805 H4_HUMAN H4C16 Histone H4 6.0414e+06 445468 1.02486e+06 1.11988e+06 965591 473798 810411 673925 947877 555612 1.18159e+06 952316 915006 578894 633692 829642 604811 565725 611923 788495 +P62826 P62826;F5H018;J3KQE5 RAN_HUMAN RAN GTP-binding nuclear protein Ran 2.5429e+06 2.4036e+06 2.27282e+06 2.49276e+06 2.438e+06 2.42454e+06 2.65794e+06 2.6257e+06 2.14944e+06 2.29376e+06 2.98787e+06 2.75884e+06 2.01449e+06 2.20885e+06 3.34479e+06 3.05178e+06 2.24414e+06 2.86166e+06 3.38464e+06 3.25749e+06 +P62829 P62829;J3KT29;J3KTJ3;C9JD32;J3QQT9 RL23_HUMAN RPL23 60S ribosomal protein L23 595559 719483 560665 693906 583870 636443 607331 688279 440982 526963 635265 555887 407532 579210 621996 566072 239846 478137 699946 334308 +P62841 P62841;A0A0B4J2B4;K7ELC2;K7EM56;S4R417;S4R456;K7EJ78;K7EQJ5 RS15_HUMAN RPS15 40S ribosomal protein S15 314868 306899 351209 265910 418634 255555 313868 260718 331190 309205 317607 247324 294846 179446 318460 63685.9 306235 287573 156700 +P62847 P62847;E7ETK0;A0A2R8Y849;A0A2R8Y8A0;A0A2R8YD14 RS24_HUMAN RPS24 40S ribosomal protein S24 1.05426e+06 1.14355e+06 1.56445e+06 1.11458e+06 856046 905533 1.03381e+06 974725 537484 610069 919316 959428 675919 786397 450623 948419 544844 960168 924726 625722 +P62851 P62851 RS25_HUMAN RPS25 40S ribosomal protein S25 3.79231e+06 4.20511e+06 4.15375e+06 5.16694e+06 3.44655e+06 4.14808e+06 3.59934e+06 4.30181e+06 3.32946e+06 3.62397e+06 3.77433e+06 3.95848e+06 2.90411e+06 3.95515e+06 3.05494e+06 3.60212e+06 2.94927e+06 3.83764e+06 3.67252e+06 2.48775e+06 +P62854 P62854 RS26_HUMAN RPS26 40S ribosomal protein S26 1.66841e+06 1.83036e+06 2.11259e+06 1.7749e+06 966397 1.13739e+06 1.4961e+06 1.36604e+06 1.09717e+06 1.13152e+06 1.57439e+06 1.5618e+06 973300 746733 1.17858e+06 1.22878e+06 986399 1.2232e+06 1.36658e+06 1.12769e+06 +P62857 P62857 RS28_HUMAN RPS28 40S ribosomal protein S28 171845 209631 142741 293363 137872 175411 133815 185690 86166 229510 221024 241692 145661 180293 119994 249678 198531 172554 229483 211665 +P62861 P62861;E9PR30 RS30_HUMAN FAU FAU ubiquitin-like and ribosomal protein S30 422821 457262 452779 629851 534171 631445 392600 579507 458149 505054 504903 536319 518420 615230 549051 431107 466176 464262 319270 551441 +P62879 P62879;C9JIS1;C9JXA5;E7EP32 GBB2_HUMAN GNB2 Guanine nucleotide-binding protein G(I)/G(S)/G(T) subunit beta-2 169361 145460 80750 184457 142910 164713 195127 115327 210056 141298 235019 225710 146116 223629 126134 164409 179987 213424 122224 +P62888 P62888;E5RI99 RL30_HUMAN RPL30 60S ribosomal protein L30 1.38912e+06 1.16927e+06 1.20339e+06 1.47603e+06 835686 683508 1.07088e+06 1.04463e+06 468375 600784 1.2019e+06 1.04976e+06 506615 620103 829919 1.0134e+06 536661 614981 1.06745e+06 693546 +P62899 P62899;B7Z4C8;B7Z4E3;B8ZZK4;C9JU56;H7C2W9 RL31_HUMAN RPL31 60S ribosomal protein L31 431231 455359 714228 639416 494762 442551 459334 485315 471986 403988 678022 485883 366132 470438 482977 502527 344147 476404 405021 384850 +P62906 P62906 RL10A_HUMAN RPL10A 60S ribosomal protein L10a 3.79646e+06 3.25924e+06 3.33479e+06 3.39427e+06 3.07745e+06 3.22949e+06 3.90261e+06 2.78225e+06 3.13551e+06 4.165e+06 3.77834e+06 3.2205e+06 3.0404e+06 2.89274e+06 2.73791e+06 3.85124e+06 3.6235e+06 3.30102e+06 3.672e+06 3.05236e+06 +P62910 P62910;D3YTB1;F8W727 RL32_HUMAN RPL32 60S ribosomal protein L32 435550 439501 498973 516009 268223 278164 319525 311107 231811 220913 362141 357188 267732 193214 107726 298159 217464 197904 370403 259784 +P62913 P62913;Q5VVC8 RL11_HUMAN RPL11 60S ribosomal protein L11 586214 634551 412687 649503 536480 590623 488058 747832 410971 522545 614684 643148 390839 504375 461172 565855 287180 483588 614819 303593 +P62917 P62917;E9PKU4;E9PKZ0;G3V1A1 RL8_HUMAN RPL8 60S ribosomal protein L8 4.46642e+06 3.53603e+06 3.73297e+06 4.34107e+06 2.95209e+06 2.72595e+06 3.56924e+06 2.89077e+06 2.71635e+06 2.32398e+06 3.65197e+06 2.68782e+06 2.19614e+06 2.40355e+06 2.43874e+06 2.45495e+06 1.72012e+06 2.08417e+06 3.51939e+06 1.71411e+06 +P62937 P62937 PPIA_HUMAN PPIA Peptidyl-prolyl cis-trans isomerase A 1.04201e+07 7.07566e+06 5.40301e+06 8.3513e+06 8.34061e+06 6.69027e+06 8.56554e+06 5.97827e+06 7.39656e+06 7.56613e+06 8.36168e+06 7.23393e+06 8.25631e+06 7.72538e+06 8.82639e+06 6.7891e+06 7.6063e+06 5.98801e+06 7.81962e+06 6.96679e+06 +P62942 P62942;A0A7I2V3S6;A0A087WTS4;A0A087WV48;A0A087WZM5;Q1JUQ3;Q5W0X3 FKB1A_HUMAN FKBP1A Peptidyl-prolyl cis-trans isomerase FKBP1A 158827 174786 170479 153899 183021 120278 191539 114254 213369 140489 183063 98753.6 192786 116623 197991 140904 181665 145989 188457 113318 +P62979 P62979 RS27A_HUMAN RPS27A Ubiquitin-40S ribosomal protein S27a 4.17247e+06 3.60264e+06 3.87765e+06 4.39055e+06 3.7263e+06 3.19043e+06 3.95806e+06 3.24738e+06 2.87916e+06 2.87166e+06 3.8534e+06 2.88721e+06 3.02534e+06 2.46454e+06 4.29668e+06 3.00259e+06 2.86332e+06 3.03979e+06 2.9838e+06 2.96847e+06 +P62993 P62993 GRB2_HUMAN GRB2 Growth factor receptor-bound protein 2 318696 243021 260588 279799 378567 304772 304616 212670 310093 393596 322579 234796 343136 267693 296983 305626 382898 219736 347317 237351 +P62995 P62995 TRA2B_HUMAN TRA2B Transformer-2 protein homolog beta 299976 213848 384809 411225 222982 253067 259862 251530 230993 206243 389536 330276 214904 246436 225724 238117 246913 164532 370598 248873 +P63000 P15153;P60763;P63000;B1AH77;B1AH78;B1AH80;J3KSC4;J3QLK0 RAC1_HUMAN RAC1 Ras-related C3 botulinum toxin substrate 1 396352 139972 315946 219489 319705 213151 342690 266856 337305 183413 408014 274637 306236 257111 329466 272390 279844 241232 356402 237894 +P63010 P63010;Q10567;A0A087X253 AP2B1_HUMAN AP2B1 AP-2 complex subunit beta 234741 231904 148761 210149 242929 298672 184389 310590 266370 230389 233051 246682 242261 247709 177971 263360 154732 239593 251387 162151 +P63092;Q5JWF2 P63092;Q5JWF2;A0A0A0MR13;A0A590UJF0;A0A590UJQ9;A0A590UJS2;A0A590UJY2;A0A7I2V5R6;B0AZR9;Q5JWE9;A0A590UK28;A0A804HIH4 GNAS1_HUMAN;GNAS2_HUMAN GNAS Guanine nucleotide-binding protein G(s) subunit alpha isoforms short 497716 230052 514343 261036 532487 317577 479742 267324 563338 276426 616128 283951 428033 306929 476621 300852 517804 286273 480788 261401 +P63104 P31947;P61981;P63104;Q04917;A2IDB2;B0AZS6;B7Z2E6;E7ESK7;E7EX29;H0YB80 1433Z_HUMAN YWHAZ 14-3-3 protein zeta/delta 4.5984e+06 4.13941e+06 3.51156e+06 3.53202e+06 3.23731e+06 3.49114e+06 3.90949e+06 2.71257e+06 3.20343e+06 3.57875e+06 3.78036e+06 3.27707e+06 3.07827e+06 3.9925e+06 5.28519e+06 4.1724e+06 4.83616e+06 3.91606e+06 3.94289e+06 3.70986e+06 +P63151 P63151;E5RIY1 2ABA_HUMAN PPP2R2A Serine/threonine-protein phosphatase 2A 55 kDa regulatory subunit B alpha isoform 208182 146887 77569.5 122491 191669 146349 154482 171768 161045 130379 182546 194955 158087 119217 185791 124002 158727 147265 154714 +P63165 P63165;B8ZZN6;B8ZZ67 SUMO1_HUMAN SUMO1 Small ubiquitin-related modifier 1 353118 295518 659928 394828 463592 352962 535007 312923 460456 394706 486537 255280 593062 408166 503388 425160 510244 378325 406780 333253 +P63167 P63167;Q96FJ2 DYL1_HUMAN DYNLL1 Dynein light chain 1, cytoplasmic 173099 21379.8 46985.1 47535.5 11885.2 28607.8 11693.6 58390.7 69582.8 46106.8 114780 37581 +P63173 P63173;J3KT73;J3QL01 RL38_HUMAN RPL38 60S ribosomal protein L38 119005 44338.6 85522.8 74935.1 76852.2 53284.8 95071.7 52425.1 71870.1 49431.7 47942.9 82507.1 70823.6 93667.7 +P63208 P63208;E5RJR5;E5RI56 SKP1_HUMAN SKP1 S-phase kinase-associated protein 1 287204 313487 259974 369384 284630 379807 326677 307763 222292 427590 327870 351800 308530 358562 182547 403696 316230 322995 412419 440221 +P63220 P63220;Q8WVC2 RS21_HUMAN RPS21 40S ribosomal protein S21 316121 387576 311533 373732 320412 390711 339605 353592 257438 488754 435296 382609 405444 453284 420018 415550 394969 403836 371372 477242 +P63241 P63241;Q9GZV4;Q6IS14;C9J4W5;C9J7B5;F8WCJ1;I3L397;I3L504 IF5A1_HUMAN EIF5A Eukaryotic translation initiation factor 5A-1 571592 975744 336665 724714 707289 878855 774487 684097 562564 1.04017e+06 605848 802417 666184 876632 721966 1.21192e+06 717999 769252 953076 1.23975e+06 +P63244 P63244;D6R9L0;D6R9Z1;D6RAC2;D6REE5;D6RFX4;H0Y8R5;H0Y8W2;H0YAM7;J3KPE3;H0YAF8 RACK1_HUMAN RACK1 Receptor of activated protein C kinase 1 1.56781e+06 979172 665259 1.35993e+06 1.29144e+06 1.31629e+06 1.1463e+06 1.06319e+06 1.01399e+06 1.35887e+06 1.31097e+06 1.70546e+06 1.07946e+06 1.31934e+06 1.91935e+06 1.02688e+06 914141 1.33044e+06 1.42267e+06 1.10342e+06 +P63261 P60709;P63261;A0A2R8Y793;A0A2R8YEA7;A0A6Q8PFE4;A0A804GS07;E7EVS6;I3L1U9;I3L3I0;I3L4N8 ACTG_HUMAN ACTG1 Actin, cytoplasmic 2 1.02694e+07 1.33683e+07 7.67558e+06 1.27338e+07 1.20567e+07 1.647e+07 1.0691e+07 1.65868e+07 1.11126e+07 1.46804e+07 1.08278e+07 1.46399e+07 1.03648e+07 1.51364e+07 1.04945e+07 1.29082e+07 8.36807e+06 1.33259e+07 1.28917e+07 1.09789e+07 +P63279 P63279;B0QYN7;H3BPC4;H3BQQ9 UBC9_HUMAN UBE2I SUMO-conjugating enzyme UBC9 398909 559961 455943 384782 419627 428728 457817 330034 305651 393975 374006 301257 333454 342037 339258 453920 401215 430423 407546 526598 +P63313 P21752;P63313 TYB10_HUMAN TMSB10 Thymosin beta-10 488711 676891 350068 564056 249609 363774 277909 283076 405438 252100 278058 247695 318472 284691 365806 394816 465918 544305 359713 +P67809 P67809;C9J5V9;H0Y449 YBOX1_HUMAN YBX1 Y-box-binding protein 1 1.31309e+06 871855 1.45539e+06 1.24056e+06 896520 789354 997876 725983 987807 724357 979841 828658 692884 737283 844000 777113 1.13026e+06 856766 1.25143e+06 672526 +P67870 P67870;A0A0G2JM12;A0A7I2YQ78;N0E472;Q5SRQ3;Q5SRQ6 CSK2B_HUMAN CSNK2B Casein kinase II subunit beta 77351.6 59070.6 65676.7 107014 70135 82300.7 58791 75027.6 49010.3 73582.1 121042 86265.7 58668.9 85450 32391.7 85337.8 74586.8 105890 33979.4 +P67936 P67936;A0A2R8YE05;A0A5F9UN72;K7ENT6;K7EPB9 TPM4_HUMAN TPM4 Tropomyosin alpha-4 chain 1.11733e+06 944882 806458 1.08219e+06 1.018e+06 1.37034e+06 1.02353e+06 988264 1.13777e+06 1.37409e+06 985167 1.26687e+06 913223 1.15618e+06 1.08389e+06 1.17126e+06 904497 882284 1.18249e+06 755500 +P68036 P68036;A0A1B0GUS4 UB2L3_HUMAN UBE2L3 Ubiquitin-conjugating enzyme E2 L3 314534 318240 171039 225983 198971 247147 300718 163620 196584 283241 214900 213041 154246 258847 291513 373771 283445 243021 431142 243417 +P68104 P68104;Q5VTE0;A0A087WVQ9;A0A7I2V3H3;A0A7I2V5N4;A0A7I2V659 EF1A1_HUMAN EEF1A1 Elongation factor 1-alpha 1 8.86998e+06 8.07913e+06 4.82865e+06 6.83359e+06 1.17879e+07 9.99348e+06 9.8109e+06 8.96526e+06 1.08245e+07 9.25053e+06 9.62395e+06 9.02906e+06 1.05074e+07 8.80928e+06 8.01402e+06 7.59292e+06 9.46633e+06 7.83359e+06 1.29983e+07 5.84663e+06 +P68366 A6NHL2;P68363;P68366;Q71U36;Q9BQE3;F5H5D3 TBA4A_HUMAN TUBA4A Tubulin alpha-4A chain 9.21534e+06 1.05771e+07 2.86843e+06 6.93394e+06 8.80951e+06 1.08552e+07 7.39329e+06 1.05994e+07 7.62125e+06 9.57423e+06 7.48572e+06 8.8728e+06 7.99092e+06 9.5943e+06 7.80681e+06 9.20905e+06 6.53394e+06 8.57672e+06 1.1347e+07 6.61393e+06 +P68371 P68371;Q13885;Q9BVA1 TBB4B_HUMAN TUBB4B Tubulin beta-4B chain 1.9309e+06 2.16462e+06 1.12649e+06 1.34621e+06 2.18448e+06 2.66395e+06 1.95214e+06 2.32504e+06 2.29917e+06 1.91057e+06 2.03555e+06 1.88583e+06 2.20359e+06 2.55588e+06 2.0088e+06 1.76174e+06 1.92403e+06 2.33796e+06 2.50301e+06 1.49946e+06 +P68400;Q8NEV1 P68400;Q8NEV1;A0A2R8Y4D6;A0A2R8Y5A0;A0A2R8YDP2;A0A2R8YF43;A0A2R8YFU2;E7EU96;A0A2R8Y3W6;A0A2R8Y4H0;A0A2R8Y797;A0A2R8Y7T1;A0A2R8YCK2;A0A2R8YD58;A0A2R8YDY7;A0A2R8YEL7;A0A2R8YEW1;A0A2R8YF47;V9GY80;V9GYA2 CSK21_HUMAN;CSK23_HUMAN CSNK2A1;CSNK2A3 Casein kinase II subunit alpha 173433 124314 177725 160112 137069 115733 125840 106304 123914 118753 158940 127604 112389 114202 170994 134079 75725.8 116584 49484.2 +P68431;P84243;Q16695;Q71DI3 P68431;P84243;Q16695;Q6NXT2;Q71DI3;Q5TEC6;K7EK07;A0A590UJL8 H31T_HUMAN;H31_HUMAN;H32_HUMAN;H33_HUMAN H3-3B;H3-4;H3C12;H3C13 Histone H3.1 9.27811e+06 1.17566e+06 1.54165e+06 1.90756e+06 1.58314e+06 989853 1.43905e+06 1.41556e+06 1.33032e+06 929267 1.6544e+06 1.65479e+06 1.14327e+06 1.23097e+06 1.48886e+06 1.40147e+06 1.02379e+06 964861 1.37036e+06 845959 +P68871 P68871;A0A2R8Y7R2;F8W6P5 HBB_HUMAN HBB Hemoglobin subunit beta 987143 667050 649555 814498 559536 861534 +P69849;Q15155;Q5JPE7 Q15155;Q5JPE7;P69849;A0A0G2JN29;A0A0G2JP90;A0A7I2V2Z5;A0A7I2V3P4;A0A7I2V4D7;A0A7I2V4T6;A0A7I2V6B0;J3KN36 NOMO1_HUMAN;NOMO2_HUMAN;NOMO3_HUMAN NOMO1;NOMO2;NOMO3 Nodal modulator 3 68691 26565.1 80833.9 105948 59078.3 98041.1 63958.2 126937 62421.1 101775 82712.6 99023.4 80265 114697 85378.8 96416.4 81634.9 39410.3 77423.7 +P69905 P69905 HBA_HUMAN HBA2 Hemoglobin subunit alpha 108606 143798 +P78318 P78318 IGBP1_HUMAN IGBP1 Immunoglobulin-binding protein 1 15470.1 33094.5 44818.2 7471.31 39242.4 20635.2 +P78330 P78330;C9JBI3 SERB_HUMAN PSPH Phosphoserine phosphatase 175977 205699 121347 174088 178523 180321 265546 153983 169055 215191 184559 149541 202608 173932 236970 249436 207644 229695 208121 265038 +P78344 P78344;D3DQV9;E9PKF8;H0Y3P2 IF4G2_HUMAN EIF4G2 Eukaryotic translation initiation factor 4 gamma 2 105535 58141.4 43458.9 122718 114986 125711 119699 65537.6 81374.6 106898 60875.4 81912.1 83992.1 120283 141431 57420.1 81798.6 140428 +P78347 P78347;A0A494C1K3;A0A8I5KVB5 GTF2I_HUMAN GTF2I General transcription factor II-I 76794.9 105508 91617.4 113633 76933.8 104906 66832.9 126319 63142.6 83910.4 69043.8 80141.5 98272.6 92404.5 74839.6 99020.3 68856.3 99171.8 120385 65913.6 +P78371 P78371;F5GWF6 TCPB_HUMAN CCT2 T-complex protein 1 subunit beta 1.18347e+06 1.0433e+06 712430 865620 1.38438e+06 1.02323e+06 1.08413e+06 1.09247e+06 1.42326e+06 1.0346e+06 1.21027e+06 991515 1.33359e+06 915816 625554 920920 1.0342e+06 764645 1.51284e+06 752613 +P78380 P78380;F5H7N8 OLR1_HUMAN OLR1 Oxidized low-density lipoprotein receptor 1 639272 807932 480073 522151 749634 616075 627534 606520 570785 793516 +P78385 P78385 SWISS-PROT:P78385 SWISS-PROT:P78385 3.85169e+06 166175 296521 75746.1 72064.4 +P78386 P78385;P78386;Q6NT21;Q61726;A6NCN2 SWISS-PROT:P78386 SWISS-PROT:P78386 3.2893e+07 49700.1 247273 62246.1 174724 +P78417 P78417;Q5TA02 GSTO1_HUMAN GSTO1 Glutathione S-transferase omega-1 1.98007e+06 1.80593e+06 2.16342e+06 1.77914e+06 1.90925e+06 1.7791e+06 2.15963e+06 1.47071e+06 1.78718e+06 1.81231e+06 2.01613e+06 1.4068e+06 1.88419e+06 1.69082e+06 2.11736e+06 1.87163e+06 2.43945e+06 2.01586e+06 1.97383e+06 1.70544e+06 +P78527 P78527 PRKDC_HUMAN PRKDC DNA-dependent protein kinase catalytic subunit 268839 344838 183463 290204 246714 428228 201064 430677 181278 356038 217921 353262 226103 350782 213764 350338 190455 302740 396650 255155 +P78536 P78536;A0A8V8TN27;A0A8V8TNK7;A0A8V8TPG8;A0A8V8TPV7;A6H8L4;A8K1B4 ADA17_HUMAN ADAM17 Disintegrin and metalloproteinase domain-containing protein 17 139854 114407 183229 230930 181395 207122 176134 144732 194064 246736 236065 210813 234858 198226 166010 150534 160899 181145 139267 +P80303 P80303;E9PKG6;A0A2R8Y6G7;E9PJP3;E9PLE9;E9PLR0;E9PM22 NUCB2_HUMAN NUCB2 Nucleobindin-2 19569 42476.9 25931.2 33910.2 86848.5 97993 110647 48277.5 82675.6 61035.7 20630.8 +P80723 P80723 BASP1_HUMAN BASP1 Brain acid soluble protein 1 2.90833e+06 3.4819e+06 3.95587e+06 4.10306e+06 3.04205e+06 5.35868e+06 3.78961e+06 3.534e+06 4.97182e+06 6.85418e+06 4.42226e+06 5.91856e+06 4.28041e+06 5.24607e+06 4.47624e+06 6.39417e+06 5.33338e+06 5.55034e+06 5.08488e+06 5.16741e+06 +P81605 P81605 DCD_HUMAN DCD Dermcidin 536747 79770.8 241375 156734 320195 269081 334689 206247 220899 287808 536124 302498 579761 468336 74200.6 846543 479490 306727 207084 209593 +P82970 P82970;Q5JSK6;Q5JSK7;Q5JSK8;Q5JSL0 HMGN5_HUMAN HMGN5 High mobility group nucleosome-binding domain-containing protein 5 173737 85105.5 212223 76403.6 215304 108178 234321 70301.2 214160 114269 284156 211600 67239 154244 115006 229486 72949.8 227783 +P82979 P82979;F8VZQ9;H0YHG0;F8VS12 SARNP_HUMAN SARNP SAP domain-containing ribonucleoprotein 151648 287047 314457 352273 411704 403927 306913 352661 449303 397608 260014 327250 378984 424540 328086 257007 325491 440493 224832 172715 +P83731 P83731;C9JNW5;C9JXB8 RL24_HUMAN RPL24 60S ribosomal protein L24 1.6493e+06 1.5382e+06 1.88354e+06 1.95145e+06 1.15939e+06 1.1479e+06 1.29678e+06 1.19197e+06 1.02909e+06 971209 1.39597e+06 1.33126e+06 967908 1.07126e+06 1.0241e+06 1.11935e+06 1.00747e+06 1.05877e+06 1.35582e+06 1.11611e+06 +P83916 P83916;B5MD17;J3KS05 CBX1_HUMAN CBX1 Chromobox protein homolog 1 27446.7 20916.9 27050.6 36602.9 25953.4 33867 26513.5 31460.1 +P84090 P84090;G3V279 ERH_HUMAN ERH Enhancer of rudimentary homolog 37926.6 89651.1 129222 75406.1 81235.3 52518.6 92612.6 96556.9 77443 112001 132864 42250.2 247958 112819 83635.8 82642.2 +P84098 P84098;A0A7I2YQG2;J3KTE4;J3QR09 RL19_HUMAN RPL19 60S ribosomal protein L19 3.02981e+06 2.43182e+06 2.25422e+06 2.34412e+06 1.82816e+06 1.74573e+06 2.23183e+06 2.17423e+06 1.34127e+06 1.35847e+06 2.52457e+06 2.17496e+06 1.13688e+06 1.76306e+06 2.81668e+06 1.75917e+06 952715 1.53295e+06 2.12852e+06 1.40256e+06 +P84103 P84103;A0A087X2D0 SRSF3_HUMAN SRSF3 Serine/arginine-rich splicing factor 3 803792 453696 842102 681336 654653 753070 649538 685568 695804 687276 698087 704825 494235 687953 605683 617534 537325 610568 705686 468113 +P86790;P86791 P86791;P86790 CCZ1B_HUMAN;CCZ1_HUMAN CCZ1;CCZ1B Vacuolar fusion protein CCZ1 homolog B 120412 125287 47559.2 107275 122627 82715.8 126587 78750.7 98155.1 102275 73032.3 98326.6 93322 91705.4 87545.4 96129.3 +P98095 P98095 FBLN2_HUMAN FBLN2 Fibulin-2 1.55162e+06 170747 1.57455e+06 425279 2.04886e+06 422639 2.05984e+06 466397 2.73444e+06 413637 2.41111e+06 697678 2.28295e+06 561521 1.72462e+06 502436 1.72584e+06 547096 1.30446e+06 417713 +P98160 P98160 PGBM_HUMAN HSPG2 Basement membrane-specific heparan sulfate proteoglycan core protein 1.89398e+06 1.28007e+06 1.98532e+06 2.5413e+06 2.22211e+06 2.37963e+06 2.67606e+06 2.58932e+06 2.57602e+06 2.57299e+06 2.84376e+06 3.48838e+06 2.39494e+06 3.10043e+06 2.77981e+06 3.65195e+06 2.06374e+06 3.34098e+06 1.63616e+06 3.67866e+06 +P98179 P98179 RBM3_HUMAN RBM3 RNA-binding protein 3 28489 24655.2 82994.1 100884 20235 99136.9 58063.7 20322.8 15924.9 75371.5 90508 14362.5 26303.1 +P99999 P99999;C9JFR7 CYC_HUMAN CYCS Cytochrome c 1.0135e+06 951484 1.6116e+06 925896 1.25545e+06 1.005e+06 1.51471e+06 708084 1.34661e+06 1.00668e+06 1.20303e+06 866571 1.18077e+06 1.08432e+06 1.11966e+06 1.1996e+06 1.05415e+06 853063 838844 936358 +Q00341 Q00341;A0A024R4E5;C9J5E5;C9JES8;C9JHS7;C9JHZ8;C9JIZ1;C9JZI8;H7C0A4 VIGLN_HUMAN HDLBP Vigilin 60915.4 100313 106268 108044 184883 86244.4 188490 64335.3 139348 88607.4 131283 126480 146393 104061 140521 63465.9 142291 166841 +Q00577 Q00577 PURA_HUMAN PURA Transcriptional activator protein Pur-alpha 103842 43334.6 72826.3 37176 76363.2 42291.8 76381.6 59162.2 38851.8 102176 42342.7 37861.8 39527.2 129691 38002.7 33413.5 44043.2 90738.2 +Q00610 P53675;Q00610;A0A087WVQ6;A0A087WX41;K7EJJ5;A0A8V8TQ14;A0A8V8TQK1;A0A8V8TQK3;A0A8V8TR47;A0A8V8TRE8;A0A8V8TRF0 CLH1_HUMAN CLTC Clathrin heavy chain 1 458664 383367 246269 381632 568986 728471 468650 647867 563045 737456 532701 637658 581918 730965 560464 663079 494828 646201 777654 483079 +Q00688 Q00688 FKBP3_HUMAN FKBP3 Peptidyl-prolyl cis-trans isomerase FKBP3 20231.6 43792.8 36985.6 54751.1 50506.2 52565.7 42032.6 35961.5 29805.5 29221.9 161069 63878.9 39924.7 +Q00765 Q00765 REEP5_HUMAN REEP5 Receptor expression-enhancing protein 5 31437.7 28681.1 44995 32254.6 42325.8 25471.3 28477.1 47583.7 60685.2 49695.9 38939.3 62250.3 +Q00796 Q00796;A0A6I8PIS1 DHSO_HUMAN SORD Sorbitol dehydrogenase 263312 412322 260746 369682 218395 340862 239616 411751 235425 408036 113647 339997 200175 328641 244804 378944 248706 312923 261967 276588 +Q00839 Q00839;A0A1W2PP34;A0A1W2PP35;A0A1W2PPH7;A0A1W2PPL4;A0A1W2PPS1;A0A1W2PQ74;A0A1W2PQD4;A0A1W2PQL0;A0A1W2PRZ7;A0A1X7SBS1;Q5RI18 HNRPU_HUMAN HNRNPU Heterogeneous nuclear ribonucleoprotein U 994516 729416 1.19217e+06 1.18145e+06 736497 927997 741387 837930 841210 919160 905550 990875 573046 784437 600012 831091 549400 646913 795658 576485 +Q01082 Q01082;A0A087WUZ3 SPTB2_HUMAN SPTBN1 Spectrin beta chain, non-erythrocytic 1 493529 321335 431394 358992 487311 401672 430594 391037 447583 397620 487254 413888 425629 398538 416223 351249 383843 367755 444646 311248 +Q01105 Q01105;A0A0C4DFV9;A0A8I5KS71;A0A8J8YYJ1 SET_HUMAN SET Protein SET 2.9901e+06 1.96009e+06 3.55037e+06 2.64996e+06 2.3093e+06 1.75732e+06 3.10951e+06 1.54906e+06 2.55085e+06 1.7504e+06 2.86409e+06 1.84267e+06 1.70015e+06 1.7154e+06 2.41307e+06 1.80212e+06 1.45744e+06 1.55439e+06 2.42799e+06 1.0116e+06 +Q01130 Q01130;J3KP15;J3QL05 SRSF2_HUMAN SRSF2 Serine/arginine-rich splicing factor 2 2.41544e+06 840001 1.39009e+06 1.52866e+06 1.19748e+06 1.00891e+06 1.37076e+06 694704 1.08941e+06 1.07429e+06 1.28114e+06 1.41761e+06 1.13555e+06 1.22774e+06 1.54922e+06 1.15021e+06 1.03552e+06 1.15859e+06 1.98831e+06 794177 +Q01433 Q01433;A0A5F9UK94;A0A7P0Z4H6;A0A804CCZ3;E9PJF6;H0Y360;A0A590UJX5;A0A7P0T999 AMPD2_HUMAN AMPD2 AMP deaminase 2 51134.5 97405 78584.1 89570.5 109947 45291.6 118481 115597 66607.1 90214.9 99836.3 73683.7 75055.3 35594.4 91797.7 91201.1 +Q01459 Q01459 DIAC_HUMAN CTBS Di-N-acetylchitobiase 109957 114758 208450 221387 191471 186372 244766 172454 253143 281160 187068 183454 261302 246851 274439 242858 247999 336720 131099 371477 +Q01518 Q01518 CAP1_HUMAN CAP1 Adenylyl cyclase-associated protein 1 265214 223188 168968 247942 313321 245475 279551 296853 264702 235062 293945 289158 243334 219160 215310 228565 296209 179455 359414 166618 +Q01546 Q01546 SWISS-PROT:Q01546 SWISS-PROT:Q01546 124974 47827.7 +Q01581 Q01581 HMCS1_HUMAN HMGCS1 Hydroxymethylglutaryl-CoA synthase, cytoplasmic 302411 187603 243895 189168 302939 200699 261876 163503 314912 200968 286440 166007 287141 179304 196862 181388 275274 190160 244062 149717 +Q01650 Q01650 LAT1_HUMAN SLC7A5 Large neutral amino acids transporter small subunit 1 507064 185874 929098 341641 570956 224288 788147 243782 838358 370563 861437 365280 674562 488849 744890 305420 811831 507330 829365 287622 +Q01813 Q01813;A0A7I2V3Z0;B1APP6;A0A8V8TMY4;H0Y3Y3 PFKAP_HUMAN PFKP ATP-dependent 6-phosphofructokinase, platelet type 160606 170421 92429.6 106990 187995 176766 162356 214792 137815 130751 197801 170285 164129 134264 109818 177668 109350 116557 251073 105956 +Q01844 Q01844;A0A0D9SFL3;B0QYK0;C9JGE3;H7BY36 EWS_HUMAN EWSR1 RNA-binding protein EWS 92971.8 134667 104209 146636 175251 140719 146285 297851 266371 135063 170703 276396 245779 239293 209522 332433 291674 97074.4 125011 +Q01955 Q01955 CO4A3_HUMAN COL4A3 Collagen alpha-3(IV) chain 219982 176616 183754 186583 211686 277415 195505 190156 188348 167683 200806 178044 +Q02413 Q02413 DSG1_HUMAN DSG1 Desmoglein-1 297796 96814.3 144865 32512.9 255573 129667 176397 51516.8 96181.6 112725 109455 597959 179126 213509 32401.3 449385 75310.9 103170 54592.7 464140 +Q02487 Q02487 DSC2_HUMAN DSC2 Desmocollin-2 69916 19962.3 51828.2 26932.2 71640 30419 95687.5 34054.1 87186 52860.3 84414.3 53835.1 49348.7 61545.5 86857 58757.9 103681 66493.2 59849.2 +Q02543 Q02543;M0R0P7;M0R117;M0R1A7;M0R3D6 RL18A_HUMAN RPL18A 60S ribosomal protein L18a 2.1635e+06 2.00918e+06 2.30975e+06 2.72377e+06 1.24014e+06 1.2857e+06 1.84006e+06 1.71403e+06 950464 952252 1.98418e+06 1.65391e+06 745510 1.00775e+06 1.1763e+06 1.34925e+06 821981 1.00294e+06 1.90422e+06 1.08135e+06 +Q02750 Q02750;A0A8I5QJ96;A0A8I5KRX5;A0A8I5KVF6;A0A8I5KXN4;A0A8I5KYB4;A0A8I5QJC1;A0A8I5QJL9;A0A8I5QJP8;B4DFY5 MP2K1_HUMAN MAP2K1 Dual specificity mitogen-activated protein kinase kinase 1 55384.5 93792.7 54382.2 68209.1 59553.6 64759.8 50657.7 55808 44552.3 48317.6 240389 97764.6 +Q02790 Q02790;H0YFG2 FKBP4_HUMAN FKBP4 Peptidyl-prolyl cis-trans isomerase FKBP4 622058 662114 473932 629299 695353 532762 731125 568092 610808 539863 720019 558152 702232 575918 1.01159e+06 704666 558241 600177 896341 601469 +Q02809 Q02809 PLOD1_HUMAN PLOD1 Procollagen-lysine,2-oxoglutarate 5-dioxygenase 1 972154 1.01657e+06 1.13569e+06 1.45934e+06 1.12096e+06 1.33223e+06 1.33341e+06 1.20622e+06 1.38247e+06 1.35345e+06 1.38342e+06 1.56681e+06 1.24552e+06 1.54854e+06 1.30299e+06 1.68108e+06 1.10211e+06 1.59219e+06 885775 1.90821e+06 +Q02818 Q02818;C9JKZ2 NUCB1_HUMAN NUCB1 Nucleobindin-1 1.60654e+06 1.34699e+06 2.27825e+06 2.76345e+06 2.3903e+06 2.01458e+06 2.39395e+06 2.02007e+06 2.85672e+06 2.59037e+06 2.73832e+06 3.25672e+06 2.7883e+06 2.35209e+06 1.73824e+06 2.67889e+06 2.65641e+06 1.866e+06 2.018e+06 2.72868e+06 +Q02878 Q02878;F8VR69;F8VZ45;U3KQR5 RL6_HUMAN RPL6 60S ribosomal protein L6 3.86292e+06 2.72039e+06 3.73627e+06 3.68776e+06 2.42136e+06 2.27333e+06 2.62708e+06 2.54668e+06 2.14389e+06 1.91399e+06 2.88087e+06 2.42061e+06 1.81674e+06 1.98681e+06 1.74274e+06 2.08373e+06 1.74985e+06 1.92495e+06 2.89916e+06 1.71781e+06 +Q03001 Q03001;F8W9J4;A0A0U1RQJ2;A0A494C073;A0A494C1U5;A0A7P0T890;E7ETB9;E9PHM6;F6QMI7;H0YC65 DYST_HUMAN DST Dystonin 116089 72627.9 171635 94348.9 49115.9 66303.1 58677.2 97401 151852 42464.5 90198.1 16671.3 204691 384048 157436 211680 288477 +Q03113;Q14344 Q03113;Q14344 GNA12_HUMAN;GNA13_HUMAN GNA12;GNA13 Guanine nucleotide-binding protein subunit alpha-12 597371 825773 746741 696576 470547 +Q03154 Q03154;A0A1B0GU86;A0A1B0GW23;C9JMV9 ACY1_HUMAN ACY1 Aminoacylase-1 199809 96680.1 205788 174252 102459 218126 75674.3 208203 140303 166699 214757 141872 102335 249465 147386 131700 +Q03167 Q03167;E9PKY4 TGBR3_HUMAN TGFBR3 Transforming growth factor beta receptor type 3 78755.5 50394.1 47234.4 95034.9 78755.9 74853.8 105832 87818 73425.9 57702 104359 121887 78405 96340.4 94731 83966.6 75277.6 74917.7 65109.2 72741.2 +Q03252 Q03252 LMNB2_HUMAN LMNB2 Lamin-B2 153355 103004 147047 165950 102330 119180 181778 168166 107177 127501 179002 196581 120294 92437.2 128647 110667 101559 82663.2 +Q04323 Q04323;A0A087WTZ5;E9PJ81;E9PRQ7 UBXN1_HUMAN UBXN1 UBX domain-containing protein 1 77448 106900 87822.7 75018.5 154127 190201 66406.3 183544 102516 122074 161743 149770 124204 134257 64708.8 44375.9 +Q04446 Q04446;E9PGM4 GLGB_HUMAN GBE1 1,4-alpha-glucan-branching enzyme 32104.9 66157.1 49289.6 34052.6 74949.8 32200.5 61543.2 26488 79221.3 60993.4 55236.3 53047.7 35320 71529.9 62604.2 24536.4 +Q04637 Q04637;E7EUU4;E7EX73;E9PGM1;A0A7I2Y1C3;C9J2Z7;C9J6B6;C9JF13;C9K073 IF4G1_HUMAN EIF4G1 Eukaryotic translation initiation factor 4 gamma 1 403560 162128 484840 369648 438447 272667 309343 383197 439184 322503 405657 465182 415918 394225 461556 445962 380044 +Q04695 Q04695;Q9QWL7;K7EPJ9;K7ESE1 SWISS-PROT:Q04695 SWISS-PROT:Q04695 383716 136290 185585 332842 160944 275865 194947 255785 322541 274215 211095 1.31063e+06 320026 2.10701e+06 142908 412793 557912 287204 111268 669459 +Q04721 Q04721 NOTC2_HUMAN NOTCH2 Neurogenic locus notch homolog protein 2 34145.1 22940.3 8680.12 39844.8 25929.5 27032 29017.2 43870.4 20686.2 51153.2 26037.5 46620.7 14958.4 +Q04726 Q04726;H0YKH0;H0YKN8;H0YKT5;H0YL70;H0YLI3;H0YLW9;H0YNT2 TLE3_HUMAN TLE3 Transducin-like enhancer protein 3 14977.4 27709.8 34702.7 33254.4 30506.2 40610.4 13213.1 34292.3 30881.1 +Q04760 Q04760 LGUL_HUMAN GLO1 Lactoylglutathione lyase 1.08033e+06 1.1378e+06 1.08026e+06 725993 1.14881e+06 911072 1.17908e+06 781856 925886 1.02655e+06 1.04092e+06 658307 1.25666e+06 902467 1.29536e+06 1.28793e+06 1.24142e+06 946136 980938 1.10004e+06 +Q04837 Q04837;C9K0U8;E7EUY5;A0A0G2JLD8 SSBP_HUMAN SSBP1 Single-stranded DNA-binding protein, mitochondrial 193547 138446 304477 182705 240362 158313 259918 129085 230544 158315 253605 154259 212229 179423 276572 161207 169743 171924 189786 138868 +Q04917 Q04917;A2IDB1;F8WEB6 1433F_HUMAN YWHAH 14-3-3 protein eta 316064 485895 272624 400733 372119 475733 446147 443324 392709 454892 414182 342254 394922 406063 281004 524922 385876 403701 361295 417286 +Q04941 Q04941 PLP2_HUMAN PLP2 Proteolipid protein 2 418125 57239.8 42857.3 41897.3 88831.9 74244.7 90983.3 73336.3 134954 77637.9 84432.2 85516.2 130246 120667 124552 114795 63964.2 114485 76825.7 50537.8 +Q05048 Q05048;A0A0A0MSZ9;A3KFI9 CSTF1_HUMAN CSTF1 Cleavage stimulation factor subunit 1 82672.5 110044 82298.3 92368.5 100906 85959.2 101030 113470 115972 123746 131110 92833.3 112340 90471.5 106216 92242.9 46637.7 122320 96169.2 129403 +Q05519 Q05519;B4DTC1;B4DWT1;Q5T760 SRS11_HUMAN SRSF11 Serine/arginine-rich splicing factor 11 74429.3 110507 150125 129359 306470 115032 334750 74151.1 149170 119901 166235 80502.8 165842 139851 149186 163663 253267 93188.2 +Q05682 Q05682;E7EX44;A0A6Q8PGI1;E9PGZ1 CALD1_HUMAN CALD1 Caldesmon 41576 53759.8 53726.7 27547.2 45651 42655.8 30318.5 19724 37672.5 25189.7 41664.5 35976.3 39767 52718.1 43784.7 +Q06124 Q06124;A0A1W2PPU4;A0A8I5KRZ3;A0A8I5QJ58;A0A8I5KVS6;A0A8I5KW48;A0A8I5QL30 PTN11_HUMAN PTPN11 Tyrosine-protein phosphatase non-receptor type 11 67226.2 109291 66237 79146.9 54372.3 86823.1 121949 93429.7 76671.8 94791.2 66421.9 79766.3 +Q06203 Q06203 PUR1_HUMAN PPAT Amidophosphoribosyltransferase 82033 96580.2 75951.4 119804 124702 80967.3 117949 78456.8 123413 131471 109201 86724.6 101291 98536.9 135526 39709.2 92026.8 106998 67970.6 +Q06210 Q06210;A0A6I8PRN4;A0A6I8PTT9 GFPT1_HUMAN GFPT1 Glutamine--fructose-6-phosphate aminotransferase [isomerizing] 1 304154 358603 143917 284705 291454 353654 245396 326536 190107 318631 247827 295163 279206 318965 244423 318377 254747 326458 291365 230294 +Q06265 Q06265;D6R905;D6RA17;D6RIY6;D6RAP4 EXOS9_HUMAN EXOSC9 Exosome complex component RRP45 72920.5 69613 68711 64197.5 83162.5 94248.8 61851.6 49437.2 53212.9 74362.1 39324.3 44892.7 114317 55712.5 60059.5 62041.5 62880.9 +Q06323 Q06323 PSME1_HUMAN PSME1 Proteasome activator complex subunit 1 409181 523530 135573 502562 344060 457011 180795 508856 231533 480158 270419 556347 264839 544491 421795 473575 237060 371852 390199 388226 +Q06481 Q06481 APLP2_HUMAN APLP2 Amyloid beta precursor like protein 2 180443 67656.2 165787 115654 195013 125586 220659 138662 235847 103122 246467 163295 202679 129990 241376 140388 124288 151009 160454 89396 +Q06830 Q06830;A0A0A0MSI0 PRDX1_HUMAN PRDX1 Peroxiredoxin-1 7.06905e+06 7.26838e+06 5.37237e+06 7.70545e+06 5.53004e+06 6.17707e+06 6.19155e+06 4.74036e+06 6.07871e+06 8.02612e+06 6.20204e+06 6.47267e+06 7.35381e+06 6.40672e+06 5.73467e+06 6.59621e+06 8.01217e+06 4.93087e+06 7.59587e+06 7.7863e+06 +Q07020 Q07020;F8VYV2;G3V203;H0YHA7;J3QQ67;A0A075B7A0 RL18_HUMAN RPL18 60S ribosomal protein L18 2.73374e+06 2.65426e+06 3.11968e+06 3.5035e+06 2.04636e+06 2.10945e+06 2.4204e+06 2.54432e+06 1.89e+06 1.77399e+06 2.52958e+06 2.19294e+06 1.60058e+06 1.85253e+06 2.00803e+06 1.90937e+06 1.39174e+06 1.91582e+06 2.05097e+06 1.69589e+06 +Q07021 Q07021;I3L3B0;I3L3Q7 C1QBP_HUMAN C1QBP Complement component 1 Q subcomponent-binding protein, mitochondrial 40530 54822.6 37556.8 20848.6 76243.4 17741.9 +Q07627 Q07627;Q8IUG1 KRA11_HUMAN KRTAP1-1 Keratin-associated protein 1-1 2.72728e+06 +Q07666 Q07666 KHDR1_HUMAN KHDRBS1 KH domain-containing, RNA-binding, signal transduction-associated protein 1 37655.1 96958.3 107712 216956 284838 100823 183421 178865 206256 57055.7 135973 123753 229997 88178.4 86770.5 129736 156311 84377.4 +Q07812 Q07812;I6LPK7 BAX_HUMAN BAX Apoptosis regulator BAX 25664.8 23246.4 18211.1 36454.2 19173 29654.5 21134.4 17089.2 20059.5 16165.9 16551.3 35841.7 +Q07954 Q07954;Q6PJ72 LRP1_HUMAN LRP1 Prolow-density lipoprotein receptor-related protein 1 33940.5 39582.8 32100.6 25877.4 37826.1 31544.7 61703.4 74400.2 34903.6 33617.3 32299.2 51340.9 48608.3 38420.2 +Q07955 Q07955;J3KTL2;J3KSR8 SRSF1_HUMAN SRSF1 Serine/arginine-rich splicing factor 1 2.65505e+06 2.00327e+06 2.75605e+06 2.71314e+06 2.00471e+06 2.02215e+06 2.54452e+06 1.91381e+06 2.06669e+06 1.83301e+06 2.36753e+06 1.9875e+06 1.61555e+06 1.77505e+06 1.75803e+06 1.7767e+06 1.57528e+06 1.54525e+06 2.02794e+06 1.23655e+06 +Q07960 Q07960;H0YE29 RHG01_HUMAN ARHGAP1 Rho GTPase-activating protein 1 68023.3 71865 45532 47933.1 64621.6 62439.4 52208 41002.8 77139 60031.9 43411.4 81736.6 57405.8 +Q08188 Q08188;A0A494C0J7 TGM3_HUMAN TGM3 Protein-glutamine gamma-glutamyltransferase E 427650 32919.5 163480 31961.6 25868 115870 96076.1 94961.4 +Q08209 Q08209;E7ETC2 PP2BA_HUMAN PPP3CA Protein phosphatase 3 catalytic subunit alpha 83290.9 140599 141932 124628 130707 112525 161263 169736 117249 122070 123536 123419 125905 92062.5 104114 107795 49810.5 +Q08211 Q08211 DHX9_HUMAN DHX9 ATP-dependent RNA helicase A 249523 208879 208147 236986 289453 311024 256227 287041 312121 280984 244864 285330 272035 315506 220312 238792 225783 271483 273327 135960 +Q08257 Q08257;A6NP24;C9JH92 QOR_HUMAN CRYZ Quinone oxidoreductase 172825 203461 197477 194914 163675 151505 204947 113370 162826 177078 151194 115493 149756 162096 191544 189432 222927 169205 183683 163425 +Q08380 Q08380;K7EJD3;K7EP36;K7EKQ5;K7ERZ6;K7ESM3 LG3BP_HUMAN LGALS3BP Galectin-3-binding protein 7.16088e+06 7.16274e+06 9.18948e+06 2.02037e+07 8.18666e+06 1.21213e+07 1.18566e+07 1.41275e+07 1.10231e+07 1.36282e+07 1.19114e+07 1.81394e+07 8.8783e+06 1.65433e+07 1.07491e+07 2.04859e+07 6.82019e+06 1.71047e+07 7.38888e+06 2.36858e+07 +Q08554 Q08554 DSC1_HUMAN DSC1 Desmocollin-1 326694 141332 127176 49824.3 189459 164855 144263 51954.3 110566 103470 129364 456378 135179 200534 81456.4 354849 126765 78279.6 362682 +Q08623 Q08623 HDHD1_HUMAN PUDP Pseudouridine-5'-phosphatase 101422 23725.8 91763.2 81758.6 109207 45753 70893.1 99113.6 111805 70167 80957.2 139717 102999 +Q08629 Q08629;A0A0A0MQX7 TICN1_HUMAN SPOCK1 Testican-1 387110 394441 561963 662223 534501 569784 619451 643156 619283 635953 693889 864655 631486 754926 532568 776893 546352 709811 468391 796413 +Q08752 Q08752 PPID_HUMAN PPID Peptidyl-prolyl cis-trans isomerase D 40117.1 42978.6 16713.6 38560.3 13646.5 22203.9 17844.3 41898.3 16944.3 +Q08945 Q08945 SSRP1_HUMAN SSRP1 FACT complex subunit SSRP1 657434 485444 531959 579950 406614 442449 425951 437948 359535 363169 462110 425686 335149 383767 353721 365475 311360 369195 574573 289812 +Q08J23 Q08J23 NSUN2_HUMAN NSUN2 RNA cytosine C(5)-methyltransferase NSUN2 593739 889423 381745 722606 702725 882190 572115 987607 478017 731112 493418 811488 454741 792487 615131 809336 425310 650896 785715 467590 +Q09028 Q09028 RBBP4_HUMAN RBBP4 Histone-binding protein RBBP4 532015 689192 917628 562637 659385 606970 714704 651351 681365 723310 700537 538781 728873 703683 535094 682992 605565 753277 598827 695215 +Q09161 Q09161 NCBP1_HUMAN NCBP1 Nuclear cap-binding protein subunit 1 93158.6 44476.2 60733.7 94925.7 80509.3 58184.3 87391.1 48495.8 76764.4 65729.6 79159.3 76966 82667.6 47761.3 57125.7 56119 94037.6 +Q09666 Q09666 AHNK_HUMAN AHNAK Neuroblast differentiation-associated protein AHNAK 768924 842139 724758 1.3799e+06 1.01331e+06 2.39457e+06 791184 2.13883e+06 1.2484e+06 2.3748e+06 893936 1.80157e+06 1.24502e+06 2.12009e+06 953517 1.68254e+06 1.38978e+06 1.77942e+06 1.26629e+06 1.66483e+06 +Q0IIK2 Q0IIK2;Q29443 TREMBL:Q0IIK2 TREMBL:Q0IIK2 4.61217e+06 3.23189e+06 5.07343e+06 2.21454e+06 5.28733e+06 2.63718e+06 4.65794e+06 1.33211e+06 6.07067e+06 3.24676e+06 4.01133e+06 1.90639e+06 6.88137e+06 2.62417e+06 5.1331e+06 2.21059e+06 8.70219e+06 2.99006e+06 2.3866e+06 2.87254e+06 +Q0V8M9 Q0V8M9 TREMBL:Q0V8M9;Q9TRI0 TREMBL:Q0V8M9;Q9TRI0 55682.7 43951.6 101827 71544.7 69480.6 56541.6 63566 111305 74097.4 51356.8 101333 106131 96810 38973.4 56555.9 89165.7 68019.1 35131.1 64719.1 +Q0VCM5 Q0VCM5 TREMBL:Q0VCM5 TREMBL:Q0VCM5 32955 27466.3 25648 62464.4 37855.8 47320.6 35416.6 50402.4 32825.6 47838.7 38358.4 43715.1 42536.3 28855.2 24450.4 44073.3 12200.3 +Q0VDF9 Q0VDF9 HSP7E_HUMAN HSPA14 Heat shock 70 kDa protein 14 40215.5 40518.3 42381.6 65431.6 66834.2 24756.4 51653.7 20924.5 44992 48903.3 32537.6 47268.6 59666.9 +Q10469 Q10469 MGAT2_HUMAN MGAT2 Alpha-1,6-mannosyl-glycoprotein 2-beta-N-acetylglucosaminyltransferase 123050 16269.1 38916 144230 46658.5 119155 63356 161073 46137.6 147852 69882.9 147353 75898.3 132337 51425.9 94438 54192.8 70155.5 +Q10471 Q10471 GALT2_HUMAN GALNT2 Polypeptide N-acetylgalactosaminyltransferase 2 1.26149e+06 1.53353e+06 1.41537e+06 2.24575e+06 1.92082e+06 2.37162e+06 2.47733e+06 1.94708e+06 2.18623e+06 2.43457e+06 2.22259e+06 2.84531e+06 2.01071e+06 2.85591e+06 2.55799e+06 2.84917e+06 1.54346e+06 2.063e+06 1.52463e+06 2.63134e+06 +Q10567 Q10567;C9J1E7 AP1B1_HUMAN AP1B1 AP-1 complex subunit beta-1 231741 173416 131663 180465 187500 204104 172158 182109 163306 193663 193337 209320 225100 234411 176299 189921 171684 179468 184661 181421 +Q12792 Q12792 TWF1_HUMAN TWF1 Twinfilin-1 303728 225942 227306 235080 201766 191485 252847 182978 237805 237142 255762 188796 213014 210169 166234 239895 169182 330372 +Q12794 Q12794 HYAL1_HUMAN HYAL1 Hyaluronidase-1 26110.2 28117.1 62265.4 65611.8 57511.1 99413.7 78817.8 127424 100959 86249.4 67575.5 19117.5 40615.8 37790.3 29225.9 +Q12805 Q12805;A0A0U1RQV3 FBLN3_HUMAN EFEMP1 EGF-containing fibulin-like extracellular matrix protein 1 1.25276e+07 4.66113e+06 1.20102e+07 9.71961e+06 1.96157e+07 8.21438e+06 1.6107e+07 6.96166e+06 1.97113e+07 9.43713e+06 1.50053e+07 8.93277e+06 2.15784e+07 1.04395e+07 1.96352e+07 6.67799e+06 1.5635e+07 7.73998e+06 1.06222e+07 8.52371e+06 +Q12824 Q12824;G5E975;C9JTA6;A0A0G2JSE9;A0A0U1RQQ2;A0A0U1RRB8;A0A2R8Y5N5;A0A2R8YEE8 SNF5_HUMAN SMARCB1 SWI/SNF-related matrix-associated actin-dependent regulator of chromatin subfamily B member 1 74539.6 65216.8 43829.9 241845 140799 182331 108289 154191 84522.8 142119 133069 179602 244469 97517.8 90454.2 93977.3 +Q12874 Q12874 SF3A3_HUMAN SF3A3 Splicing factor 3A subunit 3 81775 80390 59243.9 107561 144660 151588 103960 148275 116447 91261.9 119838 165694 120805 174030 87861 130555 88682.7 112945 125776 53009.5 +Q12888 Q12888;A6NNK5 TP53B_HUMAN TP53BP1 TP53-binding protein 1 41664.1 46715.4 42057 58112.4 +Q12904 Q12904;A0A5F9ZHC5;A0A804HJ47;A0A8C8KIA0;A0A8C8MUD1 AIMP1_HUMAN AIMP1 Aminoacyl tRNA synthase complex-interacting multifunctional protein 1 128633 195540 225547 199500 194569 225139 179260 189900 115572 196183 175767 199665 144131 228645 187682 224397 143759 169842 194903 99882 +Q12905 Q12905;B4DY09 ILF2_HUMAN ILF2 Interleukin enhancer-binding factor 2 739175 744855 816909 856717 926631 942508 792675 850121 848526 820655 759951 984599 888642 780590 624250 734172 872759 864981 729956 640650 +Q12906 Q12906 ILF3_HUMAN ILF3 Interleukin enhancer-binding factor 3 606598 684143 718182 770802 773776 718189 657928 750729 777415 730632 627518 865688 705252 693083 427562 757240 686593 663275 574118 590214 +Q12907 Q12907;A0A8Q3WK65;D6RBV2;D6RDX1 LMAN2_HUMAN LMAN2 Vesicular integral-membrane protein VIP36 873226 550973 948000 799271 623123 643523 908961 584833 924489 975340 925473 931325 1.02856e+06 837984 584067 936605 1.28202e+06 1.1691e+06 738587 1.22143e+06 +Q12913 Q12913;A0A8V8TP51 PTPRJ_HUMAN PTPRJ Receptor-type tyrosine-protein phosphatase eta 506699 191957 676928 393985 503065 262146 601349 240300 693938 372874 580014 301448 493483 373900 589046 383416 863298 404059 387695 311847 +Q12931 Q12931;I3L0K7;I3L239 TRAP1_HUMAN TRAP1 Heat shock protein 75 kDa, mitochondrial 253258 151304 170451 275373 243067 170909 137610 221033 195945 159007 229387 242775 223818 213028 145689 137763 100568 133816 150655 119420 +Q12996 Q12996;E9PLP8 CSTF3_HUMAN CSTF3 Cleavage stimulation factor subunit 3 110301 92538.2 88744 99939.2 122650 104759 179027 105113 116030 107722 137055 225977 139577 96674.3 118995 42950.8 114318 117932 41549.8 +Q13011 Q13011;M0QZW4;M0R248 ECH1_HUMAN ECH1 Delta(3,5)-Delta(2,4)-dienoyl-CoA isomerase, mitochondrial 149906 152840 341820 185429 194732 136767 228324 116022 220560 163611 177423 146262 194034 153871 194407 173439 148688 149959 133520 162305 +Q13045 Q13045 FLII_HUMAN FLII Protein flightless-1 homolog 55829.6 43114.5 38113 57336.3 53250.6 65863.7 72472.6 60027.7 45143.6 63988.3 55663.3 80382.2 51517 45523.9 45581.1 +Q13098 Q13098;A0A096LP07;A0A096LPJ3;A8K070;C9JFE4 CSN1_HUMAN GPS1 COP9 signalosome complex subunit 1 166405 149326 92752.2 132345 136544 122871 165408 135898 84037.7 98772.9 125184 131555 107317 115329 130152 94017.1 77367.4 114092 237530 74543.6 +Q13107 Q13107;Q9Y4E8;H7C189 UBP4_HUMAN USP4 Ubiquitin carboxyl-terminal hydrolase 4 81399.7 66127.8 23951.5 56641.3 73391.4 72107 70817.1 73405.4 42795.2 48816.5 42544.5 44791.9 60050.4 57418.3 73575.9 58825.3 41379 35391.9 77174 +Q13112 Q13112 CAF1B_HUMAN CHAF1B Chromatin assembly factor 1 subunit B 53155.4 26833 34141.1 66949.4 24806.3 29126.1 53620.6 66048.7 21838 60857.5 79676.3 37917.5 36817.9 41659 28889.8 62670.6 +Q13126 Q13126;B4DUC8 MTAP_HUMAN MTAP S-methyl-5'-thioadenosine phosphorylase 113976 132463 109912 130000 170367 115254 136929 102978 161253 120365 139181 127723 140191 104083 115803 96783 148912 100725 91629.3 +Q13148 Q13148;A0A087WX29;A0A0A0N0M3;A0A087WV68;A0A087WW61;A0A087WX67;A0A087WXQ5;A0A087WXV3;A0A087WYY0;A0A087X260;B1AKP7;G3V162;K7EJM5;K7EN94 TADBP_HUMAN TARDBP TAR DNA-binding protein 43 20528.9 30173.5 82319.7 130888 24012.4 105753 60753.3 96721.3 30549.5 49858.9 58601.1 106822 47560.4 58472.1 32004.5 135748 76517.1 +Q13155 Q13155;A8MU58 AIMP2_HUMAN AIMP2 Aminoacyl tRNA synthase complex-interacting multifunctional protein 2 206385 290679 186096 303558 231533 361289 235672 462139 154433 266097 222697 295058 189817 307214 293853 318737 158068 296484 330863 205963 +Q13162 Q13162;A6NG45 PRDX4_HUMAN PRDX4 Peroxiredoxin-4 356548 273436 359139 479585 301575 398937 308848 325842 218103 498498 272071 489098 246542 344234 229786 332628 335927 183504 208418 270482 +Q13177 Q13177;H7C1X3 PAK2_HUMAN PAK2 Serine/threonine-protein kinase PAK 2 48658.7 60576.7 63615.2 66705.5 105461 63640.1 90922.7 80763.1 29479 50182.8 33120.5 74819.5 95390.2 62414.2 61252.6 95907.3 +Q13185 Q13185 CBX3_HUMAN CBX3 Chromobox protein homolog 3 386063 389276 518763 533612 562949 485682 589589 404772 481674 561443 477281 497122 320705 360326 588061 334416 320120 309619 469550 181356 +Q13200 Q13200 PSMD2_HUMAN PSMD2 26S proteasome non-ATPase regulatory subunit 2 203054 210560 108760 166090 173347 218388 121498 207419 125817 194023 163311 207785 194009 219053 115722 183979 143716 160196 236508 134285 +Q13214 Q13214;A0A0C4DGV8;B4DEK9 SEM3B_HUMAN SEMA3B Semaphorin-3B 534957 241319 524896 446769 636540 342958 827646 399784 566751 323511 887444 560054 577241 333291 931892 469532 582373 324076 700378 520647 +Q13219 Q13219 PAPP1_HUMAN PAPPA Pappalysin-1 82156.1 33324.2 101504 56260.3 65380.8 47149.2 87204.6 38195.4 55989.3 33681.4 102137 53018.8 66384.4 30152 112692 50933.2 83170.2 58187.8 71574.3 61565.9 +Q13228 Q13228 SBP1_HUMAN SELENBP1 Methanethiol oxidase 612647 520180 200130 426143 242139 470882 239254 309051 274138 580122 214781 306045 225846 465741 188913 419588 338025 508206 228444 416687 +Q13242 Q13242 SRSF9_HUMAN SRSF9 Serine/arginine-rich splicing factor 9 145733 82796 92548.2 119064 193729 168009 139958 140182 198605 172349 140681 142704 135540 167635 123743 114303 200150 169217 +Q13243 Q13243 SRSF5_HUMAN SRSF5 Serine/arginine-rich splicing factor 5 50560.7 20319.5 29294.4 39378.3 32837.5 33546 28657.9 48050.8 18824.4 30001.2 23847.5 26600.8 32608.8 72687.4 +Q13247 Q08170;Q13247;A0A590UK80;A0A0D9SEM4;A0A590UJK4;A0A590UJP7;A0A590UK01 SRSF6_HUMAN SRSF6 Serine/arginine-rich splicing factor 6 2.12521e+06 1.8019e+06 2.20613e+06 2.11395e+06 1.96295e+06 1.78359e+06 2.31477e+06 1.99221e+06 1.73461e+06 1.62466e+06 2.18788e+06 1.7452e+06 1.61132e+06 1.82472e+06 1.93677e+06 1.82594e+06 1.23306e+06 1.45213e+06 2.01383e+06 1.13613e+06 +Q13257 Q13257 MD2L1_HUMAN MAD2L1 Mitotic spindle assembly checkpoint protein MAD2A 127518 97335.5 124920 141237 +Q13263 Q13263;M0R0K9 TIF1B_HUMAN TRIM28 Transcription intermediary factor 1-beta 1.71852e+06 1.49848e+06 1.24325e+06 1.46803e+06 1.89683e+06 1.55499e+06 1.39708e+06 1.46209e+06 1.67831e+06 1.43393e+06 1.50472e+06 1.40474e+06 1.55031e+06 1.38636e+06 1.16358e+06 1.15293e+06 1.31521e+06 1.25379e+06 1.87604e+06 704991 +Q13275 Q13275;C9JPG5;C9J1V2;C9J4H5;H7C4A2 SEM3F_HUMAN SEMA3F Semaphorin-3F 314867 172690 208368 280319 466074 292562 414053 293297 452163 342864 451619 385500 434956 314251 340366 399652 263353 301072 300036 358147 +Q13283 Q13283;Q9UN86;A0A7I2V2R0;A0A7I2V2S5;A0A7I2V3C4;A0A7I2V3E2;A0A7I2V4Y8;A0A7I2V5M7;A0A7I2V651;A0A7I2YQD9;A0A7I2YQN9;A0A7I2YQS2;A0A7I2YQU8;A0A7P0P2U7;D6R9A4;D6RAC7;D6RB17;D6RBR0;D6RBW8;D6RGJ4;E5RH00;E5RH42;E5RI46;E5RIF8;E5RJU8 G3BP1_HUMAN G3BP1 Ras GTPase-activating protein-binding protein 1 399809 712665 721090 635069 639909 908249 500418 774596 669412 727802 398688 680816 477149 738840 378201 594466 464762 701409 458193 330657 +Q13310 Q13310;A0A7I2V4L7;A0A7I2V598;A0A7I2V5W9;B1ANR0;H0Y5F5;H0YEU6 PABP4_HUMAN PABPC4 Polyadenylate-binding protein 4 153029 199178 161649 209074 348257 340305 247429 268009 310523 289820 252641 236531 375092 296273 247228 227851 230626 279149 263931 209763 +Q13332 Q13332;G8JL96 PTPRS_HUMAN PTPRS Receptor-type tyrosine-protein phosphatase S 102109 56100.8 167575 123054 132615 88819.5 195742 122616 102117 92390.1 173324 110926 149480 127799 233265 195261 85387.3 107987 105155 167880 +Q13347 Q13347;A0A7I2V3U3;A0A7I2V5V8;A0A7I2YQC8;A0A7I2YQF8;A0A7I2YQT9;A0A7I2YQU2;A0A7I2V4C2 EIF3I_HUMAN EIF3I Eukaryotic translation initiation factor 3 subunit I 231571 258617 180343 240003 306676 311589 201385 273399 296082 300022 241834 236575 281610 283715 258697 211165 197479 246707 280513 155403 +Q13404 Q13404;I3L0A0;G3V2F7 UB2V1_HUMAN UBE2V1 Ubiquitin-conjugating enzyme E2 variant 1 513818 715206 541143 609049 604468 518116 690591 443245 404376 508183 510615 322758 393831 464343 611942 561350 636531 630944 618241 550635 +Q13409 Q13409;E7EMU4;E7EQL5;E7EQU2;E7ERH4;E7ERR6;E7ESD3;E7ET01;E7ETL8;E7EUM4;E7EV09;E9PGG1 DC1I2_HUMAN DYNC1I2 Cytoplasmic dynein 1 intermediate chain 2 74562.7 80274 110261 68511.8 105272 58774.7 91613.9 73907 120667 82378.1 97219.5 43810.4 74685.7 89562.4 54026.9 110497 92630 109393 30642.1 +Q13421 Q13421;H3BMA1;H3BR90;H3BUX1 MSLN_HUMAN MSLN Mesothelin 450986 1.02535e+06 695030 2.19612e+06 896602 1.88576e+06 730091 1.80864e+06 973511 2.59917e+06 828160 2.38592e+06 982587 2.28733e+06 821138 1.83937e+06 799659 1.97831e+06 559036 2.07478e+06 +Q13428 Q13428;A0A3B3IS06;A0A6I8PRA0;E7ETY2 TCOF_HUMAN TCOF1 Treacle protein 172766 251400 140040 346761 209304 294958 165204 298785 153256 249527 199379 315665 224603 320317 181498 272751 166955 275972 206533 221500 +Q13435 Q13435;E9PPJ0;H0YEX5 SF3B2_HUMAN SF3B2 Splicing factor 3B subunit 2 235889 181956 240624 274312 360332 421814 344761 372227 412267 368165 337163 418607 285605 383872 247528 339942 213144 297143 336762 236574 +Q13438 Q13438;A0A8V8TPZ4;A0A8V8TPZ9;A0A8V8TQI0;A0A8V8TQI2;A0A8V8TQI4;A0A8V8TQI8;A0A8V8TR31;A0A8V8TR34;A0A8V8TRD7;A0A8V8TRE0;B4E321;F8VZI7;F8W0R2;F8W1N0;F8W1V2 OS9_HUMAN OS9 Protein OS-9 80309.6 43154.7 58592.5 98950.7 104489 106435 119303 123781 123320 97768.8 84333.3 144162 115516 162866 71166.6 116657 145029 31990.4 76022.4 +Q13442 Q13442 HAP28_HUMAN PDAP1 28 kDa heat- and acid-stable phosphoprotein 427128 396170 322623 286090 377756 484732 435446 364612 468208 513606 441846 334927 357699 383983 395049 347556 340941 363465 429240 262281 +Q13443 Q13443;A0A7I2V2E1;A0A7I2V2Z6;A0A7I2V303;A0A7I2V3B5;A0A7I2V3C1;A0A7I2V3W3;A0A7I2V4B3;A0A7I2V5N8;A0A7I2V5Q0;A0A7I2YQ97;A0AVL1;F8WC54;A0A7I2V5C0 ADAM9_HUMAN ADAM9 Disintegrin and metalloproteinase domain-containing protein 9 64002.3 70627.9 64662.3 137365 134756 161496 151204 129330 146307 165350 127811 170611 132846 131986 57405.1 187668 85891.8 83177.7 99360.2 140753 +Q13444 Q13444 ADA15_HUMAN ADAM15 Disintegrin and metalloproteinase domain-containing protein 15 24383.9 33489.9 102369 65752.7 80729.7 41794.2 142325 81186.4 107136 76984.4 132248 68633.9 126641 66861.9 94042.3 56408.9 84592.7 55766.8 50623.5 +Q13501 Q13501;E7EMC7;C9JRJ8;E3W990;E9PFW8 SQSTM_HUMAN SQSTM1 Sequestosome-1 84915.4 201703 100495 237152 93871.2 226545 95987.9 226627 86178.5 217754 95048.6 185360 68037 199760 104421 184070 94747.4 191253 111157 88084.3 +Q13509 Q13509;A0A0B4J269 TBB3_HUMAN TUBB3 Tubulin beta-3 chain 63171.3 99285.8 152304 107820 139099 242505 +Q13510 Q13510;A0A1B0GTM3;A0A1B0GTZ5;A0A1B0GUA4;A0A1B0GUE3;A0A1B0GUG1;A0A1B0GUH5;A0A1B0GW68;E7EMM4;A0A1B0GTP7;A0A1B0GUW4;A0A1B0GV06 ASAH1_HUMAN ASAH1 Acid ceramidase 698952 392464 621878 773468 859540 530018 851977 763911 965030 686699 834470 887965 1.03781e+06 723300 589034 944297 798309 839210 522782 1.10399e+06 +Q13561 Q13561;A0A7I2V2Z2;A0A7I2V390;A0A7I2V4H4;F8VRV7;F8VW18;F8W1I6;F8W0U6 DCTN2_HUMAN DCTN2 Dynactin subunit 2 244529 228200 202028 257795 259832 255034 229841 234626 189332 296708 243611 202794 300495 244759 256832 217061 258754 194587 234584 190814 +Q13564 Q13564 ULA1_HUMAN NAE1 NEDD8-activating enzyme E1 regulatory subunit 42613.4 105489 117093 93897.1 102948 65058.7 123383 64255.6 124467 95865.5 101383 122265 100770 78377.7 +Q13595 Q13595 TRA2A_HUMAN TRA2A Transformer-2 protein homolog alpha 431903 340447 474422 596677 368085 285283 524127 377398 324091 235524 532864 476840 209546 229953 441190 306965 171611 202381 435005 108922 +Q13596 Q13596;H0YK43 SNX1_HUMAN SNX1 Sorting nexin-1 12478.1 25980.4 35979.2 24113 15171.8 20724.7 17634.2 +Q13610 Q13610;B4DJV5 PWP1_HUMAN PWP1 Periodic tryptophan protein 1 homolog 173907 163514 144768 177253 193625 166518 154024 185945 133174 224545 154801 216471 188708 184519 173451 173129 130342 125838 142354 +Q13616 Q13616;A0A590UJ21;A0A590UJ50;A0A590UJC4;A0A590UJR3;A0A590UK34 CUL1_HUMAN CUL1 Cullin-1 115832 113389 55851.7 129211 85591.7 87558.1 111234 88791.5 61292.8 53337.5 114966 106981 74045.9 59185.1 94377.4 134126 60421.7 88037.3 159982 101527 +Q13617 Q13617;A0A0A0MTN0;Q5T2B5;A0A0D9SET6;A0A8I5KPX0;A0A8I5KQQ5;A0A8I5KR51;A0A8I5KS98;A0A8I5KSW7;A0A8I5KTY2;A0A8I5KTY5;A0A8I5KU22;A0A8I5KUA0;A0A8I5KUA9;A0A8I5KV54;A0A8I5KVE2;A0A8I5KVR3;A0A8I5KWB8;A0A8I5KXB2;A0A8I5KYA3;A0A8I5KYM7;A0A8I5KYP8;A0A8I5KYZ8;A0A8I5QJI3;A0A8I5QKN3;Q5T2B7 CUL2_HUMAN CUL2 Cullin-2 29998.7 32511.9 31639.2 21698 34023 16669.2 56750.4 57355.1 33684.9 +Q13619 Q13619;Q13620;A0A669KAX4;A0A7P0T954;A0A7P0T9L3;A0A7P0TAQ3;A0A804CL36;K4DI93;A0A0A0MR50;A0A7P0T8P8;A0A7P0T9C8;A0A7P0T9D0 CUL4A_HUMAN CUL4A Cullin-4A 73737.6 76609.1 67563.9 82877.6 67498.1 89643.3 61958.2 102566 90639 67585.3 106319 50507.5 80222.7 64663 98519.7 42553.3 65144.4 93723.4 60228.2 +Q13620 Q13620;A0A669KAX4;A0A7P0T954;A0A7P0T9L3;A0A7P0TAQ3;A0A804CL36;K4DI93;A0A669KBG9;A0A7P0T8P8;A0A7P0T9C8;A0A7P0T9D0;A0A7P0T9P5;A0A7P0TAF9 CUL4B_HUMAN CUL4B Cullin-4B 16344.4 43232.9 35018.2 53294.7 43412.9 18686.7 22833.1 43898.2 27132.2 36026 +Q13630 Q13630;H0YCP7 FCL_HUMAN GFUS GDP-L-fucose synthase 1.11711e+06 1.18112e+06 1.20815e+06 984145 951865 1.10358e+06 1.24797e+06 855338 1.20607e+06 1.3457e+06 1.12345e+06 999680 1.23603e+06 1.05722e+06 1.29747e+06 1.22585e+06 1.71959e+06 1.43084e+06 1.10843e+06 1.08763e+06 +Q13685 Q13685;C9JEH3;C9JG97;C9JTS3 AAMP_HUMAN AAMP Angio-associated migratory cell protein 26496.4 23222 42861.5 29474.6 34680.3 29103.8 24924.2 38683 21489.3 11630.5 24738 22885 31268.5 +Q13813 Q13813;A0A0D9SF54;A0A0D9SGF6 SPTN1_HUMAN SPTAN1 Spectrin alpha chain, non-erythrocytic 1 405815 254977 374320 299519 401102 321687 341370 306865 387489 337135 365952 323476 318548 338621 314893 296723 311318 295286 384762 178855 +Q13835 Q13835 PKP1_HUMAN PKP1 Plakophilin-1 747678 242403 26918.7 17712.7 35412.2 132270 37197.9 56857.4 139054 112471 +Q13838 Q13838;Q5STU3;A0A0G2JJZ9;A0A140T9X3;H0Y400 DX39B_HUMAN DDX39B Spliceosome RNA helicase DDX39B 1.65666e+06 1.36928e+06 1.20898e+06 1.46536e+06 1.46951e+06 1.28127e+06 1.37764e+06 1.46712e+06 1.29093e+06 1.09279e+06 1.33551e+06 1.48858e+06 1.14288e+06 1.32893e+06 1.28114e+06 1.25771e+06 1.22394e+06 1.17685e+06 1.27666e+06 882065 +Q13867 Q13867;K7ES02;J3KSD8;K7ESE8 BLMH_HUMAN BLMH Bleomycin hydrolase 2.62888e+06 851773 1.04173e+06 699870 849515 738471 974295 382979 790599 780685 1.0239e+06 612738 661276 558823 1.02958e+06 789561 1.56037e+06 782150 1.14244e+06 1.44209e+06 +Q13868 Q13868;A0A8I5KVY3;A3KFL2;A3KFL5;A0A8I5KV47;A0A8I5QL34;B4E001 EXOS2_HUMAN EXOSC2 Exosome complex component RRP4 188238 185492 202975 198317 210949 157748 209084 171465 227308 161796 180508 235986 229998 184446 197282 215218 197452 +Q13885 Q13885;Q9BVA1 TBB2A_HUMAN TUBB2A Tubulin beta-2A chain 179907 35488.2 8508.88 15451.3 8697.36 12481.5 32037.5 1.12641e+06 26484.6 55529 506339 +Q13895 Q13895;F8WBL2 BYST_HUMAN BYSL Bystin 5716.84 8336.43 16778.6 18521.7 12172.7 14199.6 28364.3 +Q13907 Q13907;A0A8Q3WKR8 IDI1_HUMAN IDI1 Isopentenyl-diphosphate Delta-isomerase 1 115172 128230 212189 108461 95270.9 87766.5 115844 68010 94856.7 122148 124797 117909 84906.7 80902.8 58035.9 95439.3 112718 90593.9 99318.4 133284 +Q14019 Q14019 COTL1_HUMAN COTL1 Coactosin-like protein 1.5768e+06 1.66971e+06 1.22554e+06 1.52783e+06 1.08007e+06 1.36724e+06 1.28119e+06 860705 955860 1.86351e+06 1.24722e+06 1.32355e+06 1.10745e+06 1.35281e+06 1.39624e+06 1.46274e+06 2.81259e+06 1.60196e+06 1.95726e+06 2.04352e+06 +Q14103 Q14103;D6RAF8;H0Y8G5;H0YA96 HNRPD_HUMAN HNRNPD Heterogeneous nuclear ribonucleoprotein D0 635403 630876 693165 721272 873672 1.01115e+06 567587 698452 906107 1.04727e+06 504744 850478 822163 990188 513178 705980 1.19234e+06 899574 517427 647837 +Q14108 Q14108;A0A1W2PPU6;A0A1W2PQB7;A0A1W2PRS1;A0A1W2PS43;A0A1W2PNX7;A0A1W2PPX5;A0A1W2PQR6;A0A1W2PS70;A0A1W2PSE4;D6RDG0 SCRB2_HUMAN SCARB2 Lysosome membrane protein 2 67487.4 23198.3 19151.5 11198.6 12868.6 16529.1 42697.7 +Q14114 Q14114;A0A590UJ57;A0A590UJ98;A0A590UJF9;A0A590UK45;A0A590UK84;A0A590UK97;E9PKG2;E9PP15 LRP8_HUMAN LRP8 Low-density lipoprotein receptor-related protein 8 70515.3 52364.7 106657 132387 82420.8 89705.2 88565 60701.6 110370 127678 109386 132474 62106 106645 128546 116503 125821 130196 71077.6 185779 +Q14116 Q14116 IL18_HUMAN IL18 Interleukin-18 213398 162571 114719 142193 175905 89594.8 180525 130411 109370 124587 183404 126976 186920 115068 222393 106850 140509 71797.9 186471 74220.4 +Q14118 Q14118 DAG1_HUMAN DAG1 Dystroglycan 1 7.32805e+06 3.84319e+06 7.67435e+06 7.40992e+06 1.0523e+07 7.61776e+06 1.00797e+07 4.77046e+06 1.09425e+07 9.32063e+06 1.05439e+07 8.08052e+06 1.13277e+07 8.75699e+06 1.17266e+07 6.70769e+06 8.71357e+06 6.27539e+06 7.21677e+06 8.57861e+06 +Q14126 Q14126;A0A804HJ09 DSG2_HUMAN DSG2 Desmoglein-2 336763 331390 596980 493552 302586 443147 404006 259125 359767 548844 456648 383625 324592 414480 383666 445430 727638 471071 296422 505772 +Q14134 Q14134;E9PIQ2;E9PPX1;E9PRL4;H0YD78 TRI29_HUMAN TRIM29 Tripartite motif-containing protein 29 136136 44047.4 52525.8 44143.8 123078 81625.7 108919 36793.1 86303.1 112574 68271.8 81788.6 +Q14141 Q14141;B1AMS2 SEPT6_HUMAN SEPTIN6 Septin-6 37293.3 31651 33946 25331.7 +Q14151 Q14151 SAFB2_HUMAN SAFB2 Scaffold attachment factor B2 21348.9 26213.4 27479.9 27737.5 +Q14152 Q14152 EIF3A_HUMAN EIF3A Eukaryotic translation initiation factor 3 subunit A 548228 509300 352941 449669 538949 515723 453487 487623 502162 445843 513983 401011 562030 451829 451086 456252 378671 444359 619803 333913 +Q14157 Q14157;F8W726 UBP2L_HUMAN UBAP2L Ubiquitin-associated protein 2-like 28850.3 58352.5 112732 174689 44863.2 111200 145340 197547 49669.1 93124 124786 213468 75582.3 84165 101546 166821 19106.9 40823.7 +Q14166 Q14166;V9GY16 TTL12_HUMAN TTLL12 Tubulin--tyrosine ligase-like protein 12 71840.6 81941.9 56724 84229.3 89633.6 57684.1 97870.3 77996.5 69560.1 66422.5 69063.4 72752.7 82501.7 71120.7 48127.2 49150.3 123411 62323.4 +Q14192 Q14192 FHL2_HUMAN FHL2 Four and a half LIM domains protein 2 56740.8 48177.1 77274.4 30132.6 47563.2 39230.4 81614.4 91354.9 58435.1 44468.2 +Q14203 Q14203;E7EX90;A0A7P0Z4C3;A0A804CDA6;E7EWF7 DCTN1_HUMAN DCTN1 Dynactin subunit 1 25374.6 41210 36174.5 69085.9 41280.1 44162 36908.9 30717.8 23607.6 65260.4 +Q14204 Q14204;A0A2R8Y5T0;A0A2R8Y6H2;A0A2R8Y706;A0A2R8YFZ7;A0A7P0T7V5;A0A7P0T825;A0A7P0T8N2;A0A7P0T8W5;A0A7P0T916;A0A7P0T934;A0A7P0T9C4;A0A7P0T9F1;A0A7P0T9K2;A0A7P0T9R4;A0A7P0T9S3;A0A7P0T9Y3;A0A7P0TA13;A0A7P0TAF3;A0A7P0TAM4;A0A7P0TAW9;A0A7P0TB64;A0A7P0TB75;A0A7P0TBJ7;A0A2R8YGC7;A0A7P0T942;A0A7P0T990;A0A7P0TAV5;A0A7P0TBC9;A0A804HHY6 DYHC1_HUMAN DYNC1H1 Cytoplasmic dynein 1 heavy chain 1 398414 410203 309088 345044 555695 271003 521756 513893 378816 292735 439379 291121 487521 268109 502746 224394 377826 438814 409522 +Q14232 Q14232 EI2BA_HUMAN EIF2B1 Translation initiation factor eIF-2B subunit alpha 61609.9 36933.7 30729 49686.6 45650.2 36054.7 52068.1 35783.8 36110.3 21397.2 50513.3 37670.9 55156.1 74712.3 +Q14247 Q14247 SRC8_HUMAN CTTN Src substrate cortactin 86861.6 82226.1 77781.7 114247 210099 327467 187325 178947 319027 414813 196438 189666 199065 253017 201760 177597 282158 225091 198851 135463 +Q14258 Q14258;A0A3B3IUA7 TRI25_HUMAN TRIM25 E3 ubiquitin/ISG15 ligase TRIM25 72108.2 60346.1 60319 65855.4 116206 97936.5 32156.8 96016.7 74313.4 91649.5 96125.5 71991.5 84371.7 88450.9 132568 +Q14315 Q14315 FLNC_HUMAN FLNC Filamin-C 722841 1.0394e+06 933480 1.30181e+06 1.14904e+06 1.38781e+06 1.0417e+06 1.42916e+06 921762 1.29699e+06 916698 1.16493e+06 776315 1.18669e+06 1.05829e+06 1.06619e+06 663458 1.16417e+06 704002 773925 +Q14320 Q14320;Q9Y247;B0S8I6 FA50A_HUMAN FAM50A Protein FAM50A 40111.2 41468.2 56203.4 81377.4 73113 50655.6 78070.3 39129.8 75696.8 48167.6 67435.9 109204 58898.5 13650.1 53482 49843.7 42076.7 73063.4 47506.5 +Q14444 Q14444;E9PLA9;G3V153 CAPR1_HUMAN CAPRIN1 Caprin-1 87676.7 302390 212442 237442 431327 609097 209336 314775 526364 684656 119188 362516 469591 557430 169364 336143 452294 614200 109963 294956 +Q14498 Q14498;Q86U06;A0A0U1RQH7;G3XAC6;H0Y4X3;Q5QP22;Q5QP23 RBM39_HUMAN RBM39 RNA-binding protein 39 100243 62691.8 71229.6 137917 91770.2 122960 88483.8 181948 134402 107994 112135 160514 83435.3 141620 60843.6 130026 121466 98351.8 192361 +Q14517 Q14517 FAT1_HUMAN FAT1 Protocadherin Fat 1 236456 53471.9 336751 95662.5 306535 77330.6 378405 71100.6 326953 82462.6 370982 98965.8 308995 95236.4 375914 111705 304316 119279 226177 113136 +Q14525 Q14525;Q15323;Q9UE12;A0A140TA69 SWISS-PROT:Q14525 SWISS-PROT:Q14525 5.51666e+07 61696.3 138885 100229 519520 280874 +Q14532 Q14532 SWISS-PROT:Q14532 SWISS-PROT:Q14532 3.87275e+06 334612 431472 381239 +Q14533 Q14533 SWISS-PROT:Q14533 SWISS-PROT:Q14533 2.36488e+06 +Q14566 Q14566 MCM6_HUMAN MCM6 DNA replication licensing factor MCM6 346002 413448 353313 445289 404076 414355 350114 420101 326568 377053 369228 378848 319669 372553 324456 322697 250428 343156 339881 188409 +Q14651 Q14651 PLSI_HUMAN PLS1 Plastin-1 18697.6 38213.3 47813.8 56830.8 20308.1 +Q14683 Q14683;G8JLG1;A0A6Q8PHC3 SMC1A_HUMAN SMC1A Structural maintenance of chromosomes protein 1A 165161 188185 120387 208082 158870 211268 141185 215988 142530 188653 157984 217151 133967 188553 136635 177054 94351.9 165801 170269 115166 +Q14692 Q14692 BMS1_HUMAN BMS1 Ribosome biogenesis protein BMS1 homolog 58510.9 70328.2 16968.6 55821.3 97474.8 +Q14694 Q14694 UBP10_HUMAN USP10 Ubiquitin carboxyl-terminal hydrolase 10 65110.9 76576.6 76163.8 87696.7 117901 48054.8 87257.1 75768.2 121585 63436.3 118400 104679 68018.8 77135.4 121704 118926 +Q14697 Q14697;E9PKU7;F5H6X6 GANAB_HUMAN GANAB Neutral alpha-glucosidase AB 314122 288384 159755 392792 341381 362250 254044 406438 368939 318664 278758 494406 310881 428257 285957 352179 197195 339305 201444 250752 +Q14703 Q14703 MBTP1_HUMAN MBTPS1 Membrane-bound transcription factor site-1 protease 317484 244029 390454 387871 414513 322432 514115 307130 419764 396689 461324 415072 464748 395143 521477 447815 482200 421171 316233 424710 +Q14738 Q14738;E9PFR3;H0Y8C4;H7C5Q9 2A5D_HUMAN PPP2R5D Serine/threonine-protein phosphatase 2A 56 kDa regulatory subunit delta isoform 50284.1 81358.8 62186.5 83079.6 82327.8 63999.7 49259.3 86544 15788.9 64414.5 50137 45615 43765.9 +Q14764 Q14764 MVP_HUMAN MVP Major vault protein 107227 119981 109528 123043 119386 125580 122759 111861 77541.2 110876 98903.7 94086.8 100269 101478 107280 104871 151715 112895 +Q14839 Q12873;Q14839;Q8TDI0;A0A0C4DGG9;A0A2R8Y212;A0A2R8Y425;A0A2R8Y521;A0A2R8Y5J0;A0A2R8Y8C1;A0A2R8YFD8;A0A2R8YFK9;A0A2U3TZM0;F5GWX5;A0A2R8Y445;A0A2R8YD40;A0A2R8YDJ9;A0A2R8YER1;A0A2R8Y4X2;A0A2R8Y5M9;A0A2R8Y7I0;A0A2R8Y7M9;A0A2R8YDW2;A0A2R8YE38;A0A8V8TR54;K7EMY3 CHD4_HUMAN CHD4 Chromodomain-helicase-DNA-binding protein 4 38729.6 47917.8 36316.6 52125.9 28952.4 29875.7 48148.5 42786.8 37403.1 72674 37827.7 35546.5 51214 +Q14847 Q14847;C9J9W2;F6S2S5 LASP1_HUMAN LASP1 LIM and SH3 domain protein 1 137353 95679.6 165756 173780 308850 415665 227593 341047 408855 542890 230207 314559 471234 364933 239968 295411 211144 232975 238804 307065 +Q14914 F2Z3J9;Q14914 PTGR1_HUMAN PTGR1 Prostaglandin reductase 1 166397 137418 64347.9 97904 163582 60554.5 136516 121933 121880 81279.1 156523 111945 151522 114354 185080 90882.5 131710 57912.1 188479 83861.7 +Q14919 Q14919;C9JCC6;E9PNC7;E9PQX9 NC2A_HUMAN DRAP1 Dr1-associated corepressor 93005.4 85878.3 81568.7 65222.6 100659 63161.3 81745.2 111630 95372.1 97979.7 95679.7 77867 67721.1 87662.4 109241 +Q14974 Q14974;J3KTM9;J3QKQ5 IMB1_HUMAN KPNB1 Importin subunit beta-1 662443 611981 347935 526503 731514 691634 630777 685377 674173 616024 659290 625306 747375 755900 740121 700077 473304 591812 733869 573334 +Q14978 Q14978;A0A0A0MRM9 NOLC1_HUMAN NOLC1 Nucleolar and coiled-body phosphoprotein 1 112657 226237 152205 298059 153672 298378 135091 239500 202001 308057 169224 284345 175708 278343 80120.4 258933 85565.8 283833 100753 214470 +Q14CN4 Q6IME9;P08729;Q14CN4-1;Q3KNV1;Q14CN4 K2C72_HUMAN KRT72 Keratin, type II cytoskeletal 72 2.0426e+06 138481 805356 455344 628381 611548 412879 253576 361858 438539 652026 2.72244e+06 431384 1.86309e+06 841844 515262 294447 +Q14CX7 Q14CX7;H0YHR9 NAA25_HUMAN NAA25 N-alpha-acetyltransferase 25, NatB auxiliary subunit 126578 190926 166366 144513 127368 162744 154339 96546.8 107808 131162 130980 53175.1 133991 210322 146202 145426 +Q15003 Q15003;C9J470;C9JZP1;E9PHA2 CND2_HUMAN NCAPH Condensin complex subunit 2 42578.5 86675.9 53715.6 72777.6 41289.8 77136.3 59421.5 38213.2 29648.3 39138.1 96563.7 9475.18 40767.9 35510 71625.1 47047 +Q15008 Q15008;C9J0E9 PSMD6_HUMAN PSMD6 26S proteasome non-ATPase regulatory subunit 6 182902 161067 177978 141385 179721 159885 174890 115282 144651 153175 148133 123334 98213.4 84572.2 129838 94625 156008 162121 125711 +Q15012 Q15012 LAP4A_HUMAN LAPTM4A Lysosomal-associated transmembrane protein 4A 96792.5 119323 128201 134370 13975 128024 114057 83098.6 23616.3 100116 48699.1 +Q15019 Q15019;B5MCX3;C9J2Q4;H7C2Y0;H7C310 SEPT2_HUMAN SEPTIN2 Septin-2 201320 136616 132770 124500 240796 167884 203773 187123 254199 174759 223158 184622 182199 169098 225939 149266 215341 156666 253388 130093 +Q15020 Q15020;A0A499FI31 SART3_HUMAN SART3 Squamous cell carcinoma antigen recognized by T-cells 3 16312.9 23934.4 34355.6 18617.2 27189.2 25589.8 14580.7 25332.4 38066.9 35603.3 28332.6 +Q15021 Q15021;E7EN77 CND1_HUMAN NCAPD2 Condensin complex subunit 1 101376 145754 84394.7 116743 163013 103050 107947 41054.9 136799 81911.5 101062 128556 156987 83287.3 121871 99417.2 114370 145025 +Q15024 Q15024 EXOS7_HUMAN EXOSC7 Exosome complex component RRP42 17029.1 20183.6 32851.9 24464.4 26871.6 35015.7 15191.6 30006.9 20223.1 34545.2 22441.8 27859 21947.9 +Q15029 Q15029 U5S1_HUMAN EFTUD2 116 kDa U5 small nuclear ribonucleoprotein component 344783 326171 242521 392090 344408 347298 267917 380106 296041 383653 329405 405101 308411 359435 254392 306239 216361 327988 355648 181054 +Q15046 Q15046 SYK_HUMAN KARS1 Lysine--tRNA ligase 704091 510706 647283 609443 650283 495443 563492 516150 631805 510853 620411 547954 646695 503040 448501 496527 540222 411358 605310 515429 +Q15056 Q15056;A0A7I2V4E4;A0A7I2V3D5;A0A7I2V3E4 IF4H_HUMAN EIF4H Eukaryotic translation initiation factor 4H 88248.3 187965 324386 483740 593404 232214 426540 533990 706485 188218 392188 553025 477025 269956 386515 381102 302302 310348 336601 +Q15084 Q15084 PDIA6_HUMAN PDIA6 Protein disulfide-isomerase A6 700097 302081 713705 506986 795021 312794 898573 426658 867013 294192 839418 419691 679309 371390 569776 242369 561402 281320 590352 179742 +Q15102 Q15102;M0R389 PA1B3_HUMAN PAFAH1B3 Platelet-activating factor acetylhydrolase IB subunit alpha1 43784.4 28756.9 41938.6 32962 34898.2 42142.1 24358.9 32567.9 48332.4 35754.6 61458 48917.2 37278.4 32620.3 +Q15113 Q15113 PCOC1_HUMAN PCOLCE Procollagen C-endopeptidase enhancer 1 2.53901e+06 84842.5 3.65911e+06 3.07036e+06 22437.6 4.17136e+06 34595 4.07715e+06 228274 4.31505e+06 3.60865e+06 35488.8 4.06458e+06 3.32279e+06 3.41343e+06 +Q15149 Q15149;A0A8I5KSD5;A0A8I5KUE3;A0A8I5KYZ1;E9PMV1;H0YDN1 PLEC_HUMAN PLEC Plectin 640548 392018 368554 545778 549746 538010 414260 473315 538575 532789 504610 552482 495671 614110 433752 472375 411608 550239 475414 309647 +Q15181 Q15181 IPYR_HUMAN PPA1 Inorganic pyrophosphatase 1.07546e+06 954010 682113 1.12556e+06 1.2904e+06 992680 1.04967e+06 1.17726e+06 1.12644e+06 1.06605e+06 1.13891e+06 1.21139e+06 1.11621e+06 1.00919e+06 850065 920374 2.20519e+06 1.00629e+06 1.22123e+06 823600 +Q15185 Q15185;A0A087WYT3 TEBP_HUMAN PTGES3 Prostaglandin E synthase 3 1.02807e+06 617550 352269 700816 727506 868570 771868 698748 447072 676240 797332 889277 759910 713101 725744 787472 628151 565983 1.04674e+06 671549 +Q15233 Q15233;A0A7I2V464;A0A7I2V4J2;A0A7I2V4K9;A0A7I2V4N2;A0A7I2V535;A0A7I2V5Y7;A0A7P0MRW0;C9IZL7;C9J4X2;C9JYS8 NONO_HUMAN NONO Non-POU domain-containing octamer-binding protein 364971 454988 548192 609153 1.08522e+06 1.26793e+06 743892 1.03955e+06 1.0847e+06 1.19595e+06 699441 1.01346e+06 893032 1.06833e+06 700728 736392 943871 949230 771762 641174 +Q15257 Q15257;A0A6Q8PH23;A0A804CD06;A6PVN5;B4DDQ6;B7ZBP7;B7ZBP9;B7ZBQ0;F6WIT2;H0Y6E5;Q68CR8 PTPA_HUMAN PTPA Serine/threonine-protein phosphatase 2A activator 34898.4 23249.3 40522.6 16726.9 47642.8 20716.4 48696.7 17258.7 52232.7 +Q15262 Q15262;E9PGC5;Q5TG12;H0YDP9 PTPRK_HUMAN PTPRK Receptor-type tyrosine-protein phosphatase kappa 137568 149622 277435 247411 263251 236753 290164 192349 342961 311525 262630 225523 287108 342793 343715 301870 307535 345133 163352 279682 +Q15293 Q15293 RCN1_HUMAN RCN1 Reticulocalbin-1 935181 618627 1.18947e+06 940144 838900 654900 1.19361e+06 667069 773771 823872 1.01666e+06 1.04957e+06 923367 796810 923376 984959 1.37768e+06 915637 903753 1.19804e+06 +Q15323 Q15323;Q9UE12 SWISS-PROT:Q15323 SWISS-PROT:Q15323 2.52167e+07 163031 492567 370509 258967 107683 +Q15365 Q15365 PCBP1_HUMAN PCBP1 Poly(rC)-binding protein 1 351507 436497 139944 526611 342051 704590 287567 910131 291236 632475 411025 603999 427389 676642 503466 583719 218574 492316 545538 467826 +Q15366 P57721;Q15366;E9PFP8;F8VXH9;F8VZX2;F8W0G4;F8W1G6;H3BND9;H3BRU6;J3QT27 PCBP2_HUMAN PCBP2 Poly(rC)-binding protein 2 124382 169893 205345 205452 391028 125209 508244 155870 348286 165799 299812 192879 364633 248086 335310 107940 339800 366514 201819 +Q15369 Q15369;A0A8I5KYG1;A0A8J9AD82;E5RHG8 ELOC_HUMAN ELOC Elongin-C 135752 159057 170837 136465 120228 149091 155308 160083 114696 128126 115539 144121 127138 145639 91912.2 143982 148710 145499 126661 137325 +Q15370 Q15370;A0A0B4J296;A0A8I5KUS5;B8ZZU8;I3L0M9 ELOB_HUMAN ELOB Elongin-B 107364 151528 117240 194826 94507.5 142928 106705 214303 196196 141130 273153 130986 182137 142087 102780 +Q15393 Q15393 SF3B3_HUMAN SF3B3 Splicing factor 3B subunit 3 435955 355759 478895 509873 511055 452160 422316 494168 480583 417767 443003 526610 431121 461904 342789 371498 367434 410487 396544 315569 +Q15424 Q15424 SAFB1_HUMAN SAFB Scaffold attachment factor B1 131985 176062 104000 201499 201923 241410 183011 230471 136708 212475 158730 293004 167917 214408 157483 189865 159028 177832 123970 124555 +Q15428 Q15428 SF3A2_HUMAN SF3A2 Splicing factor 3A subunit 2 59693.5 60878 158439 174130 98890.1 170640 161330 141551 101706 145617 157247 121081 87633.6 66288.7 129610 76732.3 121324 107352 70540.4 +Q15435 Q15435;C9J177;H7C003;H7C3Q5 PP1R7_HUMAN PPP1R7 Protein phosphatase 1 regulatory subunit 7 46025.9 25668.6 81693.6 29942.4 45406.5 35894.7 46808.1 57946.5 83753.7 +Q15436 Q15436;F5H365 SC23A_HUMAN SEC23A Protein transport protein Sec23A 276190 381275 214986 325054 220777 298780 285604 270382 149892 295090 220497 275870 213661 258394 309198 403567 240294 304894 265332 411647 +Q15437 Q15437;A0A2R8YFH5;Q5QPE2 SC23B_HUMAN SEC23B Protein transport protein Sec23B 182089 192710 256888 155945 136383 175585 129236 241425 141672 176871 183885 143369 150982 166344 217769 184311 210804 218239 196857 +Q15459 Q15459 SF3A1_HUMAN SF3A1 Splicing factor 3A subunit 1 228391 188867 263985 255171 308539 344920 339870 365435 353067 323393 315562 361081 369231 366018 272683 283591 314287 327819 344260 173675 +Q15477 Q15477;A0A8V8TL97;A0A8V8TLA2;A0A8V8TLC0;A0A8V8TLR3;A0A8V8TN04;H7C4L3;H7C5N0 SKI2_HUMAN SKIC2 SKI2 subunit of superkiller complex protein 55603.4 18576.1 45093.7 29496.2 54313.3 38040.6 46670.2 61292.5 38942.5 45126.6 26939.1 46260.1 +Q15517 Q15517;G8JLG2;Q2L6G8 CDSN_HUMAN CDSN Corneodesmosin 570306 83235.6 250295 108953 284685 187027 172405 77217.6 62252.4 144716 151553 759571 284891 169134 62844 650782 169418 156079 87641.8 435376 +Q15582 Q15582;H0Y8L3 BGH3_HUMAN TGFBI Transforming growth factor-beta-induced protein ig-h3 106623 491346 991656 210230 1.0279e+06 106526 880112 148437 1.00279e+06 110252 1.40479e+06 131515 1.16047e+06 75001.7 966833 151838 1.10795e+06 54202.7 929516 +Q15631 Q15631;E9PGT1;H7C1D4;A0A0J9YVW2 TSN_HUMAN TSN Translin 332543 184227 216453 180173 255439 145275 285730 97789.1 221828 148875 230773 104717 227140 124625 235588 170427 346354 133589 323126 138787 +Q15637 Q15637;A0A7P0T9U7;C9J792;F8WEV5 SF01_HUMAN SF1 Splicing factor 1 34338.1 41642.7 23939.7 39336.3 78584 17278.1 67324.6 44105.6 13931.9 47685.8 +Q15642 Q15642;M0R0F9;W4VSQ9;M0R2H7 CIP4_HUMAN TRIP10 Cdc42-interacting protein 4 48647.1 36018.6 38997.5 39286.1 47389.9 56650.3 42991 39874.3 44943.3 55101.2 42435 31072.2 30641.6 +Q15645 Q15645 PCH2_HUMAN TRIP13 Pachytene checkpoint protein 2 homolog 112317 41770.8 48788 128738 108708 64500 49871.8 101334 57053.8 68604.1 153854 +Q15691 Q15691 MARE1_HUMAN MAPRE1 Microtubule-associated protein RP/EB family member 1 63341.8 138338 38228.3 122268 92297.5 144523 87742.3 128491 101445 131797 85798.6 116902 71702.6 120869 122027 125964 55984.5 73821 91651.8 107033 +Q15717 Q15717;M0QZR9 ELAV1_HUMAN ELAVL1 ELAV-like protein 1 112013 126957 151324 222753 241667 239834 168486 178495 245709 240510 188133 198530 264482 196615 153490 117203 144986 151978 158618 99513.4 +Q15746 Q15746;A0A8I5KU53;A0A8I5KUP2;A0A8I5QKL9;A0A8I5KTQ1;A0A8I5KUB2 MYLK_HUMAN MYLK Myosin light chain kinase, smooth muscle 55269.3 37204.2 37996.4 45463 34631 +Q15758 Q15758;M0QXM4 AAAT_HUMAN SLC1A5 Neutral amino acid transporter B(0) 126348 52406.3 259348 141882 149997 86720.2 212646 119372 279341 118766 252389 121444 265189 155093 214068 158212 166893 223427 161738 117238 +Q15818 Q15818 NPTX1_HUMAN NPTX1 Neuronal pentraxin-1 7.20893e+06 2.42119e+06 9.9948e+06 4.99256e+06 9.53493e+06 3.44877e+06 1.06352e+07 3.40678e+06 1.14089e+07 3.96236e+06 1.13321e+07 4.76175e+06 1.04082e+07 4.20122e+06 1.01062e+07 4.57122e+06 1.03486e+07 3.99172e+06 6.66047e+06 4.86683e+06 +Q15819 Q13404;Q15819;I3L0A0;D6RG00;E5RIF1;G3V113;G3V2F7;H0YBX6 UB2V2_HUMAN UBE2V2 Ubiquitin-conjugating enzyme E2 variant 2 517303 570947 372565 454386 583053 406842 670147 334223 488577 493088 527090 309111 315961 473742 668347 360211 425359 499204 659102 357630 +Q15843 Q15843;E9PL57;E9PS38;F8VSA6;S4R3E9 NEDD8_HUMAN NEDD8 NEDD8 75704.6 80785.8 107636 37946.6 88914.5 44305.5 63229.7 105312 66266.5 101376 91910.6 33130.6 99027.8 209880 88380.6 111437 +Q15904 Q15904;A0A7I2V3T8;A0A7I2YQA5;A6QRJ1 VAS1_HUMAN ATP6AP1 V-type proton ATPase subunit S1 90920.5 78075.4 55925 133473 134654 73040.7 103230 141830 103098 111051 164667 160234 190298 95907.7 61611.7 121601 38266.1 88344.2 75100 206531 +Q15942 Q15942;H0Y2Y8 ZYX_HUMAN ZYX Zyxin 32211.4 30633.5 65395.7 189886 211127 167039 221730 232069 276067 163960 189688 239998 193966 203152 190216 113209 148476 216640 185051 +Q16181 Q16181;Q6ZU15;A0A024RA87;E7EPK1;E7ES33;G3V1Q4;A0A0U1RRE1;A0A0U1RRH9 SEPT7_HUMAN SEPTIN7 Septin-7 127109 82033.2 58665.6 151658 133786 111987 115014 111020 94365.6 124965 112868 95781.8 133088 122715 110159 127624 98817 141122 +Q16186 Q16186;A0A087WX59 ADRM1_HUMAN ADRM1 Proteasomal ubiquitin receptor ADRM1 46175.6 81025.4 89304.5 128274 159154 106536 162047 137421 120649 134871 110531 205952 119654 146323 108499 70880.4 109505 160705 80072.8 +Q16222 Q16222 UAP1_HUMAN UAP1 UDP-N-acetylhexosamine pyrophosphorylase 526317 759428 575088 661625 674011 748628 706183 684063 743190 781042 689095 638945 556286 615574 513783 646645 702199 668215 549582 552028 +Q16270 Q16270 IBP7_HUMAN IGFBP7 Insulin-like growth factor-binding protein 7 9.05573e+06 8.00909e+06 1.33909e+07 1.39242e+07 9.17117e+06 9.59544e+06 1.27505e+07 9.15562e+06 9.5033e+06 1.04957e+07 1.21872e+07 1.26085e+07 9.67081e+06 1.21563e+07 1.55218e+07 1.47916e+07 1.1545e+07 1.44719e+07 8.55498e+06 1.8129e+07 +Q16394 Q16394;H7C1H6 EXT1_HUMAN EXT1 Exostosin-1 328031 330850 426604 603822 404789 471221 501766 510728 391509 479234 526455 631439 664938 574876 390154 697260 298305 574543 380093 864336 +Q16401 Q16401 PSMD5_HUMAN PSMD5 26S proteasome non-ATPase regulatory subunit 5 103781 129480 82685 72283.4 141209 37690.6 106189 68038 117120 77928.6 144130 33940.6 119487 38052.3 73750.5 53104.8 78942.8 100078 +Q16513 Q16513;B1AL79 PKN2_HUMAN PKN2 Serine/threonine-protein kinase N2 18971.2 28630.5 50497.8 24980.1 18103.4 13634.9 45990.9 30154.4 53690.4 +Q16531 Q16531;A0A7P0T870;A0A7P0T965;A0A7P0TAK7;A0A7P0TB30;A0A7P0Z3Z5;A0A7P0Z4B9;F5GY55;F5GWI0 DDB1_HUMAN DDB1 DNA damage-binding protein 1 1.13463e+06 1.27689e+06 1.2823e+06 1.43583e+06 931286 1.03797e+06 950299 932383 977058 1.20453e+06 965110 1.10651e+06 879438 1.08791e+06 824644 1.21671e+06 1.29099e+06 1.13727e+06 938494 1.17498e+06 +Q16543 Q16543;K7EKQ2;K7EQA9 CDC37_HUMAN CDC37 Hsp90 co-chaperone Cdc37 189467 206461 255609 151768 191682 257177 190235 196308 160202 225197 156458 208761 180475 225395 153849 196257 193613 220863 194331 +Q16555 Q16555;A0A1C7CYX9;E5RFU4 DPYL2_HUMAN DPYSL2 Dihydropyrimidinase-related protein 2 378105 410999 372960 581183 850506 768958 669140 671882 637074 779471 588693 640008 677666 719490 932582 683540 701699 543124 683255 581521 +Q16576 Q16576;E9PC52;Q5JP01 RBBP7_HUMAN RBBP7 Histone-binding protein RBBP7 484546 697474 441267 726187 428577 778472 415805 593936 448119 915692 462830 795273 445348 732982 342276 764810 438032 743061 458650 693366 +Q16595 Q16595;C9JAX1;A0A2R8Y5A1 FRDA_HUMAN FXN Frataxin, mitochondrial 15326.4 21938.5 21171.4 47002.2 30251 22718.9 16774.7 42817.8 +Q16610 Q16610 ECM1_HUMAN ECM1 Extracellular matrix protein 1 116584 108047 194623 40541.5 176436 29804.8 167638 19105.9 208103 44403.2 220685 58078.1 207293 30367.7 235091 11932.5 146628 25656.7 239802 +Q16629 Q16629;C9JAB2;A0A0B4J1Z1 SRSF7_HUMAN SRSF7 Serine/arginine-rich splicing factor 7 1.51009e+06 1.1839e+06 1.65264e+06 1.80863e+06 1.34761e+06 1.37344e+06 1.74555e+06 1.26596e+06 1.27917e+06 1.33176e+06 1.71903e+06 1.47073e+06 1.30519e+06 1.22664e+06 1.21774e+06 1.36194e+06 1.05539e+06 1.03282e+06 1.70371e+06 1.22538e+06 +Q16630 Q16630;F8WJN3 CPSF6_HUMAN CPSF6 Cleavage and polyadenylation specificity factor subunit 6 42258.1 43811.4 71170 132981 196734 238639 253706 248437 287093 272961 187979 220689 183231 254032 206268 181055 179860 247830 239199 157624 +Q16643 Q16643;D6R9W4;D6RFI1 DREB_HUMAN DBN1 Drebrin 70509.4 120001 133102 112129 140163 109604 102123 117658 148892 131690 103610 104639 139573 79608.1 118278 85810.6 95968.5 79529.3 115414 41889.5 +Q16658 Q16658;A0A0A0MSB2;C9JFC0;C9JPH9 FSCN1_HUMAN FSCN1 Fascin 1.92694e+06 1.65655e+06 1.13259e+06 1.71592e+06 1.73017e+06 1.59418e+06 1.37116e+06 1.63241e+06 1.36777e+06 1.85009e+06 1.69017e+06 1.92358e+06 1.42555e+06 1.7066e+06 1.51025e+06 1.77057e+06 1.45022e+06 1.45494e+06 1.54928e+06 1.40214e+06 +Q16706 Q16706 MA2A1_HUMAN MAN2A1 Alpha-mannosidase 2 61792.7 31126.3 113051 78682.4 151447 65781.9 154341 63601.2 150614 48486.6 145099 70248.6 179568 64687.8 163500 68273.7 95360.6 89276.6 52657.9 +Q16719 Q16719 KYNU_HUMAN KYNU Kynureninase 383018 350837 31273.2 385660 256770 389036 330767 140830 354788 459259 355006 24592.2 424127 +Q16763 Q16763;K7EPJ1 UBE2S_HUMAN UBE2S Ubiquitin-conjugating enzyme E2 S 464788 1.54681e+06 1.86811e+06 1.07446e+06 965595 1.7069e+06 2.12539e+06 1.1114e+06 1.15979e+06 1.95615e+06 1.14769e+06 3.59725e+06 748577 2.78228e+06 +Q16775 Q16775;H3BPK3;H3BPQ4;H3BQW8 GLO2_HUMAN HAGH Hydroxyacylglutathione hydrolase, mitochondrial 77216.6 90898.4 61306.6 41866.6 68890.7 46922.1 46387.2 70220.3 59924.5 65761.6 48387 91683.5 63595.8 79439.6 56422.9 46792.7 48514.7 +Q16787 Q16787;A0A0A0MSA0;A0A0A0MTS5;A0A0A6YYF2;A0A3B3ITG1 LAMA3_HUMAN LAMA3 Laminin subunit alpha-3 56350.6 58378.1 56009.9 94786.2 57026.9 91393 73481.7 111935 48567.6 85697.2 85724.7 141142 52774.7 125410 61364.3 136659 24993.7 107113 40672.3 145252 +Q16851 Q16851;A0A087WYS1;A0A7I2YQ74;E7EUC7 UGPA_HUMAN UGP2 UTP--glucose-1-phosphate uridylyltransferase 199346 193240 157268 150669 210486 187924 230021 164039 177007 140030 229792 152516 174290 158289 252557 191606 139121 156746 240835 134551 +Q16881 Q16881;A0A182DWI3;E9PIR7 TRXR1_HUMAN TXNRD1 Thioredoxin reductase 1, cytoplasmic 722034 857560 619705 621555 513739 660183 629436 513928 482327 681969 505286 472918 469670 650646 628715 824397 565109 539116 576662 691654 +Q1RMK2 Q1RMK2 TREMBL:Q1RMK2 TREMBL:Q1RMK2 36248.7 20966.6 59402.5 49549.5 33982.8 47112.1 88282.9 57431.5 46606.6 46484.1 73293.4 58145.6 78729.4 49951.9 71576.1 79558.6 34613.6 64573.3 +Q28085 Q28085 SWISS-PROT:Q28085 SWISS-PROT:Q28085 291253 71177.1 99722.8 124988 139609 117853 130335 144142 168125 132011 149415 140734 196663 68226.3 133040 87399.6 109337 80613.9 138004 +Q29980 Q29980;A0A024RCL3;A0A0G2JH58;A0A0G2JH91;A0A0G2JHB5;A0A0G2JJ55;A0A0G2JK11;A0A140T979;A0A140T986;A0A140T9B8;A0A140T9D8;A0A140T9H4;A0A140T9M1;A0A140T9M6;A0A140T9P3;A0A140T9T9;A0A1W2PPN1;A0A1W2PRA6;F5H7Q8;H0Y615;Q96QC4 MICB_HUMAN MICB MHC class I polypeptide-related sequence B 57262.6 49599.4 84375.6 83995.4 106860 60363.5 80308.2 73175.1 94357.5 98879.3 78745 98565.1 80672 91621.7 79549.1 85354 79492.4 66413.5 +Q29983 Q29983;A0A024RCL3;A0A0G2JK11;A0A140T986;A0A1W2PPN1;A0A1W2PRA6;Q96QC4 MICA_HUMAN MICA MHC class I polypeptide-related sequence A 11772 63413.3 18821.3 24235.8 26845.1 +Q29RF7 Q29RF7 PDS5A_HUMAN PDS5A Sister chromatid cohesion protein PDS5 homolog A 44995.2 39549.4 22496.6 21668.2 43044.9 39281.1 36073.2 31568.8 25951.9 49204.6 31317.3 +Q2HJF0 Q2HJF0 TREMBL:Q2HJF0 TREMBL:Q2HJF0 127491 78084.3 226590 188311 112096 107086 121844 81389.9 106941 106675 142188 95910 146122 49351.7 79483.2 93846.1 132382 121536 128566 114432 +Q2KJF1 Q2KJF1 TREMBL:Q2KJF1 TREMBL:Q2KJF1 131840 211796 162006 224472 176171 209918 179254 139510 265719 235877 156158 182468 267473 241338 174683 160191 267855 224923 89716.5 126293 +Q2NL82 Q2NL82;I3L1Q5 TSR1_HUMAN TSR1 Pre-rRNA-processing protein TSR1 homolog 67250.7 78277.3 55341.2 55920.5 87132.7 51923.9 66593.5 49190.4 57137.4 51705.5 41618.4 48999.3 40328.5 36982.6 76743.5 +Q2TAA2 Q2TAA2;C9J5J2;C9JDY4;C9JE02;H7C5G1 IAH1_HUMAN IAH1 Isoamyl acetate-hydrolyzing esterase 1 homolog 49383.7 43541.8 32639.4 29987.3 67997.9 +Q2TAY7 Q2TAY7 SMU1_HUMAN SMU1 WD40 repeat-containing protein SMU1 62765 79327.3 82723.7 58177.5 118857 111230 189681 77538.3 45636.5 160738 111968 108713 175179 +Q2UVX4 Q2UVX4 SWISS-PROT:Q2UVX4 SWISS-PROT:Q2UVX4 207092 226528 213861 229918 246859 230761 251103 185656 303308 204748 271362 266404 379191 256153 252605 229322 259053 181051 165944 223188 +Q32MZ4 Q32MZ4 LRRF1_HUMAN LRRFIP1 Leucine-rich repeat flightless-interacting protein 1 61591.6 24644.5 47218.1 32815.9 63611.7 62369.2 56727.9 53711.3 31484.2 62160.2 70065.5 52222.1 94733.8 57087.8 77807.8 55250.4 27234 43605.9 88822 43542.6 +Q32P28 Q32P28;H7C2W6 P3H1_HUMAN P3H1 Prolyl 3-hydroxylase 1 166339 41660.3 118251 81905.3 197512 103437 147314 127168 233788 80084.1 183136 169531 203673 123439 114578 93138.3 191909 116185 126440 33835.5 +Q38SD2 Q38SD2;H0YEW1 LRRK1_HUMAN LRRK1 Leucine-rich repeat serine/threonine-protein kinase 1 146006 180108 122575 190103 158769 192909 133995 +Q3KNV1 P19013;P08729;Q3KNV1 TREMBL:Q3KNV1;Q96GE1 TREMBL:Q3KNV1;Q96GE1 95819.1 145803 213788 559606 94148.8 269948 246122 676083 40716.8 326084 343992 588638 100843 273023 270657 514973 52841 240807 +Q3KUS7 Q3KUS7 TREMBL:Q3KUS7 TREMBL:Q3KUS7 87618.5 80973.3 91845.3 102024 119514 88160.2 101930 81788.8 121825 103390 97457.2 114156 106241 112349 82904.8 76666.3 75517.3 83359.3 47897.1 49112.1 +Q3LI61 Q3LI61 KR202_HUMAN KRTAP20-2 Keratin-associated protein 20-2 461313 +Q3LI64 Q3LI64 KRA61_HUMAN KRTAP6-1 Keratin-associated protein 6-1 447608 +Q3LI72 Q3LI72 SWISS-PROT:Q3LI72 SWISS-PROT:Q3LI72 1.84822e+06 +Q3LI77 Q3LI77 KR134_HUMAN KRTAP13-4 Keratin-associated protein 13-4 280342 394539 1.16628e+06 190767 158263 +Q3LI83 Q3LI83 KR241_HUMAN KRTAP24-1 Keratin-associated protein 24-1 7.40608e+06 +Q3LXA3 Q3LXA3;H0YCY6 TKFC_HUMAN TKFC Triokinase/FMN cyclase 57067.8 205676 152640 71949.6 227905 71034.2 237493 87518.7 210950 57845.3 152841 163732 74324.2 169570 256785 35678.1 +Q3MHN5 Q3MHN5;ENSEMBL:ENSBTAP00000018229 SWISS-PROT:Q3MHN5 SWISS-PROT:Q3MHN5 78002.8 309692 50994.5 70494.4 105626 36651.9 24519.1 103059 90621.4 40016 38005.5 290353 76383 53884.8 44969.9 302311 55317.4 31045.4 74788.6 +Q3SX09 Q3SX09 TREMBL:Q3SX09 TREMBL:Q3SX09 547741 381881 408094 576141 592693 547607 459141 390158 784028 649386 532799 617138 719993 702247 449437 445899 451190 491950 357132 456489 +Q3SZ57 Q3SZ57 SWISS-PROT:Q3SZ57 SWISS-PROT:Q3SZ57 4.52487e+06 7.91504e+06 4.05889e+06 4.40246e+06 4.54624e+06 4.78191e+06 3.36965e+06 2.72186e+06 6.4226e+06 4.55074e+06 3.9635e+06 3.90549e+06 9.94659e+06 4.73967e+06 4.75461e+06 4.13346e+06 9.6609e+06 4.59976e+06 2.59924e+06 5.35964e+06 +Q3SZH5 Q3SZH5 TREMBL:Q3SZH5 TREMBL:Q3SZH5 159091 105926 153374 164339 167529 153160 151666 143946 264425 159101 193701 171178 181007 184552 102279 140464 171863 232689 90400.4 126217 +Q3SZR3 Q3SZR3 SWISS-PROT:Q3SZR3 SWISS-PROT:Q3SZR3 261463 623259 211376 142881 249593 85416.6 114055 209374 257624 83003.7 146782 580540 247994 178422 143710 649254 140330 71152.8 132175 +Q3SZV7 Q3SZV7 TREMBL:Q3SZV7 TREMBL:Q3SZV7 59643.9 57779.4 57203.4 69515.3 35282.3 65433.8 61687.9 47583.8 40228.6 23724.5 157315 39078.7 75274.2 25512.7 139930 28147.1 36441.3 90271.4 +Q3T052 Q3T052 TREMBL:Q3T052;Q5EA67 TREMBL:Q3T052;Q5EA67 222701 145352 229924 225961 261987 231828 223500 209554 284276 234521 215535 288119 249051 269920 176797 215674 165427 209000 109317 +Q3Y5Z3 Q3Y5Z3 SWISS-PROT:Q3Y5Z3 SWISS-PROT:Q3Y5Z3 43562.9 103539 73626.4 68091.1 67756.2 70827 97586.5 43228.1 161119 27329.7 48792.9 26887.3 146275 29774.9 +Q3ZBD7 Q3ZBD7 SWISS-PROT:Q3ZBD7 SWISS-PROT:Q3ZBD7 306638 447817 247346 245008 288882 185510 322720 208183 284566 320266 223295 199365 311172 255796 235557 362300 328809 187384 328073 320906 +Q3ZBS7 Q3ZBS7 TREMBL:Q3ZBS7 TREMBL:Q3ZBS7 1.46009e+06 1.17155e+06 1.21505e+06 1.69645e+06 1.07355e+06 1.06276e+06 1.07374e+06 1.02321e+06 1.27104e+06 1.04979e+06 1.33581e+06 1.45515e+06 1.44278e+06 1.18383e+06 1.30857e+06 1.24006e+06 1.12347e+06 1.09663e+06 894214 1.31438e+06 +Q4J6C6 Q4J6C6 PPCEL_HUMAN PREPL Prolyl endopeptidase-like 59679.6 34840.9 52388.5 56175.2 97278.4 50834.3 59710.1 57036.8 30544.2 67077.5 56673 49992.9 +Q4LDE5 Q4LDE5 SVEP1_HUMAN SVEP1 Sushi, von Willebrand factor type A, EGF and pentraxin domain-containing protein 1 470980 356633 573879 824101 760643 746859 706122 924454 994313 789342 867456 1.2891e+06 772853 964380 727659 883614 661338 1.00476e+06 448730 965705 +Q4VC31 Q4VC31;C9JQ41;H7C525 MIX23_HUMAN MIX23 Protein MIX23 33929.3 27465.2 110178 54231.6 48782 19419.9 83014.8 16777 91806 42565.5 46159.2 96587.4 41243.4 76544 +Q504Y2 Q504Y2 PKDCC_HUMAN PKDCC Extracellular tyrosine-protein kinase PKDCC 74079.8 73785.4 49913.1 70765 36997.6 50125.4 49049.6 66261.6 49988.8 105471 36685.7 48035.9 +Q52LG2 Q52LG2 KR132_HUMAN KRTAP13-2 Keratin-associated protein 13-2 1.61541e+06 283819 +Q53EL6 Q53EL6 PDCD4_HUMAN PDCD4 Programmed cell death protein 4 110295 50204.5 62119.6 41932.9 157339 177002 47889.4 117041 37453.3 104194 33427.9 127915 102965 84033.2 105839 73667.9 +Q53FA7 Q53FA7 QORX_HUMAN TP53I3 Quinone oxidoreductase PIG3 81669.1 68157.8 56136.7 39828.7 68001.6 80585.7 35326.8 88564.9 71432 100878 43531 44651.5 54007.8 +Q53GS9 Q53GS9;B9A018;A0A087X1B2 SNUT2_HUMAN USP39 U4/U6.U5 tri-snRNP-associated protein 2 296478 142169 216706 156427 164244 152800 182498 156750 132406 147693 204850 131125 140792 113246 157298 106432 102835 119776 156319 +Q562R1 Q562R1 ACTBL_HUMAN ACTBL2 Beta-actin-like protein 2 218452 228109 328600 275293 274929 289301 483538 317193 286069 246770 453017 660858 387936 239064 315029 376146 1.06174e+06 +Q58D62 Q58D62 SWISS-PROT:Q58D62 SWISS-PROT:Q58D62 413239 637835 400614 525668 485950 531191 422351 353669 612963 502794 479954 467102 921081 614431 427936 423554 650639 461006 278908 560242 +Q58FF6 Q58FF6 H90B4_HUMAN HSP90AB4P Putative heat shock protein HSP 90-beta 4 279992 60853.5 67930.5 65487.6 83909.3 247788 94456 158263 +Q5D862 Q5D862 SWISS-PROT:Q5D862 SWISS-PROT:Q5D862 294562 59389.6 135384 52326.5 245609 145710 149297 107134 63850.5 72142 201340 285823 245294 127556 59626.8 343747 81953 72665.5 75811.5 608809 +Q5JSH3 Q5JSH3;H7BY83 WDR44_HUMAN WDR44 WD repeat-containing protein 44 165003 75754.5 71584.5 34498.6 50491.8 52606.2 69181.2 41816.4 34586.7 43985.6 76968.5 59023.4 51753.4 56070.2 53348.9 45799.8 113046 +Q5JTH9 Q5JTH9;A0A087X0G0 RRP12_HUMAN RRP12 RRP12-like protein 38058.5 87942.7 91462.9 41276 139060 26432.1 138924 120896 115026 123861 86857.5 79849.6 33577.8 92419.5 +Q5JW98 Q5JW98 CAHM4_HUMAN CALHM4 Calcium homeostasis modulator protein 4 302857 +Q5T3I0 Q5T3I0 GPTC4_HUMAN GPATCH4 G patch domain-containing protein 4 242390 46756.8 241711 224516 241533 216986 176571 202974 173094 +Q5T4S7 Q5T4S7 UBR4_HUMAN UBR4 E3 ubiquitin-protein ligase UBR4 42156 41535.3 34004.5 73298.9 53302.6 55509.5 31304.4 38899.1 30932.8 61507.2 37471.5 46046.6 59916.1 +Q5T749 Q5T749 KPRP_HUMAN KPRP Keratinocyte proline-rich protein 78339.6 93794.7 69617 41343.2 28587.6 35512.3 84597.8 98049.1 55169.5 81544 74125.8 23763.2 548812 +Q5T750 Q5T750 XP32_HUMAN XP32 Skin-specific protein 32 548614 341728 234745 106649 161703 321738 139222 446165 164538 37308.4 262293 85753.8 868856 +Q5TFE4 Q5TFE4 NT5D1_HUMAN NT5DC1 5'-nucleotidase domain-containing protein 1 63454.6 148459 81473.2 80375.2 93861.8 83532.4 58559.8 71569.2 116301 46467.6 63637.4 96103.6 110515 74888.1 145359 81500.6 80411.9 54568.1 68242.9 +Q5VSG8 Q5VSG8;E9PMU5 MANEL_HUMAN MANEAL Glycoprotein endo-alpha-1,2-mannosidase-like protein 30935.6 56703.1 90379.4 43800.9 77728.1 44038.3 94254.5 35255.3 102165 34930 84384 48980.1 131431 54510.5 106028 84904.7 59144.1 136983 +Q5VTL8 Q5VTL8;A0A0A0MRN0 PR38B_HUMAN PRPF38B Pre-mRNA-splicing factor 38B 48836.7 60080.6 41569.1 67466.9 38712.4 58510.8 58100.7 65506.8 +Q5VTR2 Q5VTR2;C9J0A5;C9JWJ4;C9JXC9 BRE1A_HUMAN RNF20 E3 ubiquitin-protein ligase BRE1A 36491.3 45682.1 15587.1 31703.3 39070 13205 30692.6 34003.3 +Q5VW32 Q5VW32;F5GXQ0;Q5VW33 BROX_HUMAN BROX BRO1 domain-containing protein BROX 46466.3 65772.9 33460.7 30328.5 29816.8 21284.3 72346.4 30538.9 37948.2 30926.7 26505 37511.4 68512.7 18409.1 36389.7 +Q5VYK3 Q5VYK3;J3KN16;A0A804HJA4 ECM29_HUMAN ECPAS Proteasome adapter and scaffold protein ECM29 60006.8 81419.7 55192.3 70573.1 85292.1 53826.3 89228.1 51284.7 60251.7 43174.5 62406.8 44968.2 76302.1 48015.4 59942.6 26240.2 52430 79746.4 +Q641Q3 Q641Q3 METRL_HUMAN METRNL Meteorin-like protein 42606.2 40172.8 82641.4 102291 97962.2 71587.1 109778 78411 62678.7 188727 69262.6 130648 53241.7 76671 97697.6 46592.7 +Q674R7 Q674R7 ATG9B_HUMAN ATG9B Autophagy-related protein 9B 123068 +Q68BL7 Q68BL7;Q5JTM7 OLM2A_HUMAN OLFML2A Olfactomedin-like protein 2A 214492 220815 154868 516354 183411 396885 141305 638584 167413 422946 233974 631642 222961 414938 160059 429681 226819 243336 143605 +Q68CQ4 Q68CQ4 UTP25_HUMAN UTP25 U3 small nucleolar RNA-associated protein 25 homolog 114961 90393.6 80269.3 102822 123198 136333 115867 132858 160581 130961 126625 128520 149645 129992 117673 108130 51676.9 161638 150588 +Q6A162 Q6IFU5;Q6A162;G3V1C2 K1C40_HUMAN KRT40 Keratin, type I cytoskeletal 40 609982 23766 +Q6A163 Q6IFU6;Q6A163 K1C39_HUMAN KRT39 Keratin, type I cytoskeletal 39 1.05024e+06 +Q6DKJ4 Q6DKJ4;A0A0G2JQK5 NXN_HUMAN NXN Nucleoredoxin 53099.1 39685 11300.3 33821.8 6140.42 26190.2 37578.3 +Q6EMK4 Q6EMK4 VASN_HUMAN VASN Vasorin 159890 458282 225422 801301 157961 613630 223405 547944 205107 809280 230438 728121 189243 687502 286872 959891 216507 960185 151503 928689 +Q6FI81 Q6FI81;H3BPG7;H3BQ23;H3BT65;H3BV90;H3BUG4 CPIN1_HUMAN CIAPIN1 Anamorsin 90323.1 136535 50975.4 133134 135807 152604 94987.8 153890 93613 200569 89195.8 132244 161334 153173 120294 141530 99762.2 103436 163874 +Q6IA86 Q6IA86 ELP2_HUMAN ELP2 Elongator complex protein 2 42782.3 25333.4 23955.3 21814.3 31914.2 19224.8 35291.4 +Q6IN85 Q5MIZ7;Q6IN85;G3V231;G3V4R3;G3V5A2;G3V5Z3;H0YIY8 P4R3A_HUMAN PPP4R3A Serine/threonine-protein phosphatase 4 regulatory subunit 3A 23410.4 20312.7 32685.1 21055.8 34906.5 36086.5 +Q6ISB0 Q9NSB2;Q6ISB0 TREMBL:Q6ISB0 TREMBL:Q6ISB0 236439 133493 737909 268630 114544 274180 619231 62088.6 550720 2.29538e+06 127687 305162 77229.9 167987 253448 +Q6KB66 Q6KB66-1;Q6KB66 K2C80_HUMAN KRT80 Keratin, type II cytoskeletal 80 315748 100807 77091.6 63799.5 48624.5 21446.7 62123.2 103689 312400 104994 125913 212952 58956.6 74770.5 166364 +Q6MZM0 Q6MZM0 HPHL1_HUMAN HEPHL1 Ferroxidase HEPHL1 305438 +Q6NT21 Q6NT21 TREMBL:Q6NT21 TREMBL:Q6NT21 279183 +Q6NYC8 Q6NYC8;A0A0G2JHC2 PPR18_HUMAN PPP1R18 Phostensin 15339.3 23186.3 48969.5 46013.4 41783.3 36797.6 +Q6P2Q9 Q6P2Q9;I3L0J9 PRP8_HUMAN PRPF8 Pre-mRNA-processing-splicing factor 8 149219 161415 152828 184260 154760 175568 140009 203384 95634.6 144844 169010 190386 129711 149444 92429.4 135964 133160 188049 79538.2 +Q6P996 Q6P996;H3BND4;Q86XE2;Q6XYB5 PDXD1_HUMAN PDXDC1 Pyridoxal-dependent decarboxylase domain-containing protein 1 37213.6 21304.1 41522.3 13348.8 +Q6PD62 Q6PD62 CTR9_HUMAN CTR9 RNA polymerase-associated protein CTR9 homolog 60573.8 83409.6 88095.9 71917.7 103072 56625.6 107264 66062 99594.1 81518.9 67632.3 87821 161316 70277.7 53536 +Q6PGP7 Q6PGP7;A0A8V8TLS3;A0A8V8TLT2;A0A8V8TMA8;A0A8V8TMB2;A0A8V8TNJ4 SKI3_HUMAN SKIC3 SKI3 subunit of superkiller complex protein 31054.2 18279.3 14023.4 +Q6PKG0 Q6PKG0;A0A0B4J210;A0A8I5KSP1;A0A8I5KWU3;E5RH50;H0YC33 LARP1_HUMAN LARP1 La-related protein 1 137992 143061 192939 111407 183955 88876.1 139324 147360 200666 116641 163887 124883 166614 77975.2 128113 158108 190075 185443 99334.5 +Q6UVK1 Q6UVK1 CSPG4_HUMAN CSPG4 Chondroitin sulfate proteoglycan 4 71112.3 204578 117269 334542 95900.8 232618 98304.7 222867 89801.6 275444 111285 267084 105080 267864 106578 320955 101219 309383 79867.4 267381 +Q6UXI7 Q6UXI7;B5MD45 VITRN_HUMAN VIT Vitrin 79839 114625 130097 140992 140301 178744 153781 102033 96018.5 28527.8 80310.9 +Q6XQN6 Q6XQN6 PNCB_HUMAN NAPRT Nicotinate phosphoribosyltransferase 72675.9 161096 102641 143374 120223 205125 96727 174810 133221 181616 104354 153234 120914 217690 133132 178248 95057.9 218037 91173.5 135561 +Q6YHK3 Q6YHK3 CD109_HUMAN CD109 CD109 antigen 652985 535757 565739 1.02974e+06 678225 784024 761785 811173 720257 833455 758720 1.18124e+06 671451 926002 492342 1.21919e+06 780069 997010 517438 1.25712e+06 +Q6YP21 Q6YP21;A0A0A0MRN7 KAT3_HUMAN KYAT3 Kynurenine--oxoglutarate transaminase 3 122593 154178 98633.1 92823.8 148481 127960 140355 97438.8 149701 141208 133882 92150 148287 106587 135230 168294 138963 117835 114046 127053 +Q6ZMW3 Q6ZMW3 EMAL6_HUMAN EML6 Echinoderm microtubule-associated protein-like 6 125034 81129.2 41807.1 194801 140609 271168 192838 232017 188085 +Q6ZTR5 Q6ZTR5 CFA47_HUMAN CFAP47 Cilia- and flagella-associated protein 47 174765 365152 153616 311607 473771 610503 378247 475135 434250 402911 404588 373121 398678 451756 267620 672403 154516 384496 472292 285902 +Q712K3 P49427;Q712K3;U3KPV8;U3KQ77 UB2R2_HUMAN UBE2R2 Ubiquitin-conjugating enzyme E2 R2 77536.5 51722.4 61951.4 40257 72370.9 26875.3 41339 53075.5 87745.1 33312.7 72698.4 79559.6 47287.9 45751.7 93293.5 +Q71RC2 Q71RC2;Q96J85 LARP4_HUMAN LARP4 La-related protein 4 71603.9 88432 105126 103505 134145 64384.9 126637 68942.5 128993 73573.7 94157.7 76268.8 115137 115982 112877 62384.7 177420 114719 +Q7KZ85 Q7KZ85 SPT6H_HUMAN SUPT6H Transcription elongation factor SPT6 59479.3 69505.1 60176.1 71681.8 97516.9 118446 87937.2 68719.9 79896.9 101814 67116.4 83209.3 96932.1 113204 67454.4 41741.5 69344.5 97114.1 68599.9 +Q7KZF4 Q7KZF4 SND1_HUMAN SND1 Staphylococcal nuclease domain-containing protein 1 725499 685977 643842 735101 847159 735886 657921 755656 851719 700030 782573 748119 790976 691452 696233 597335 696842 595269 815617 459421 +Q7L014 Q7L014;A0A0C4DG89;D6RJA6 DDX46_HUMAN DDX46 Probable ATP-dependent RNA helicase DDX46 81950.6 61822.4 51111.3 88188.7 101042 111509 87017.5 152642 85062.4 74893.2 112687 132685 105671 116669 85364 127859 59253.6 104900 135683 80307.9 +Q7L1Q6 Q7L1Q6;H0Y503 5MP2_HUMAN BZW1 eIF5-mimic protein 2 72107.2 76411.9 81514 92630.6 90039.5 68910.6 102070 81561.9 51992.5 49047.9 59651.8 75636.6 63788.7 65130.7 70693.7 47597.6 199230 +Q7L2H7 Q7L2H7;E9PN86;J3KNJ2 EIF3M_HUMAN EIF3M Eukaryotic translation initiation factor 3 subunit M 267321 237303 169549 210599 201256 281931 199930 238086 138404 164276 218377 226048 208814 179282 245847 198741 171424 159528 278401 296630 +Q7L576 Q7L576;A0A087WVZ5 CYFP1_HUMAN CYFIP1 Cytoplasmic FMR1-interacting protein 1 41896.1 60768.2 62235.5 88827.7 53349 74497.1 64675.2 61449.9 46734.9 46110.8 63188.2 78370.6 71258.6 73974 +Q7L5N1 Q7L5N1;E7EM64 CSN6_HUMAN COPS6 COP9 signalosome complex subunit 6 126707 80046.1 51679.1 75547 123564 111029 81069.8 64223.8 44086.6 68659.4 77768.3 102377 70566.6 77683 66778 70297.9 65697.3 106826 +Q7L5Y1 Q7L5Y1;A0A3F2YNX7;R4GNF8 ENOF1_HUMAN ENOSF1 Mitochondrial enolase superfamily member 1 86460.4 28705 24649.8 55744.8 20304.2 55455.4 72999.4 36346.8 66244.4 +Q7LGC8 Q7LGC8 CHST3_HUMAN CHST3 Carbohydrate sulfotransferase 3 24477.6 13530.9 26603 +Q7RTV0 Q7RTV0 PHF5A_HUMAN PHF5A PHD finger-like domain-containing protein 5A 385907 372032 365598 424931 447154 518136 492882 467650 395814 +Q7Z2W4 Q7Z2W4;C9J6P4;A0A7P0T8C6;H7C5K1 ZCCHV_HUMAN ZC3HAV1 Zinc finger CCCH-type antiviral protein 1 36827.9 54561.2 86154.9 45752.1 65877.5 44257.6 70506.5 86823 74359.5 45178 71096.2 80119.9 64816.7 57980 54426.3 62355 36467.2 +Q7Z304 Q7Z304;A0A0G2JQ91 MAMC2_HUMAN MAMDC2 MAM domain-containing protein 2 3.30974e+06 565752 2.90609e+06 1.47447e+06 3.40896e+06 1.12342e+06 3.38611e+06 1.35633e+06 3.87819e+06 1.10754e+06 3.91213e+06 2.04101e+06 3.21209e+06 1.42502e+06 2.68129e+06 1.42101e+06 2.94484e+06 1.4912e+06 2.25016e+06 1.65949e+06 +Q7Z4V5 Q7Z4V5;A0A087WX58;K7EPS6 HDGR2_HUMAN HDGFL2 Hepatoma-derived growth factor-related protein 2 36029.7 29312.4 49287.7 8611.18 20153.6 24734.1 43143.2 10398.5 11461.6 21917.8 42091.4 11143.2 24065 25854.3 +Q7Z6Z7 Q7Z6Z7 HUWE1_HUMAN HUWE1 E3 ubiquitin-protein ligase HUWE1 158868 330463 229242 159753 374749 142139 350651 118890 312330 106776 257742 120262 316195 179208 221864 86461.2 276690 170778 132317 +Q7Z794 Q7Z794 SWISS-PROT:Q7Z794 SWISS-PROT:Q7Z794 158548 63734.4 172618 40313.8 120943 76599.8 290702 415694 252629 102442 336759 124401 242461 +Q7Z7H3 Q7Z7H3 CATIP_HUMAN CATIP Ciliogenesis-associated TTC17-interacting protein 27458.2 27007.5 17423.2 12038.9 23824.2 14060.4 20935.8 21986.4 19265.9 +Q86SF2 Q86SF2;H0YAH3 GALT7_HUMAN GALNT7 N-acetylgalactosaminyltransferase 7 443539 136277 512512 219886 514518 168159 642501 199856 484654 246085 540593 236351 466986 170694 315520 251010 703282 130022 370286 249058 +Q86SG7 Q86SG7;C9J4J0;C9JBA4 LYG2_HUMAN LYG2 Lysozyme g-like protein 2 318554 +Q86SJ6 Q86SJ6 DSG4_HUMAN DSG4 Desmoglein-4 845998 144293 62558.3 79468.2 86988.8 48441 112586 195689 +Q86SQ4 Q86SQ4;F5H2L1 AGRG6_HUMAN ADGRG6 Adhesion G-protein coupled receptor G6 191872 118348 294461 221870 320077 210092 412994 233336 361481 272204 369044 276755 367061 299807 370305 271453 291343 290311 210930 291905 +Q86SR1 Q86SR1 GLT10_HUMAN GALNT10 Polypeptide N-acetylgalactosaminyltransferase 10 113309 94864.6 120891 62180.5 135401 62966.1 143507 74602.5 146013 144914 79008.6 129269 50780.1 116372 +Q86TI2 Q86TI2;A0A2R8YF65;A0A8V8TR99;M0R2A8 DPP9_HUMAN DPP9 Dipeptidyl peptidase 9 50782.6 22183.7 7403.08 24184.4 41863.5 28412.1 31841 17218.6 35401.6 19633.5 25941.4 21840.5 22550.3 34403.4 15459.4 +Q86UD1 Q86UD1;E9PJ29 OAF_HUMAN OAF Out at first protein homolog 415179 296437 592171 589539 628692 548463 642382 569556 704940 537378 680978 613490 622180 595001 688132 489164 425947 513441 383193 449457 +Q86UE4 Q86UE4;E5RJU9;H0YB56 LYRIC_HUMAN MTDH Protein LYRIC 30960.7 69298.2 52560.1 101643 102861 63684.1 61995.9 +Q86UN3 Q86UN3 R4RL2_HUMAN RTN4RL2 Reticulon-4 receptor-like 2 597531 570081 645360 868192 730474 835173 740959 607628 949025 1.22346e+06 646281 665675 952885 1.16062e+06 780521 860673 922857 1.27219e+06 501825 820946 +Q86UP2 Q86UP2;B7Z6P3;G3V4Y7;G3V5P0 KTN1_HUMAN KTN1 Kinectin 125074 193119 173720 227540 160557 236424 142942 207361 142311 223230 152570 223754 131435 222463 163881 204976 137168 180572 116760 120410 +Q86V81 Q86V81;E9PB61 THOC4_HUMAN ALYREF THO complex subunit 4 141881 53505.9 52961.9 80613.8 116416 112501 116590 117954 61575 77248.9 88177.4 82016.2 50582.1 154155 195197 65862.8 78476.3 149065 120783 90695.8 +Q86VM9 Q86VM9;E7ERS3 ZCH18_HUMAN ZC3H18 Zinc finger CCCH domain-containing protein 18 9730.75 15995.8 16068.3 33685.8 26502.3 24525.6 17039.8 +Q86VP6 Q86VP6;A0A0C4DGH5 CAND1_HUMAN CAND1 Cullin-associated NEDD8-dissociated protein 1 456395 458353 257144 372977 420523 473612 344828 460502 345640 387584 421264 428388 361420 446145 350865 393228 295210 417791 462399 277380 +Q86WJ1 Q86WJ1;A0A0A0MRH8;A0A0A0MSH9;A0A494C179;A0A494C1P7;A0A494C1F7 CHD1L_HUMAN CHD1L Chromodomain-helicase-DNA-binding protein 1-like 126730 53874.1 123126 195995 158941 140774 68622.7 174376 200529 +Q86X29 Q86X29;S4R3V8 LSR_HUMAN LSR Lipolysis-stimulated lipoprotein receptor 108890 261999 214454 237072 298809 390482 302319 278784 242437 92556 359137 +Q86XP3 Q86XP3 DDX42_HUMAN DDX42 ATP-dependent RNA helicase DDX42 118983 245755 287708 180313 261747 295335 180680 243354 284429 160162 146610 194804 263615 +Q86YZ3 Q86YZ3 SWISS-PROT:Q86YZ3 SWISS-PROT:Q86YZ3 108272 23229.8 27659.3 17458 7084.19 25907.5 165061 44482.3 32963 131928 10414.6 +Q8IUC0 Q8IUC0 KR131_HUMAN KRTAP13-1 Keratin-associated protein 13-1 1.1725e+06 152416 +Q8IUC1 Q8IUC1 KR111_HUMAN KRTAP11-1 Keratin-associated protein 11-1 5.09364e+06 +Q8IUC2 Q8IUC2 KRA81_HUMAN KRTAP8-1 Keratin-associated protein 8-1 1.8357e+06 +Q8IUC3 Q8IUC3 KRA71_HUMAN KRTAP7-1 Keratin-associated protein 7-1 415800 +Q8IUG1 Q8IUG1 KRA13_HUMAN KRTAP1-3 Keratin-associated protein 1-3 261805 +Q8IV08 Q8IV08;A0A8V8TPY8;A0A8V8TQH6;A0A8V8TRD4 PLD3_HUMAN PLD3 5'-3' exonuclease PLD3 534104 140734 235093 101971 263878 149408 289110 153535 323391 156720 244729 167457 352377 183631 253434 138681 229220 138268 206937 153557 +Q8IVM0 Q8IVM0 CCD50_HUMAN CCDC50 Coiled-coil domain-containing protein 50 38278.4 26831.4 26600.7 48355.7 42582.4 36666.4 57823.5 69519.4 43085.8 34361.8 38972.1 34177.8 79464.8 46631 54746.7 +Q8IVT2 Q8IVT2 MISP_HUMAN MISP Mitotic interactor and substrate of PLK1 15497.2 32848.2 71978.5 48944.9 29342.9 60649.5 47014.6 37260.2 73060.1 15375.2 33888.7 50575.8 +Q8IX12 Q8IX12;A0A0C4DGG8 CCAR1_HUMAN CCAR1 Cell division cycle and apoptosis regulator protein 1 21842.4 75430.9 17986.2 45675.1 58646.3 39809.1 53957.5 87173.3 23161.8 47396.1 61723.7 +Q8IYB3 Q8IYB3;A9Z1X7;M0R088 SRRM1_HUMAN SRRM1 Serine/arginine repetitive matrix protein 1 91813.4 54875 93058.8 84071.8 56297.7 96853.2 53374.1 39836.5 59679.8 82922.3 83060.3 +Q8IYD8 Q8IYD8;H0YJ14;H0YJS3;A0A8Q3SIT9;A0A8Q3SIU7;A0A8Q3SIV0;A0A8Q3SIX9;A0A8Q3SIZ4;A0A8Q3SJ05;A0A8Q3WLE8;A0A8Q3WLG3;A0A8Q3WLH2;A0A8Q3WLY4;A0A8Q3WMK3 FANCM_HUMAN FANCM Fanconi anemia group M protein 95488.8 87112 69765.2 67229.4 64880.1 149953 52813.1 75592.4 41992.1 129192 86174.5 122970 +Q8IYT4 Q8IYT4;A0A8I5QL41;K7EIJ8;A0A8I5KR90;K7EM02;K7EPQ6 KATL2_HUMAN KATNAL2 Katanin p60 ATPase-containing subunit A-like 2 1.95169e+06 1.99842e+06 1.94208e+06 1.626e+06 2.03337e+06 1.50247e+06 +Q8IZ52 Q8IZ52;A0A8I5QJC8 CHSS2_HUMAN CHPF Chondroitin sulfate synthase 2 566111 380951 457295 416849 +Q8IZ73 Q8IZ73;A0A087WXE2 RUSD2_HUMAN RPUSD2 Pseudouridylate synthase RPUSD2 59621.2 30599.9 39542.3 42115.4 40459.8 60939.4 52118 41222.1 56094.8 31787.5 +Q8IZ83 Q8IZ83;F5H4B6 A16A1_HUMAN ALDH16A1 Aldehyde dehydrogenase family 16 member A1 57027.3 47695.9 54287.8 75965.3 65365.6 52478.6 53417.6 72428.4 69071.1 56222.1 45509.1 77941.2 45955.6 53878.8 26729.3 62947.9 43694.7 49342.3 +Q8IZL8 Q8IZL8;C9JFV4;E7EV54 PELP1_HUMAN PELP1 Proline-, glutamic acid- and leucine-rich protein 1 13977.8 15412.5 18343.2 10383.3 +Q8N0V5 Q8N0V5 GNT2A_HUMAN GCNT2 N-acetyllactosaminide beta-1,6-N-acetylglucosaminyl-transferase 27019.2 29009.6 46388.3 27404.7 67240.1 49874.6 83745.5 49278 54340.1 45985.4 55296.3 68013.6 60100.5 93300.3 72973.1 38472.2 32523.4 +Q8N163 Q8N163;G3V119;H0YB24 CCAR2_HUMAN CCAR2 Cell cycle and apoptosis regulator protein 2 28224.1 23619 46338.8 54852.1 75190.5 69628.8 124150 43716.8 58634.3 78915.4 71126.5 46545.6 70720.2 75955.9 40597.9 64642.4 +Q8N1F7 Q8N1F7;H3BRD9;H3BV15;H3BVG0 NUP93_HUMAN NUP93 Nuclear pore complex protein Nup93 78659.9 49759.6 62051.1 52479.8 75486.4 62522.8 41489.3 64903.9 74433.6 80399.5 84918.3 38955.4 51672.8 76451.3 +Q8N1N4 Q7RTT2;Q8N1N4-2;Q8N1N4 K2C78_HUMAN KRT78 Keratin, type II cytoskeletal 78 315870 105336 70601.6 131445 127932 126368 102506 89443.5 84221.3 111505 359269 127552 130870 332034 101172 72284.2 343642 +Q8N257 P06899;P23527;P33778;Q16778;Q6DN03;Q6DRA6;Q8N257 H2B3B_HUMAN H2BC26 Histone H2B type 3-B 586820 27559.1 58002.8 103101 92147.6 60377.9 58116.8 99459.3 63215.6 89438.6 116974 58756.2 89843.4 62856.7 73787.7 77197.5 73262.9 +Q8N2S1 Q8N2S1;A0A0C4DH07 LTBP4_HUMAN LTBP4 Latent-transforming growth factor beta-binding protein 4 47889.7 29728.3 76151 69862.4 70595.3 63448 73379.9 94628 78299.9 86969.7 113431 74251.3 79312 76002.1 74933.5 47067.8 60881.7 50741 75260 +Q8N684 Q8N684;C9J286;C9J323;C9JM38;F5H047;F5H669;F5H6M0;J3QT54 CPSF7_HUMAN CPSF7 Cleavage and polyadenylation specificity factor subunit 7 70254.8 65251.1 76233.5 118697 76162.4 140482 46321.9 132070 55708.8 83754.2 71572.7 110810 63948.8 91433.4 38532.7 103330 101260 +Q8N6N3 Q8N6N3 CA052_HUMAN C1orf52 UPF0690 protein C1orf52 80359.5 45945.1 62207.9 34513.2 58940.9 38896.8 72935 83947.6 +Q8N7H5 Q8N7H5;B4DGJ5 PAF1_HUMAN PAF1 RNA polymerase II-associated factor 1 homolog 67823.5 94897.3 81447.8 96449.8 105047 199725 137463 158428 104340 154177 113004 99823 73776 106341 48658.7 124527 79750.4 90003.8 +Q8NBI5 Q8NBI5;E9PS74;H0YD64 S43A3_HUMAN SLC43A3 Equilibrative nucleobase transporter 1 31179.8 36722.9 18458.5 11384.2 20289.8 27512.3 33951.1 12831.4 28969 +Q8NBJ4 Q8NBJ4 GOLM1_HUMAN GOLM1 Golgi membrane protein 1 564495 573026 753339 1.16496e+06 656076 1.05502e+06 758194 1.03671e+06 689357 1.37129e+06 768412 1.55519e+06 864408 1.26981e+06 988938 1.40039e+06 1.03752e+06 1.08471e+06 533060 1.397e+06 +Q8NBJ5 Q8NBJ5;A0A8I5QKL8;A0A8I5QL08;M0QX72 GT251_HUMAN COLGALT1 Procollagen galactosyltransferase 1 80679.1 56304.2 151444 70527.3 98585.1 43458.8 124473 77424.9 140485 71008 103484 94754.3 85416.9 75102.9 121752 89226.3 57683.2 47518.8 61104.8 +Q8NBJ7 Q8NBJ7;A0A494C1B1;A8MXB9;E9PBT8;J3KQJ1;J3QT17 SUMF2_HUMAN SUMF2 Inactive C-alpha-formylglycine-generating enzyme 2 72086.7 81350.3 136651 136309 107405 75650.4 133061 93423.1 87681.8 94919.8 111280 75019.3 88819.3 83098.7 85018.8 96130.8 88968.4 98952.3 64248.1 81439.9 +Q8NBP7 Q8NBP7;A0A669KBG0 PCSK9_HUMAN PCSK9 Proprotein convertase subtilisin/kexin type 9 3.85073e+06 2.48609e+06 3.03857e+06 4.60986e+06 4.33522e+06 3.7866e+06 4.29063e+06 3.84124e+06 5.09144e+06 3.54744e+06 4.4706e+06 5.4937e+06 4.08081e+06 4.18058e+06 4.5128e+06 3.66895e+06 3.82071e+06 3.03148e+06 3.11592e+06 4.47611e+06 +Q8NBS9 Q8NBS9 TXND5_HUMAN TXNDC5 Thioredoxin domain-containing protein 5 270963 209424 532920 379591 256576 192367 423638 224679 237291 191073 392757 277597 230461 205276 425029 354707 216464 169248 326437 279418 +Q8NBZ7 Q8NBZ7;C9J3T9;C9JCB7;C9JFU6;C9JW33 UXS1_HUMAN UXS1 UDP-glucuronic acid decarboxylase 1 128772 64330.6 143965 186840 159897 157581 234306 223522 199408 185511 225760 161933 176773 114913 149220 94154.9 201941 149835 119826 +Q8NC51 Q8NC51 PAIRB_HUMAN SERBP1 Plasminogen activator inhibitor 1 RNA-binding protein 238911 294622 230586 279532 398643 355449 304274 323874 373668 357492 212389 420489 321936 347689 468925 335841 348339 283302 441516 270247 +Q8NCC3 Q8NCC3;H3BMU8 PAG15_HUMAN PLA2G15 Phospholipase A2 group XV 218909 169602 305688 293268 305534 287103 393478 217223 323270 412072 370096 318154 354980 418374 401328 370720 322095 432939 242712 495723 +Q8NCH0 Q8NCH0 CHSTE_HUMAN CHST14 Carbohydrate sulfotransferase 14 69514.9 57774.2 159656 64545.7 95841.1 62669.9 176503 81095.1 100679 62390.5 183644 74761.7 115550 58313.5 105312 56033.4 74619.3 +Q8NCL4 Q8NCL4 GALT6_HUMAN GALNT6 Polypeptide N-acetylgalactosaminyltransferase 6 199855 102940 345272 210032 341614 239283 344198 225679 328920 246211 288236 285510 416679 256316 316351 194485 273699 197642 192266 246760 +Q8NCW5 Q8NCW5;A0A7P0Z426;A0A7P0T9Y2;A0A7P0TAG8;Q5T3I3;Q5T3I4 NNRE_HUMAN NAXE NAD(P)H-hydrate epimerase 791576 943272 974904 753506 748475 780009 939403 506206 731144 840040 672608 498466 643213 755094 972213 817908 978566 774525 737639 711592 +Q8NDH3 Q8NDH3 PEPL1_HUMAN NPEPL1 Probable aminopeptidase NPEPL1 67473.7 99869 96287.1 74194.3 59821.1 75787.1 50372.9 44864.9 49393 109183 58148 71785.5 63435.4 65301.3 67225.5 101750 154499 74412.5 56867.1 161189 +Q8NE71 Q8NE71;H0YGW7;Q5STZ8 ABCF1_HUMAN ABCF1 ATP-binding cassette sub-family F member 1 34427.2 39424 35231.4 33071.6 32380.2 41547.8 34661.3 17761.2 36091.7 61950.7 +Q8NES3 Q8NES3 LFNG_HUMAN LFNG Beta-1,3-N-acetylglucosaminyltransferase lunatic fringe 380382 292561 225760 627857 499429 615510 363372 497752 618997 644759 529368 736541 425349 798309 197419 528186 423440 753092 330086 380403 +Q8NHM5 Q8NHM5;A0A0C4DGG3 KDM2B_HUMAN KDM2B Lysine-specific demethylase 2B 65612.7 62051.1 87002.9 88692.4 102974 72459.7 73567.6 19510.8 +Q8NHP8 Q8NHP8 PLBL2_HUMAN PLBD2 Putative phospholipase B-like 2 1.29468e+06 694454 1.40638e+06 1.14461e+06 1.57544e+06 905036 1.62279e+06 958354 1.90468e+06 920094 1.82445e+06 1.14626e+06 1.71484e+06 1.15358e+06 1.80261e+06 1.06553e+06 1.78717e+06 1.177e+06 1.03151e+06 994482 +Q8TAF3 Q8TAF3;C9JC24;F8W7Q3;F8W9K4 WDR48_HUMAN WDR48 WD repeat-containing protein 48 67974.2 85810.9 142624 67529.2 79915.5 69266.2 107192 48177.1 50470.9 75210.9 80121.1 63456.3 53516.8 73514 136066 93985.6 82023.7 82499.1 95953.5 43406.6 +Q8TAQ2 Q8TAQ2;Q92922;F8VXC8 SMRC2_HUMAN SMARCC2 SWI/SNF complex subunit SMARCC2 42418.1 41922.3 54774.1 47377.4 98633.4 43757.9 111141 41053.7 77572.5 62820.7 90463.1 74366.6 31542.8 65960.9 24992.2 67000.9 49094 81445.3 +Q8TBC4 Q8TBC4;F8W8D4 UBA3_HUMAN UBA3 NEDD8-activating enzyme E1 catalytic subunit 107440 67120.7 61689.9 75283.3 71753.7 47565.2 73360.4 59626.2 61180.5 43704 65181.8 67233.4 79364.6 35818.1 51396.1 59179.7 74266 57123.3 +Q8TEA8 Q8TEA8 DTD1_HUMAN DTD1 D-aminoacyl-tRNA deacylase 1 142900 197810 173040 148505 162056 127793 195919 153675 107000 157423 211719 120412 114631 149804 264810 221611 144995 179281 183714 151021 +Q8TEQ6 Q8TEQ6 GEMI5_HUMAN GEMIN5 Gem-associated protein 5 135009 127053 105610 121416 130621 131637 124165 158842 75786.5 116641 112210 133854 110332 110588 90148.3 102368 72545 90310.9 144746 75847.5 +Q8TEX9 Q8TEX9;H0YL92;H0YLV0;H0YN14 IPO4_HUMAN IPO4 Importin-4 63006.8 102937 36759.8 63712.3 100176 68668.6 97605.3 102060 67688.2 94426.6 66158.9 88099.3 116240 +Q8TF66 Q8TF66 LRC15_HUMAN LRRC15 Leucine-rich repeat-containing protein 15 1.47817e+06 84871.2 +Q8WTS6 Q8WTS6;D6RJA0 SETD7_HUMAN SETD7 Histone-lysine N-methyltransferase SETD7 72929.3 117873 163262 151306 217698 146786 156338 167139 243523 130289 151761 125781 213824 174179 181188 142877 154550 142427 181318 +Q8WU90 Q8WU90;H7C466 ZC3HF_HUMAN ZC3H15 Zinc finger CCCH domain-containing protein 15 41546 59075.7 49323.1 36074.8 25924.5 40344 46107.8 58499.1 35850.1 63222.4 75878.9 62351.2 64527.4 49419.6 +Q8WUA8 Q8WUA8 TSK_HUMAN TSKU Tsukushi 62740.2 30220.2 56964.5 28275.9 45250.3 22219.6 61258.2 51845.2 28465.9 28763.2 36182.7 +Q8WUM4 Q8WUM4 PDC6I_HUMAN PDCD6IP Programmed cell death 6-interacting protein 255718 173816 137992 179052 274789 233962 244994 228167 201786 209991 271688 209002 259181 248840 234389 220540 259964 216972 322813 152706 +Q8WUX2 Q8WUX2 CHAC2_HUMAN CHAC2 Glutathione-specific gamma-glutamylcyclotransferase 2 46206.7 32679.8 7361.47 37199 16603.9 20561.3 35493.1 28020.9 36554.2 32509.5 32340 57166.7 44821.3 +Q8WVC0 Q8WVC0 LEO1_HUMAN LEO1 RNA polymerase-associated protein LEO1 148487 75450 99731 121199 41569.4 58853.1 65945 82185.5 88142.7 90383.3 66553.2 37094.9 101668 +Q8WVQ1 Q8WVQ1 CANT1_HUMAN CANT1 Soluble calcium-activated nucleotidase 1 259230 110984 333156 186707 251819 141554 341184 112416 277816 244270 338531 184787 287766 206549 274572 216234 759248 268305 276462 212635 +Q8WVV4 Q8WVV4 POF1B_HUMAN POF1B Protein POF1B 94539.2 44642.3 100628 90628.8 41011.2 56253.4 +Q8WVY7 Q8WVY7 UBCP1_HUMAN UBLCP1 Ubiquitin-like domain-containing CTD phosphatase 1 99997.3 167707 115758 89900.5 150169 125754 166985 152741 96541 155624 100038 83604.6 111189 202912 171599 119633 186034 126274 +Q8WW12 Q8WW12 PCNP_HUMAN PCNP PEST proteolytic signal-containing nuclear protein 102404 58691.4 117652 105251 168964 159035 174264 146082 142724 181111 183899 169054 148749 115582 176855 115429 116200 62705.6 182055 108149 +Q8WWI1 Q8WWI1;E9PMS6;F8WD26;A0A0A0MTE2;A0A8V8TKQ1;E9PJ10;E9PK58;E9PLU6;E9PMP7;E9PMT2;H0Y424;J3KP06 LMO7_HUMAN LMO7 LIM domain only protein 7 47440.7 110837 40036.1 100086 29103.3 102733 98320.4 81343.1 91041 62355 78285.5 60340.8 46690 54768.7 +Q8WWM7 Q8WWM7;H3BUF6;H3BSK9 ATX2L_HUMAN ATXN2L Ataxin-2-like protein 32286.9 50028.8 87066.3 26603.3 54797.5 62292.6 95129.4 34516.1 48200.4 75423 95225.9 42394.6 58255.9 80022.4 78788.6 36451.3 +Q8WX93 Q8WX93;H0YA05 PALLD_HUMAN PALLD Palladin 20459.6 57349.4 51431 48489.1 38415.9 37184 52365.9 +Q8WXF1 Q8WXF1;X6RDA4 PSPC1_HUMAN PSPC1 Paraspeckle component 1 16425.7 26033.9 79908.5 83031.1 23438.5 78100.5 52034.4 93169.8 41407.2 86236.7 55742.8 63679 54756.2 49301.6 63351.6 67234.2 40279.6 +Q8WXI7 Q8WXI7;M0QZZ9;M0R2S7;M0R2Y5 MUC16_HUMAN MUC16 Mucin-16 147861 271764 156441 127813 161024 224476 200110 295319 231715 256147 +Q8WXI9 Q8WXI9;A0A0U1RR30;A0A0U1RRM1 P66B_HUMAN GATAD2B Transcriptional repressor p66-beta 12102.1 32951.2 12218.1 31816.7 31965.2 23422 32137.8 +Q92466 Q92466 DDB2_HUMAN DDB2 DNA damage-binding protein 2 86206.6 133487 63167 163796 64242.2 137640 84379.7 118888 40119.5 109114 85394.7 142444 100877 107760 115197 153433 53613.4 88657.4 90105 131428 +Q92484 Q92484 ASM3A_HUMAN SMPDL3A Acid sphingomyelinase-like phosphodiesterase 3a 105303 106523 57608.7 147489 71439.5 143516 51796.8 166436 93964.6 141342 54804.9 171146 97542.2 158139 85146.5 151152 134558 106239 +Q92499 Q92499;A0A087WZ71;A0A087X2G1;A0A7I2V2M5;A0A7I2V430;A0A7I2V4F0;A0A7I2YQ77 DDX1_HUMAN DDX1 ATP-dependent RNA helicase DDX1 278561 251341 194635 322020 451014 475834 353051 410565 379183 421709 277272 371718 424032 491976 326272 393655 249934 406778 376044 198012 +Q92520 Q92520;C9JMN4;C9JP35 FAM3C_HUMAN FAM3C Protein FAM3C 52837.2 50208.5 59571.9 67599.6 88031.9 65382.3 120202 34546.3 92075.4 80944.2 95009.8 45750.4 92093.3 62959.1 61087.2 66368.5 113720 99409.3 60309.2 59517.5 +Q92526 Q92526 TCPW_HUMAN CCT6B T-complex protein 1 subunit zeta-2 417424 399155 422377 423862 482172 448834 425529 391765 599376 +Q92541 Q92541 RTF1_HUMAN RTF1 RNA polymerase-associated protein RTF1 homolog 21735.2 33039.4 32408.2 41635.4 45224.5 34505.3 25907.3 46289.4 17763.5 +Q92597 Q92597;A0A7I2ST14;E7ESM1 NDRG1_HUMAN NDRG1 Protein NDRG1 37208.7 32864.9 62092.8 29741.2 22945.9 36716.7 20360.3 +Q92598 Q92598;A0A0A0MSM0 HS105_HUMAN HSPH1 Heat shock protein 105 kDa 628518 582596 533663 464103 600596 495517 623305 444369 512309 535769 611214 433744 556332 490751 718499 522134 702824 536833 659052 484490 +Q92616 Q92616 GCN1_HUMAN GCN1 eIF-2-alpha kinase activator GCN1 43702.6 84608.9 74336.4 108819 29435.2 89030.2 83007.6 65810.5 74730.8 91696.7 102228 78954 97939.2 86208.4 107999 +Q92626 Q92626 PXDN_HUMAN PXDN Peroxidasin homolog 1.62179e+06 260792 1.43608e+06 512681 2.02317e+06 480117 1.92928e+06 480879 2.44009e+06 430852 2.12425e+06 609536 2.03714e+06 545598 1.70469e+06 475375 1.61743e+06 475028 1.09223e+06 284962 +Q92688 Q92688 AN32B_HUMAN ANP32B Acidic leucine-rich nuclear phosphoprotein 32 family member B 1.69814e+06 820765 1.56989e+06 1.01316e+06 1.0822e+06 724711 1.24576e+06 631164 1.26175e+06 799625 1.40944e+06 757939 1.15234e+06 676205 788140 826361 1.36822e+06 728180 1.49484e+06 470579 +Q92692 Q92692;K7EKE8 NECT2_HUMAN NECTIN2 Nectin-2 162128 109502 104272 152428 183435 144376 191435 132199 190502 182465 158334 135776 205885 188715 197183 151240 109133 133671 123823 174295 +Q92696 Q92696;H0YLH3 PGTA_HUMAN RABGGTA Geranylgeranyl transferase type-2 subunit alpha 33541.3 32712.7 28393.2 51605.6 42446.5 40754.2 +Q92743 Q92743;A0A3B3IU24;H0Y7G9 HTRA1_HUMAN HTRA1 Serine protease HTRA1 433585 349400 492054 657035 647644 562070 585323 684630 727133 635410 707484 750528 675716 669173 560630 584748 607161 619580 496903 539802 +Q92769 Q13547;Q92769;Q5TEE2;E5RFI6;E5RFP9;E5RG37;E5RGV4;E5RH52;E5RHE7;E5RJ04;E5RK19;H3BM24 HDAC2_HUMAN HDAC2 Histone deacetylase 2 376318 340243 361455 560640 362045 411730 339046 392357 202586 320504 369683 459688 305338 359627 238689 463298 404488 329230 509779 405414 +Q92820 Q92820;A0A7I2V5P2;A0A7I2V5X9;A0A7I2YQQ3 GGH_HUMAN GGH Gamma-glutamyl hydrolase 2.92799e+06 2.67536e+06 3.4589e+06 3.761e+06 3.54868e+06 3.32604e+06 4.45745e+06 2.91449e+06 4.33881e+06 4.42643e+06 4.01858e+06 3.53833e+06 3.71982e+06 3.69301e+06 2.70002e+06 4.44485e+06 4.94738e+06 3.9567e+06 2.42902e+06 5.65727e+06 +Q92839 Q92839;G3V1S7;M0QXH4;M0R2V0 HYAS1_HUMAN HAS1 Hyaluronan synthase 1 796338 956548 566689 729295 1.21607e+06 764331 1.07049e+06 1.00712e+06 1.17575e+06 595436 1.14722e+06 744429 1.16579e+06 976121 1.06435e+06 865945 859137 736771 1.19876e+06 542861 +Q92841 Q92841;A0A1X7SBZ2;A0A5H1ZRQ2 DDX17_HUMAN DDX17 Probable ATP-dependent RNA helicase DDX17 383341 402402 378246 516569 490226 636470 456162 612698 504958 568848 406325 556188 442010 587460 431595 534109 458972 523501 700047 416677 +Q92859 Q92859 NEO1_HUMAN NEO1 Neogenin 44989.4 114959 77575.3 96285.2 55981.3 84085.3 65362.7 138100 84616.6 64682.2 +Q92878 Q92878;A0A1W2PQ90;A0A494BZX5;A0A494C122;A0A494C1B7;E7EN38;E7ESD9;A0A494C0Y7 RAD50_HUMAN RAD50 DNA repair protein RAD50 59037.4 82932.9 111703 99079.7 72309.2 70710 90235.1 91535.9 104042 85618.7 88274.3 52926.9 74741.5 135774 100616 38010.8 +Q92882 Q92882 OSTF1_HUMAN OSTF1 Osteoclast-stimulating factor 1 77352 45497.9 37014.8 74914.1 78591.8 48661.1 81303.9 77919.5 61600.6 64536.5 70444.5 91673.6 86414 94391.7 70157.2 61486.5 49063.5 +Q92890 Q92890;C9J6N9 UFD1_HUMAN UFD1 Ubiquitin recognition factor in ER-associated degradation protein 1 35296.1 82022.8 86408.6 16189.4 39116.1 114591 118229 25841.4 24263.4 145756 94042.6 31152.6 61428.4 30857.1 +Q92896 Q92896;H3BM42;H3BQU9;H3BQT1 GSLG1_HUMAN GLG1 Golgi apparatus protein 1 89498.4 63960.8 155076 111756 115158 80915.8 113948 93504.3 128535 85487.6 135619 116439 114028 99660.2 103092 107217 90896.9 63673.9 63853.8 75968.3 +Q92900 Q92900 RENT1_HUMAN UPF1 Regulator of nonsense transcripts 1 108066 124086 152271 120720 197209 107499 217604 126553 172856 110351 181775 129170 199344 135831 144800 82898.4 134241 161253 98399.1 +Q92945 Q92945;M0R0C6;M0QXW7;M0QYH3 FUBP2_HUMAN KHSRP Far upstream element-binding protein 2 103176 13825 53523.4 77561.8 109285 132022 55027.4 99824.4 129422 213029 55863.5 93091 155288 157063 83340.7 49307.6 114165 117907 127457 50004 +Q92954 Q92954 PRG4_HUMAN PRG4 Proteoglycan 4 226086 2.4656e+06 198922 4.80575e+06 319828 3.98664e+06 299396 4.37238e+06 315375 4.44696e+06 335266 6.32088e+06 331712 5.16582e+06 359762 4.61375e+06 284235 5.09786e+06 219480 4.49333e+06 +Q92973 Q92973;A0A7P0T944;A0A7P0T9C0;A0A7P0TBF0;A0A7P0Z4D3 TNPO1_HUMAN TNPO1 Transportin-1 206054 213140 102572 174294 215232 222034 156021 223518 206877 210513 213674 209774 162475 222293 134264 186347 168898 183622 288862 80153.3 +Q92979 Q92979;A0A087WVM7 NEP1_HUMAN EMG1 Ribosomal RNA small subunit methyltransferase NEP1 86673.5 128597 61910.7 111874 138196 88616.9 146108 101646 124601 95955.6 132112 93192.9 99729.1 120516 119410 129682 185901 112176 +Q93008 Q93008 USP9X_HUMAN USP9X Probable ubiquitin carboxyl-terminal hydrolase FAF-X 144116 255345 182252 183583 207537 120633 195240 121229 212626 156459 211943 167812 184087 123429 224925 128311 158486 166994 +Q93009 Q93009;A0A669KBL1;H3BND8 UBP7_HUMAN USP7 Ubiquitin carboxyl-terminal hydrolase 7 191040 286834 141148 290220 198871 268299 182726 256571 178561 240711 184896 274812 214589 233457 219194 248664 215517 230072 200406 165834 +Q93045 Q93045;E5RGX5 STMN2_HUMAN STMN2 Stathmin-2 409635 393007 250438 320559 +Q93063 Q93063;A0A804HKG2;A0A804HL74;A0A804HHX4 EXT2_HUMAN EXT2 Exostosin-2 77274.9 89307.6 89015.4 155564 136808 160449 132020 175764 111783 163944 129672 225170 114263 169200 166110 211301 91979.1 155656 92184.6 154757 +Q95121 Q95121 SWISS-PROT:Q95121 SWISS-PROT:Q95121 42332.8 34116.4 36120.3 36323.1 36822 30626.9 50675.9 34170.8 41848.1 49771.9 43744.8 60470.3 26630.7 69452.1 13253 +Q969H8 Q969H8;M0QXF7;M0QYN0 MYDGF_HUMAN MYDGF Myeloid-derived growth factor 476615 507209 395697 554721 426731 392844 435971 320015 336988 428104 339202 327338 336789 349896 282862 407254 409007 421332 418991 365169 +Q969P0 Q969P0;C9J8Z4 IGSF8_HUMAN IGSF8 Immunoglobulin superfamily member 8 86474.8 71695 53694.5 70652.9 56057.1 116352 71072.3 102528 73137.9 80026.7 75007.7 +Q969Q0 P83881;Q969Q0;H0Y5B4;H7BZ11;J3KQN4 RL36L_HUMAN RPL36AL 60S ribosomal protein L36a-like 992601 927317 764308 1.05683e+06 745829 714646 826021 828438 547597 445541 925414 774533 585606 578074 726257 797857 352193 510646 779494 411095 +Q96AC1 Q96AC1;A0A0U1RRM8;H0YJ34 FERM2_HUMAN FERMT2 Fermitin family homolog 2 59933.3 124540 80994.2 69696.4 119389 42438.6 119589 87689.5 66598.9 74107.6 41333.7 101981 106390 69232.8 93171.1 127949 +Q96AE4 Q96AE4;E9PEB5;C9JSZ1 FUBP1_HUMAN FUBP1 Far upstream element-binding protein 1 31873.6 72187.1 89861.3 175786 60435.4 95309.6 178424 348003 71871.8 77879.2 179765 155963 67726.9 65167.3 98557.5 148227 93460.3 +Q96AY3 Q96AY3;K7ESG6 FKB10_HUMAN FKBP10 Peptidyl-prolyl cis-trans isomerase FKBP10 77239.2 106302 175325 179256 121073 135113 144499 107706 94066.1 134967 136451 132977 112643 118218 87617.1 154692 69194 119967 71230.6 175697 +Q96B54 Q96B54;M0QXZ5 ZN428_HUMAN ZNF428 Zinc finger protein 428 83486.5 48423.7 75114 49914.3 78839.9 53814.5 82423.4 36537.2 61265.5 56307.9 59892.8 76898.6 19531.3 34143.7 51326.5 +Q96B97 Q96B97;A0A8V8TNL0;A0A8V8TNP7;A0A8V8TNS8;A0A8V8TNT5;A0A8V8TNW8;A0A8V8TP27;A0A8V8TP33;A0A8V8TP40;A0A8V8TP63;A0A8V8TP69;A0A8V8TPM1;A0A8V8TQ02;A0A8V8TQD8;A0A8V8TQF6;A0A8V8TQG3;A0A8V8TQU1;Q5JPT2 SH3K1_HUMAN SH3KBP1 SH3 domain-containing kinase-binding protein 1 35233.2 18744.9 27069.6 12143.3 23353.1 +Q96C19 Q96C19 EFHD2_HUMAN EFHD2 EF-hand domain-containing protein D2 53291.2 120876 129629 94439 112712 99411.4 147058 84110.3 172410 71466.3 147376 110418 150583 133606 147503 39648.8 96495.1 139779 51433.1 +Q96C86 Q96C86 DCPS_HUMAN DCPS m7GpppX diphosphatase 111674 165082 131087 129335 89621.3 105175 113111 79018.9 63112.2 142640 98048.4 101758 80699.6 105356 101860 109742 119762 98935.9 105298 77165.5 +Q96C90 Q96C90 PP14B_HUMAN PPP1R14B Protein phosphatase 1 regulatory subunit 14B 142402 164406 124520 127639 160661 144038 242744 153574 165532 157447 212859 130982 119164 141583 294060 212369 91944.3 159346 243573 159721 +Q96CN7 Q96CN7;D6RGE2 ISOC1_HUMAN ISOC1 Isochorismatase domain-containing protein 1 182773 166853 185463 167386 186022 173168 161256 165839 187308 132851 150949 138428 158062 166209 154813 110395 97650.5 141720 118934 90136.5 +Q96CP2 Q4VC44;Q96CP2;I3L0R3;I3L1Y9;I3L231;I3L4I0 FWCH2_HUMAN FLYWCH2 FLYWCH family member 2 14519.5 56945.8 95494.2 33421.4 76724.4 18734.6 85955.1 76944.8 35627.3 58484.4 55559.9 46173.6 64625.8 21718.8 34391.8 +Q96CT7 Q96CT7 CC124_HUMAN CCDC124 Coiled-coil domain-containing protein 124 210441 232996 393489 261717 268219 224246 267249 172587 261600 185871 216005 164625 217397 184464 325279 167463 187608 118966 241573 87751.7 +Q96CW1 Q96CW1;A0A087WY71;A0A8I5KWD3;C9JGT8;C9JJD3;C9JPV8;E9PFW3;H7C4C3 AP2M1_HUMAN AP2M1 AP-2 complex subunit mu 185969 154667 78274.4 142570 164921 217434 154877 183757 129043 134686 150213 176027 107339 154469 52292.8 142659 80406.1 137274 164016 73508.5 +Q96D15 Q96D15;M0QZH0 RCN3_HUMAN RCN3 Reticulocalbin-3 48969.5 145098 220619 62934.2 141713 103264 84323 183088 122133 109535 37763.9 109595 130128 164996 63673.4 149323 56583 +Q96DG6 Q96DG6 CMBL_HUMAN CMBL Carboxymethylenebutenolidase homolog 403171 328338 196647 260850 264636 286619 283883 188719 204619 357250 247262 304087 347805 289582 254388 312878 479174 356532 328787 509098 +Q96EE3 Q96EE3;K7ELV2;K7ENI5 SEH1_HUMAN SEH1L Nucleoporin SEH1 54547 34702.9 27528.2 55585.2 31044.7 23906 33196.5 33765.9 31405.8 41915.6 23535.5 21446.4 37348.3 +Q96EK5 Q96EK5;A0A1B0GUA3;A0A1B0GUH6;A0A6Q8PH45;A0A6Q8PGG2;A0A6Q8PHL8 KBP_HUMAN KIFBP KIF-binding protein 122247 135916 115459 129386 167611 135039 148060 110946 136903 146365 132212 130277 168631 86242 152517 100650 128653 164055 81950.8 +Q96EK6 Q96EK6;G3V4W4;G3V5E4 GNA1_HUMAN GNPNAT1 Glucosamine 6-phosphate N-acetyltransferase 27459 +Q96FQ6 Q96FQ6 S10AG_HUMAN S100A16 Protein S100-A16 88706.7 228813 190931 284214 69317 112924 132106 158588 76593.8 92748.9 123228 237950 121327 118680 242972 30356.2 121053 163642 239787 +Q96FW1 Q96FW1;F5GYJ8;F5GYN4;F5H3F0;F5H6Q1 OTUB1_HUMAN OTUB1 Ubiquitin thioesterase OTUB1 299100 231531 196052 182390 292026 216468 290586 190942 276262 267292 288254 211328 385290 251715 343609 264931 298200 238002 258454 335159 +Q96G03 Q96G03 PGM2_HUMAN PGM2 Phosphopentomutase 205050 256838 201423 206302 215049 203071 255158 154133 174306 180305 202420 138662 231450 144914 237029 256626 295295 159793 226029 214680 +Q96GD0 Q96GD0 PLPP_HUMAN PDXP Chronophin 148209 139953 158793 126709 207338 117533 191292 162075 216117 146784 216596 162843 174612 199101 276015 212451 145927 168923 172524 +Q96GG9 Q96GG9;F8WC32 DCNL1_HUMAN DCUN1D1 DCN1-like protein 1 53512 71006.4 55335.4 31858.8 51234.3 53559.6 31617.3 56134 26496 31469 +Q96GM8 Q96GM8 TOE1_HUMAN TOE1 Target of EGR1 protein 1 83696.5 116936 86086.1 100022 98083.4 90285.5 117029 119075 95477.1 91631.8 83635.4 98871.3 67975.8 100221 80871.6 107577 80946 72224.8 60953.4 +Q96GQ7 Q96GQ7;B7Z6D5;A0A087WYH5 DDX27_HUMAN DDX27 Probable ATP-dependent RNA helicase DDX27 26972.9 +Q96GX9 Q96GX9;S4R3D6 MTNB_HUMAN APIP Methylthioribulose-1-phosphate dehydratase 23058.7 34110.2 29233.3 31367.1 20349.9 31020.3 27985.1 +Q96HC4 Q96HC4;A0A0A0MSP3;H0Y8Y3 PDLI5_HUMAN PDLIM5 PDZ and LIM domain protein 5 18457.7 18727.9 16246.7 14167.1 33155.2 14042.4 +Q96HE7 Q96HE7;G3V3E6;G3V5B3 ERO1A_HUMAN ERO1A ERO1-like protein alpha 141946 66143.4 101044 87262.5 122092 71716.4 127596 89055.6 146777 95818 170763 125403 88195.6 103427 120934 82320.4 96374.4 85839.7 99914.5 +Q96I82 Q96I82 KAZD1_HUMAN KAZALD1 Kazal-type serine protease inhibitor domain-containing protein 1 55341.8 33019.9 73670.3 30751.6 28670 24387.4 15463.3 27794.2 44697.7 49748.9 44626.8 47405.2 34830.1 32028.3 15909.7 +Q96IZ0 Q96IZ0 PAWR_HUMAN PAWR PRKC apoptosis WT1 regulator protein 21108.4 15139.4 45944.7 41689.2 81848.4 47224.2 61549.7 31894.6 90095.8 57203.8 53409.5 42226.5 76485.5 24678.1 58859.5 60334.2 58966.3 56841.8 +Q96JB1 Q8TE73;Q96JB1;A0A075B6F3;A0A7P0Z455;H0Y7V4 DYH8_HUMAN DNAH8 Dynein axonemal heavy chain 8 935928 2.21668e+06 1.02129e+06 966476 1.03656e+06 949503 791304 567611 1.37875e+06 1.00662e+06 932924 646093 2.36091e+06 1.07328e+06 1.23182e+06 977898 2.64269e+06 1.47472e+06 608595 1.65076e+06 +Q96JD6 Q96JD6;G3V1C1 AKCL2_HUMAN AKR1E2 1,5-anhydro-D-fructose reductase 353587 661389 251148 539773 361152 508896 352732 362184 384583 617300 256411 301130 474715 590977 413775 465542 399356 660174 310460 487658 +Q96K17 Q96K17;E9PL10 BT3L4_HUMAN BTF3L4 Transcription factor BTF3 homolog 4 83775.1 55666.5 138374 106210 152522 95888.4 116216 118419 89185.1 80991.8 134878 126076 120863 102843 +Q96KB5 Q96KB5;E5RFX4 TOPK_HUMAN PBK Lymphokine-activated killer T-cell-originated protein kinase 23828.3 56568.3 45446.5 31959.7 69449.9 38940.6 38938.7 16532.4 31505.4 59872.7 +Q96KP4 Q96KP4 CNDP2_HUMAN CNDP2 Cytosolic non-specific dipeptidase 354414 426829 319504 416201 361557 377973 383231 302363 327277 461111 347922 337611 330775 396228 332794 380723 375945 439588 299792 344641 +Q96L92 Q96L92;A0A2R8Y871;A0A2R8Y8A7;A0A2R8YD75;H7C603 SNX27_HUMAN SNX27 Sorting nexin-27 40968.5 29253.7 61504.5 58983.7 59153.1 58560.7 69972 44951.9 53391.5 68074.8 57358.4 +Q96MH2 Q96MH2;K7EJA3;K7ELS4;K7ERG7 HEXI2_HUMAN HEXIM2 Protein HEXIM2 37098.9 89411.8 70068.1 73556.8 96462.1 59575.3 87099.1 95606.6 57543.1 81561 68899.6 60485.1 42072.3 88812.7 79487 +Q96MW1 Q96MW1;Q86WV7 CCD43_HUMAN CCDC43 Coiled-coil domain-containing protein 43 83297.4 49763.8 59256.6 51884.2 87195.5 87931.6 65378.1 85471.8 52292.7 68748.1 92308.5 95049.8 96020.3 89038.9 101538 56732.3 55459.6 66642.2 39218.6 +Q96P63 Q96P63 SPB12_HUMAN SERPINB12 Serpin B12 146816 17771.2 25820.4 52459.8 56441 28745.8 19685.3 31691.9 208793 67575.5 57983.3 33813.6 133051 33203.2 34674.6 18582.8 159997 +Q96P70 Q96P70 IPO9_HUMAN IPO9 Importin-9 131553 109739 147834 98964.2 136850 98419.9 92976.8 95251.7 124818 94772.1 106256 190871 110699 97680 100908 95215.5 126817 139478 +Q96PK6 Q96PK6 RBM14_HUMAN RBM14 RNA-binding protein 14 33589.8 25916 35591.6 19852.1 46573 41426.5 47383.1 17074.4 54361 20030.7 31084.3 44843.1 28432 +Q96PZ0 Q96PZ0;E7EUH7;H0Y863 PUS7_HUMAN PUS7 Pseudouridylate synthase 7 homolog 79267.2 87541.5 94492.2 71312.1 106741 84948.9 91282.6 109715 77520.9 85579.8 41020.3 95880 78297 72161.7 40074.4 58360.4 98952.2 +Q96Q05 Q96Q05;H0YBR0 TPPC9_HUMAN TRAPPC9 Trafficking protein particle complex subunit 9 87280.4 35982.4 42354.8 +Q96QA5 Q96QA5;J3KRG2 GSDMA_HUMAN GSDMA Gasdermin-A 93694.1 26111.7 27453.7 21316.5 27974.5 44764.7 26619.3 83510.3 +Q96QD8 Q96QD8;F8VUY8 S38A2_HUMAN SLC38A2 Sodium-coupled neutral amino acid symporter 2 84299.9 38109.3 68059 21988.7 98600.4 17483.1 65982.7 19532 104665 17934 64708.5 69039.4 23091 66047.3 +Q96QK1 Q96QK1 VPS35_HUMAN VPS35 Vacuolar protein sorting-associated protein 35 637488 544836 369576 459560 633697 571934 468243 562109 502964 499791 547060 477095 534313 508319 546237 460342 436865 466835 555906 298262 +Q96RS6 Q96RS6;A0A7I2V457 NUDC1_HUMAN NUDCD1 NudC domain-containing protein 1 94454.7 187246 166762 79036.8 159552 55045.4 175976 29353.9 171972 73536.4 157041 58731.9 162438 69646.1 150500 73463.6 174875 104346 57714 +Q96S55 Q96S55 WRIP1_HUMAN WRNIP1 ATPase WRNIP1 41533.7 30636.4 34474.3 36809.4 52546.7 55082.5 17970.8 36236.9 24466.3 49631.8 47981.4 42164.7 34209.9 30192.2 +Q96SB4 Q96SB4;H3BLV9 SRPK1_HUMAN SRPK1 SRSF protein kinase 1 18295.2 28203.2 18940.3 27317.5 29508.7 +Q96SI9 Q96SI9 STRBP_HUMAN STRBP Spermatid perinuclear RNA-binding protein 62476.5 36123.9 73785.2 55693.9 31201.8 68803.1 88914.8 38079.1 260309 35381.1 +Q96TA1 Q96TA1 NIBA2_HUMAN NIBAN2 Protein Niban 2 114674 175320 41922.5 73309.3 106401 133196 97315.6 161789 95008.3 148081 96511.7 114662 129308 135313 114631 136505 68174.5 97738.4 114384 72318.9 +Q99417 Q99417;A0A087WV05;A0A0D9SEI7 MYCBP_HUMAN MYCBP c-Myc-binding protein 88730.2 102604 104238 138358 149654 110782 110230 122852 85215.8 107589 108537 133786 117112 98618.2 143243 100384 87978.7 36907.8 105048 +Q99426 Q99426;A0A494C0X0;K7EK42;K7EP07 TBCB_HUMAN TBCB Tubulin-folding cofactor B 46383.7 59543.7 46672.1 66370.1 52641.9 51187.1 43455.6 56545.6 65283.3 53704.4 45554.9 79952 36283.2 73948.5 54979.7 65219.6 19530.4 80737.7 +Q99436 Q99436;Q5TBG5 PSB7_HUMAN PSMB7 Proteasome subunit beta type-7 1.00264e+06 544013 828654 620910 665182 712222 839234 496403 680403 814196 769349 556947 713207 676506 804515 790328 1.09646e+06 765426 761006 695310 +Q99459 Q99459 CDC5L_HUMAN CDC5L Cell division cycle 5-like protein 50327.3 58426.4 93461.3 44667.9 68243.4 77004.6 80340.9 55158.1 77483.5 42081.8 73915.9 55612.1 63769.3 41650.2 60759.3 67619.9 +Q99460 Q99460;A0A087WW66;A0A7I2V262;A0A7I2V2K8;A0A7I2V4X0;A0A7I2V523;A0A7I2V5C8;A0A7I2V641;A0A7I2YQI9;A0A7I2YQY1;A0A7P0MW50 PSMD1_HUMAN PSMD1 26S proteasome non-ATPase regulatory subunit 1 199416 161217 84985.7 138000 180703 172761 123975 175803 145576 141811 159816 178265 156234 151419 134809 143332 96243.1 126347 182856 84220.5 +Q99471 Q99471 PFD5_HUMAN PFDN5 Prefoldin subunit 5 32278.2 51668.9 59828.2 49194.3 46847.2 61756.6 28401.1 67814.2 77424.1 45731.6 44784.1 18505.7 38319 61746.7 106199 +Q99497 Q99497;K7ELW0 PARK7_HUMAN PARK7 Parkinson disease protein 7 1.8422e+06 1.46409e+06 1.79609e+06 1.30541e+06 1.52158e+06 1.20302e+06 1.88471e+06 1.02731e+06 1.45108e+06 1.35357e+06 1.50332e+06 990127 1.36325e+06 1.10736e+06 1.86475e+06 1.41709e+06 1.64818e+06 999206 1.48642e+06 1.68035e+06 +Q99519 Q99519;E9PIF4 NEUR1_HUMAN NEU1 Sialidase-1 982616 228698 799561 409152 977078 370988 865449 383672 1.0581e+06 389527 955756 511874 1.04134e+06 409668 749792 311682 1.00389e+06 400810 619879 325676 +Q99536 Q99536;K7EM19;K7ENX2;K7ESA3 VAT1_HUMAN VAT1 Synaptic vesicle membrane protein VAT-1 homolog 210189 337966 113418 349818 293243 477095 160477 453788 380251 408863 244661 401114 320940 529397 290325 455888 160821 455300 234565 249052 +Q99538 Q99538 LGMN_HUMAN LGMN Legumain 355537 383308 646358 575268 390417 515919 488637 460270 472907 656851 463246 611279 584166 631913 338160 943602 601424 865922 394887 852003 +Q99543 Q99543;H7C3L7 DNJC2_HUMAN DNAJC2 DnaJ homolog subfamily C member 2 70087.1 52442.5 47244.6 96153.8 55573.2 53045.6 21870.5 30587.9 21359.3 57278.7 69485.1 69253.1 59837.4 19651.3 63064 +Q99574 Q99574;H7C5T9 NEUS_HUMAN SERPINI1 Neuroserpin 1.51343e+06 388654 1.48845e+06 673275 1.46437e+06 613627 1.64932e+06 585201 1.75677e+06 833510 1.64357e+06 972752 1.73217e+06 666021 1.4305e+06 875881 1.98099e+06 841177 1.34581e+06 1.04934e+06 +Q99584 Q99584 S10AD_HUMAN S100A13 Protein S100-A13 40959.3 42182.6 108207 90832.6 14346.3 13472.6 40500.9 36139.3 59224 88834.1 40540.9 13734.9 63214.2 67652.7 35031.7 61094.8 103264 +Q99598 Q99598;Q5VVQ1;C4P0D6;C4P0D8 TSNAX_HUMAN TSNAX Translin-associated protein X 205238 136098 196521 134223 146070 81673.1 187129 66059.4 159040 110716 193575 107654 179877 111452 285903 126307 336176 101979 240845 129203 +Q99614 Q99614;A0A804HIB8;A0A804HKC5;H0YB37 TTC1_HUMAN TTC1 Tetratricopeptide repeat protein 1 48959.4 54241.1 35578.2 94601.4 94681.3 112335 77301.5 85409.4 76959.4 113848 86393.3 81474.1 132443 108493 92629.2 138517 79954.7 125226 94099.3 58305.3 +Q99615 Q99615;A0A6I8PIK4;A0A6I8PLE7;A0A6I8PR89;A0A6I8PRZ0;A0A6I8PS32;A0A6I8PU73;A0A6I8PU89;A0A6I8PUC2;A0A6I8PUD4;K7EIH8;K7EK03;K7EPP7;K7ER44 DNJC7_HUMAN DNAJC7 DnaJ homolog subfamily C member 7 100078 41305.5 101731 123577 55014.9 109735 83591.1 75362.9 97907.5 100378 96625.4 99296.4 25174.9 44307.6 115713 43271.7 +Q99650 Q99650 OSMR_HUMAN OSMR Oncostatin-M-specific receptor subunit beta 178559 106239 122667 197897 201182 211129 199648 113639 196187 247568 168409 160466 230808 286850 203258 298081 331896 216597 139863 226694 +Q99714 Q99714;A0A804HHW5 HCD2_HUMAN HSD17B10 3-hydroxyacyl-CoA dehydrogenase type-2 139069 34936 66844.3 47131.7 49382.8 36239.8 29717.5 62623.3 56208.9 104263 54244.5 50036.3 39321.2 +Q99715 Q99715;D6RGG3 COCA1_HUMAN COL12A1 Collagen alpha-1(XII) chain 2.5607e+06 1.74968e+06 3.0509e+06 3.62676e+06 3.37008e+06 3.37246e+06 4.20042e+06 4.07213e+06 3.88971e+06 3.52965e+06 4.50011e+06 5.12689e+06 3.59695e+06 4.41166e+06 4.22732e+06 5.26669e+06 2.84032e+06 4.7365e+06 2.72424e+06 5.57576e+06 +Q99729 Q99729;D6R9P3;D6RBZ0;D6RD18 ROAA_HUMAN HNRNPAB Heterogeneous nuclear ribonucleoprotein A/B 95610.8 143454 115868 181032 265486 325703 123181 196136 281505 227999 85766.1 223172 278924 311172 165552 162548 179059 206628 90414 149891 +Q99733 Q99733;C9J1B1;C9J6D1;C9JZI7;E9PJJ2;E9PKI2;E9PKT8;E9PNJ7;E9PNW0;E9PP22;E9PS34 NP1L4_HUMAN NAP1L4 Nucleosome assembly protein 1-like 4 253573 128818 148234 162623 179235 114808 134325 138290 177947 106712 210734 295717 167608 163382 202556 174105 193713 285742 +Q99798 Q99798;A0A7I2V3F1;A0A7I2V3U0;A0A7I2V586;A0A7I2V5A1;A0A7I2V5T4;A0A7I2V614;A2A274 ACON_HUMAN ACO2 Aconitate hydratase, mitochondrial 29434 17681.5 +Q99832 Q99832 TCPH_HUMAN CCT7 T-complex protein 1 subunit eta 991381 869583 681923 758934 1.10554e+06 861052 850558 946631 972732 786332 979144 825636 974043 821725 853459 813237 688223 704404 1.15634e+06 656000 +Q99848 Q99848;H7C2Q8 EBP2_HUMAN EBNA1BP2 Probable rRNA-processing protein EBP2 122689 147865 100491 226035 72739.9 172725 77607.1 140887 81810.6 192163 93766.1 198613 98805.5 202044 60471.1 184579 65894.7 227209 86061.8 136582 +Q99873 Q99873;E9PKG1 ANM1_HUMAN PRMT1 Protein arginine N-methyltransferase 1 723685 367487 148022 446225 377609 388896 356041 353351 270061 324445 434255 331563 299891 381792 150581 293076 219371 279478 439603 254683 +Q99985 Q99985 SEM3C_HUMAN SEMA3C Semaphorin-3C 73791.3 94607.6 231113 138872 122826 135994 180111 142392 115055 129661 162825 102244 178963 98448.4 164980 61067.3 125512 95981.5 312049 +Q99996 Q99996;A0A0A0MRF6;A0A2R8Y590;A0A7P0T8N5;A0A7P0T928;A0A7P0T939;A0A7P0T9A9;A0A7P0TAD6;A0A7P0TAX3;A0A7P0TBH8;A0A7P0TBI4;A0A7P0Z4N5;A0A7P0Z4Q5 AKAP9_HUMAN AKAP9 A-kinase anchor protein 9 69461.9 64606.5 61080.4 71605 85949.9 109622 22108 64519 +Q9BPX3 Q9BPX3 CND3_HUMAN NCAPG Condensin complex subunit 3 55775.2 81685.6 44924 77165.3 85376.1 81335.4 73893.5 55519.2 63884.9 56405.6 70299.5 92551 81772.6 75610.6 71885.5 33474 76213.8 88732.2 +Q9BPX5 Q9BPX5 ARP5L_HUMAN ARPC5L Actin-related protein 2/3 complex subunit 5-like protein 847815 1.08896e+06 2.33631e+06 1.3374e+06 1.32273e+06 995876 1.40009e+06 1.10294e+06 1.39189e+06 1.09837e+06 1.27322e+06 868957 1.7042e+06 1.19345e+06 808388 1.40594e+06 1.66557e+06 1.63588e+06 1.26227e+06 1.73547e+06 +Q9BPZ3 Q9BPZ3;D6R9Y1 PAIP2_HUMAN PAIP2 Polyadenylate-binding protein-interacting protein 2 28360.1 43414.4 178534 197769 71874.4 234912 58402.7 69639.5 168740 210915 66950.7 83101.1 61253.2 +Q9BQ52 Q9BQ52;G5E9D5;E7ES68 RNZ2_HUMAN ELAC2 Zinc phosphodiesterase ELAC protein 2 119799 106979 73527.9 116665 117242 114574 128561 129570 86491.4 104692 115736 105570 68559.4 115639 108807 96554.5 67518 64442.3 130435 30560.5 +Q9BQ67 Q9BQ67 GRWD1_HUMAN GRWD1 Glutamate-rich WD repeat-containing protein 1 121503 67466 68326.7 104080 98920.7 65724.8 65474.4 76118.8 83107.1 99907.9 95401.5 88500.5 74331.8 151817 43270.6 150774 67243.4 119244 +Q9BQA1 Q9BQA1 MEP50_HUMAN WDR77 Methylosome protein 50 805812 826891 1.91774e+06 1.18271e+06 852072 681025 729756 718564 604290 715061 907129 838330 705676 556761 676539 639159 1.03474e+06 510804 715397 1.30175e+06 +Q9BQE3 Q9BQE3;F5H5D3 TBA1C_HUMAN TUBA1C Tubulin alpha-1C chain 5.49385e+06 6.72356e+06 2.35003e+06 4.49353e+06 5.39875e+06 5.76617e+06 4.51803e+06 6.61291e+06 4.41575e+06 5.8582e+06 4.83244e+06 5.39437e+06 5.26696e+06 5.76573e+06 5.00142e+06 5.79318e+06 5.02198e+06 5.92944e+06 6.66679e+06 4.64499e+06 +Q9BR76 Q9BR76;A0A087WW53;F5H0D2;F5H390 COR1B_HUMAN CORO1B Coronin-1B 75033.7 50002.4 56816.2 79146.4 33599.9 42185.7 14261.4 66568.3 49470.1 55068.2 63740.6 35612.3 60072.4 70088.6 +Q9BRA2 Q9BRA2;I3L0K2;I3L2R6;I3L3M7 TXD17_HUMAN TXNDC17 Thioredoxin domain-containing protein 17 1.16385e+06 1.34237e+06 1.40633e+06 1.26266e+06 1.31554e+06 989468 1.64713e+06 774598 1.30226e+06 1.29683e+06 1.31932e+06 809059 1.35026e+06 1.13232e+06 1.81338e+06 1.4554e+06 1.74371e+06 1.26907e+06 1.29172e+06 1.35468e+06 +Q9BRJ6 Q9BRJ6;C9JQV0 CG050_HUMAN C7orf50 Uncharacterized protein C7orf50 26976.1 29533.5 33665.1 38821.2 27859.5 53342.4 +Q9BRK5 Q9BRK5;A0A5F9UJX7;A0A5F9UP49;G3V1E2;H0Y3T6 CAB45_HUMAN SDF4 45 kDa calcium-binding protein 939907 712955 1.00361e+06 1.23921e+06 1.25439e+06 1.01242e+06 1.14219e+06 984305 1.30179e+06 1.36179e+06 1.33408e+06 1.60626e+06 1.18546e+06 996084 862429 1.19071e+06 1.19436e+06 805583 1.04535e+06 1.06222e+06 +Q9BRP8 Q9BRP8 PYM1_HUMAN PYM1 Partner of Y14 and mago 111532 110667 193147 138959 123980 236158 119426 173903 344077 133033 119545 128780 +Q9BS26 Q9BS26;A0A8I5KU76;A0A8I5KW59 ERP44_HUMAN ERP44 Endoplasmic reticulum resident protein 44 78881.6 75972.3 156023 102513 94922.5 62325 118198 110846 80675.4 107602 74842.9 +Q9BS40 Q9BS40 LXN_HUMAN LXN Latexin 796158 739795 435928 469945 596180 502568 621996 655853 697795 666316 +Q9BT78 Q9BT78;D6RAX7;D6RD63;D6RFN0 CSN4_HUMAN COPS4 COP9 signalosome complex subunit 4 284165 227270 163805 214615 330502 235684 244642 208010 293578 236127 283964 246716 296470 321666 251800 228246 286904 270577 262194 142775 +Q9BTE3 Q9BTE3 MCMBP_HUMAN MCMBP Mini-chromosome maintenance complex-binding protein 77845.3 102224 95659.8 108341 109716 70863 120478 82891.3 76896.3 87631.4 62991.1 76499.5 101287 77785.8 61515.7 50341.2 89921.8 91044.2 +Q9BTT0 Q9BTT0;E9PLC4;Q5TB19 AN32E_HUMAN ANP32E Acidic leucine-rich nuclear phosphoprotein 32 family member E 151799 157508 122474 106679 105627 114741 121794 77408.9 110062 79000.9 134547 111292 153560 69775.9 139717 157834 142385 117786 +Q9BTY2 Q9BTY2 FUCO2_HUMAN FUCA2 Plasma alpha-L-fucosidase 2.6715e+06 1.69499e+06 2.46536e+06 2.94856e+06 2.69693e+06 2.54416e+06 2.95548e+06 2.01254e+06 3.21645e+06 3.35017e+06 2.91342e+06 3.43546e+06 2.78895e+06 3.35261e+06 2.82272e+06 2.64169e+06 3.86097e+06 3.34655e+06 1.83576e+06 2.99159e+06 +Q9BUF5 Q9BUF5 TBB6_HUMAN TUBB6 Tubulin beta-6 chain 101781 124185 103222 150543 123747 94414.2 179546 94420 118826 201208 103144 81519.1 95108 97308.8 114423 59871.7 128267 250352 83461.9 +Q9BUJ2 Q9BUJ2;B7Z4B8;A0A0A0MRA5;M0R3F1 HNRL1_HUMAN HNRNPUL1 Heterogeneous nuclear ribonucleoprotein U-like protein 1 116055 105553 108960 105790 99372.5 125365 70576.2 362314 114249 105093 91765 100463 128235 99143.6 +Q9BUL8 Q9BUL8;C9J5C3;C9J6F3;C9J932;C9JND6;C9JSA3;F8WDF3;H7C5M9 PDC10_HUMAN PDCD10 Programmed cell death protein 10 161623 205453 137479 181467 195158 161948 148308 216467 188590 154359 168227 340710 180415 191591 177226 235324 177689 188915 +Q9BUP0 Q9BUP0;H7C0I0 EFHD1_HUMAN EFHD1 EF-hand domain-containing protein D1 82862 +Q9BUQ8 Q9BUQ8;A0A8V8TQ76;A0A8V8TR86;A0A8V8TRI2 DDX23_HUMAN DDX23 Probable ATP-dependent RNA helicase DDX23 194564 48343 41355.6 38843.9 64061.7 40775.6 54158.8 54963.7 40977.3 76486.9 60089.4 9473.37 45902.5 43659.2 33702.2 33171 72485.3 +Q9BV20 Q9BV20 MTNA_HUMAN MRI1 Methylthioribose-1-phosphate isomerase 56122.5 119323 113203 68724.5 159445 141047 62003.4 187328 72264.9 138146 91341.4 145221 110250 121707 104876 96753.1 +Q9BV57 Q9BV57;A0A0G2JQN5 MTND_HUMAN ADI1 Acireductone dioxygenase 728943 680659 412950 608807 686466 706203 631930 285666 593945 606116 593135 493797 627595 728637 544318 529784 708126 756872 624977 +Q9BVP2 Q9BVP2 GNL3_HUMAN GNL3 Guanine nucleotide-binding protein-like 3 311718 178090 536025 319903 224608 168005 308333 181202 235551 241003 298141 237316 268314 184363 226967 168241 307381 322633 197150 275861 +Q9BW91 Q9BW91;D6RAW2 NUDT9_HUMAN NUDT9 ADP-ribose pyrophosphatase, mitochondrial 47425.8 55785.9 40425.2 69547.5 64166.6 88423.7 47714.6 45264.5 74925.2 89814.4 45797.4 22503.8 66898.3 168514 71367 55094.8 49958.2 68249.7 45873.7 +Q9BWD1 Q9BWD1 THIC_HUMAN ACAT2 Acetyl-CoA acetyltransferase, cytosolic 56341.7 51993.8 33820 72225 58624.2 33713.2 56209.1 53721.4 70709.2 55038.6 67265.8 17640.7 +Q9BWF3 Q9BWF3 RBM4_HUMAN RBM4 RNA-binding protein 4 16031 55671.4 87391.3 34975.5 53774.2 69871.4 23887.7 53623.5 68773.4 13843.9 25286.7 49550.1 26367.7 +Q9BWJ5 Q9BWJ5 SF3B5_HUMAN SF3B5 Splicing factor 3B subunit 5 56509.5 53901 59504.8 61154.1 77001.5 78063.9 65176.9 72022.6 87601.1 78792.3 51994.6 55653.2 59740.7 18738.4 40268.8 53524.2 52348 50803.4 32411.9 +Q9BWS9 Q9BWS9;E9PI70;E9PIP0;E9PKF1;E9PPA0;E9PPH0;E9PRL3 CHID1_HUMAN CHID1 Chitinase domain-containing protein 1 77258.1 77812.8 109380 100032 114351 71769.5 154531 85317.5 116996 80394.1 134109 77828.4 102274 101669 167451 104309 123598 103823 98647.6 93818.5 +Q9BXI9 Q9BXI9 C1QT6_HUMAN C1QTNF6 Complement C1q tumor necrosis factor-related protein 6 161523 123320 254505 212746 194796 165791 224567 183275 196920 171462 218522 289779 167979 170645 182179 178166 203046 194891 137103 255190 +Q9BXJ9 Q6N069;Q9BXJ9;A0A0B4J1W3;A0A8V8TPI7;A0A8V8TQT7 NAA15_HUMAN NAA15 N-alpha-acetyltransferase 15, NatA auxiliary subunit 288069 261300 150671 264080 283944 300905 268307 255922 244880 261072 264227 263966 231086 258317 258452 254003 226628 279620 302146 151396 +Q9BXP5 Q9BXP5;H7C3A1 SRRT_HUMAN SRRT Serrate RNA effector molecule homolog 122153 121613 114591 157250 201646 243642 145208 198222 152797 177420 129961 225635 201332 231317 106275 180838 178787 139621 163624 69531.1 +Q9BXR0 Q9BXR0;K7EPI1 TGT_HUMAN QTRT1 Queuine tRNA-ribosyltransferase catalytic subunit 1 27584.3 15438.4 27149 32625.8 28145.7 +Q9BXS5 Q9BXS5;K7EJL1;K7EQX3 AP1M1_HUMAN AP1M1 AP-1 complex subunit mu-1 62700.8 74764.4 48845.9 75647.1 80341.1 85163.8 105031 107091 72154.1 69338.5 104000 61953.3 72173.9 65551.9 77263 75608.4 89677.7 81416.2 +Q9BY43 Q9BY43;E9PSI1;E9PQ80;E9PQI5 CHM4A_HUMAN CHMP4A Charged multivesicular body protein 4a 38006.5 52086.8 39462.2 37834.5 37604 35275.5 54769.9 52450.8 30825.2 46186 55335.5 17302.4 20909.6 10049.1 38644.6 +Q9BY44 Q9BY44;C9IZE1;H7C5Q3 EIF2A_HUMAN EIF2A Eukaryotic translation initiation factor 2A 233800 260485 801359 244740 316656 317030 329050 325679 251682 288765 284728 208406 196479 249942 366492 338019 200464 257342 383249 276203 +Q9BYQ2;Q9BYQ4 Q9BYQ2;Q9BYQ4;A0A140TA58 KRA92_HUMAN;KRA94_HUMAN KRTAP9-2;KRTAP9-4 Keratin-associated protein 9-4 124183 +Q9BYQ3 Q9BYP9;Q9BYQ0;Q9BYQ2;Q9BYQ3;Q9BYQ4;A8MTY7;A8MXZ3;A0A140TA58 KRA93_HUMAN KRTAP9-3 Keratin-associated protein 9-3 1.85451e+06 +Q9BYQ5 Q9BYQ5 SWISS-PROT:Q9BYQ5 SWISS-PROT:Q9BYQ5 2.53418e+06 +Q9BYQ6 Q9BYQ8;Q9BYQ6;A0A0G2JLF9 KR411_HUMAN KRTAP4-11 Keratin-associated protein 4-11 798405 +Q9BYQ7 Q9BYQ7 SWISS-PROT:Q9BYQ7 SWISS-PROT:Q9BYQ7 80498.7 +Q9BYQ9 Q9BYQ9 KRA48_HUMAN KRTAP4-8 Keratin-associated protein 4-8 338575 +Q9BYR0 Q9BYQ8;Q9BYR0;A0A0G2JLF9;A0A140TA64 KRA47_HUMAN KRTAP4-7 Keratin-associated protein 4-7 231957 +Q9BYR3 Q9BQ66;Q9BYR3;Q9BYR5 KRA44_HUMAN KRTAP4-4 Keratin-associated protein 4-4 1.11956e+06 +Q9BYR4 Q9BYR4 SWISS-PROT:Q9BYR4 SWISS-PROT:Q9BYR4 3.26181e+06 +Q9BYR6 Q9BYR6 KRA33_HUMAN KRTAP3-3 Keratin-associated protein 3-3 3.97124e+07 120268 258589 +Q9BYR7 Q9BYR7 KRA32_HUMAN KRTAP3-2 Keratin-associated protein 3-2 3.84379e+07 107421 +Q9BYR8 Q9BYR8 SWISS-PROT:Q9BYR8 SWISS-PROT:Q9BYR8 7.77056e+07 41599.2 57942.4 241773 +Q9BYS1 Q9BYS1 KRA15_HUMAN KRTAP1-5 Keratin-associated protein 1-5 2.96519e+06 +Q9BYT5;Q9BYU5 Q9BYT5;Q9BYU5;F5H1T9 KRA21_HUMAN;KRA22_HUMAN KRTAP2-1;KRTAP2-2 Keratin-associated protein 2-2 2.58106e+06 +Q9BYT8 Q9BYT8;E9PCB6;H0YAK4 NEUL_HUMAN NLN Neurolysin, mitochondrial 138835 106381 95122.9 67948 122408 87654.6 118451 85171 101693 138964 146962 99820.3 146106 116173 138442 141138 135817 130862 113830 120354 +Q9BYV8 Q9BYV8;A0A6Q8PEV5;A0A6Q8PF12;A0A6Q8PFB1;A0A6Q8PFB5;A0A6Q8PG60;A0A6Q8PH03;A0A7I2PK71;A0A7I2SYM4;C9J6R3;F8WAV3 CEP41_HUMAN CEP41 Centrosomal protein of 41 kDa 23160 +Q9BYW2 Q9BYW2;A0A1W2PPX9;A0A8I5KV15;C9JG86 SETD2_HUMAN SETD2 Histone-lysine N-methyltransferase SETD2 51139.8 38140.2 39106 11648.3 29558.3 60098.9 48410.9 36442 13152.7 +Q9BYX7 Q9BYX7;P0CG38;P0CG39 ACTBM_HUMAN POTEKP Putative beta-actin-like protein 3 549314 298185 375575 293751 544836 291107 189265 223799 2.78267e+06 828603 818788 732046 1.79671e+06 +Q9BZE4 Q9BZE4 GTPB4_HUMAN GTPBP4 GTP-binding protein 4 129916 121670 146083 173486 106804 88846.7 145475 98677 112453 87625.6 114981 100228 124608 143476 125209 121308 +Q9BZM5 Q9BZM5 ULBP2_HUMAN ULBP2 UL16-binding protein 2 66382.5 20132.6 54094.7 67306.4 58586.2 22749.3 8977.77 94765.8 30374.7 64838 20726.1 +Q9BZR6 Q9BZR6;H7C0V4;H7C215 RTN4R_HUMAN RTN4R Reticulon-4 receptor 25245.1 32966.2 59567.5 58669.8 35860.6 64790.7 11931.8 45711.1 51038.2 63459.4 43350.1 50820.7 56980.2 93992.7 63404.9 74077.7 69366.1 48926.9 67089.8 +Q9BZX2 Q9BZX2;Q9HA47;A0A2R8Y653 UCK2_HUMAN UCK2 Uridine-cytidine kinase 2 165877 167193 80397 105663 177042 163507 153116 176680 170746 156233 157909 128080 166974 146437 126698 133733 105912 118157 191638 92054 +Q9BZZ5 Q9BZZ5;E9PQK6 API5_HUMAN API5 Apoptosis inhibitor 5 237802 243102 119410 227111 312488 377183 272900 313509 284960 350585 259962 287079 330258 322964 227701 289176 249128 347378 273290 235945 +Q9C075 Q9C075 SWISS-PROT:Q9C075 SWISS-PROT:Q9C075 33806.3 48388.8 33778.2 57455.5 +Q9C0C2 Q9C0C2;A0A2R8Y5C4 TB182_HUMAN TNKS1BP1 182 kDa tankyrase-1-binding protein 16009.8 38323.2 60159.6 31547.2 56038.5 46594.3 55291.8 46207.5 58537.6 61199.9 29135.2 56395.5 34975.3 39502.5 51209.1 +Q9GZL7 Q9GZL7 WDR12_HUMAN WDR12 Ribosome biogenesis protein WDR12 102651 70867.4 64066.3 87376.3 58403.6 91642.7 85586.9 61107.5 44993.5 113094 52413.8 94385.1 91094.3 65096.3 22402.5 70801.8 54415.6 83964.1 74576.5 78045.4 +Q9GZM5 Q9GZM5;E7EQR8 YIPF3_HUMAN YIPF3 Protein YIPF3 455016 275049 730245 480865 557635 188430 590989 222164 634112 347210 531622 302242 707638 249371 322152 421492 910108 451944 432971 677220 +Q9GZM7 Q9GZM7;F6SDV2 TINAL_HUMAN TINAGL1 Tubulointerstitial nephritis antigen-like 225585 42919.5 170275 112823 310602 135414 317136 101320 264766 115024 297651 139238 263736 145152 351455 116806 197751 137386 169471 142397 +Q9GZP4 Q9GZP4 PITH1_HUMAN PITHD1 PITH domain-containing protein 1 83052.4 67421.5 112925 92763.9 69143.6 94279.3 56354.1 56559.5 73037.4 100960 57529.2 105416 65452.1 178279 108722 111040 99308.2 74896.4 80640 +Q9GZS3 Q9GZS3;H0YL19;H0YN81;H3BQA8 SKI8_HUMAN SKIC8 SKI8 subunit of superkiller complex protein 66379.9 49035.2 85042 100762 96043.8 56374.3 75076.5 51513.9 98940.3 96416.8 84990 68858.4 86056.5 91843.2 71833.6 51513.1 67041.9 80764 +Q9GZT8 Q9GZT8;E7EXA3 NIF3L_HUMAN NIF3L1 NIF3-like protein 1 501972 462699 348274 302730 432178 232205 322177 246556 593344 293343 426155 200572 594910 256868 612775 314969 625722 317585 555923 346569 +Q9GZU8 Q9GZU8;H3BMX9;H3BN22;H3BP64;H3BPF9;H3BPH9;H3BQQ6;H3BSF0;H3BSY6;H3BTI2;H3BTP8;H3BUL4;Q6P4H7 PIP30_HUMAN PSME3IP1 PSME3-interacting protein 101077 44810.4 47226.9 +Q9GZX9 Q9GZX9 TWSG1_HUMAN TWSG1 Twisted gastrulation protein homolog 1 25547.6 76918.3 242231 152860 64997.9 100149 44422.1 115912 81324.9 141366 54498 101317 72720.2 139666 80977.9 131269 374459 64662.7 43988.2 36454.4 +Q9GZZ1 Q9GZZ1;E7EQ69 NAA50_HUMAN NAA50 N-alpha-acetyltransferase 50 17212.7 24284.5 +Q9GZZ9 Q9GZZ9;A0A804HJ86;E7EQ61;E7EWE1 UBA5_HUMAN UBA5 Ubiquitin-like modifier-activating enzyme 5 42561.7 21350.6 30090.7 49944.3 +Q9H074 Q9H074;D6REB4 PAIP1_HUMAN PAIP1 Polyadenylate-binding protein-interacting protein 1 38245 70771.5 50472.9 37882 80641.4 29201 64525.3 122752 74486.5 85591.4 41481.4 75929.9 102486 61879.4 47567 +Q9H082 Q9H082;A0A494C0Z5 RB33B_HUMAN RAB33B Ras-related protein Rab-33B 140698 22542.4 35442.6 35634.8 59314.3 126618 42755.7 +Q9H0A0 Q9H0A0 NAT10_HUMAN NAT10 RNA cytidine acetyltransferase 21825.7 15067.7 23456.4 +Q9H0B6 Q9H0B6 KLC2_HUMAN KLC2 Kinesin light chain 2 22000.3 68666.1 27763.8 66961.5 74462.5 103156 50087.4 28621.3 110472 170220 60583.8 70879.1 +Q9H0D6 Q9H0D6 XRN2_HUMAN XRN2 5'-3' exoribonuclease 2 51458.7 40435.6 22100 10151.8 54683 45105.9 36614.8 14099.9 16750 23458.4 +Q9H0S4 Q9H0S4 DDX47_HUMAN DDX47 Probable ATP-dependent RNA helicase DDX47 220060 191329 201764 146374 151682 168490 138326 135263 177760 186713 166891 195063 149533 185476 +Q9H0W9 Q9H0W9;A0A087WT99;A0A1W2PRB8;E9PPB5;E9PQS1;E9PJU8 CK054_HUMAN C11orf54 Ester hydrolase C11orf54 49780.9 74408.3 82053.9 56508.4 59121.7 37238.2 47829.7 79109.6 62914.6 48160.2 39204.5 61907.4 114132 104979 106193 111010 71527.4 95617.9 +Q9H173 Q9H173;A0RZB6;D6REA1 SIL1_HUMAN SIL1 Nucleotide exchange factor SIL1 84830.2 146981 67093.5 144050 47026.5 156587 150061 46821.3 233624 28806 182045 52284.7 158712 32959.2 136052 43579.4 115770 +Q9H1E3 Q9H1E3 NUCKS_HUMAN NUCKS1 Nuclear ubiquitous casein and cyclin-dependent kinase substrate 1 184883 160738 168718 182445 170387 162839 238307 147145 176884 129845 269849 159967 191307 143805 167194 200544 84797.5 167620 193073 163955 +Q9H299 Q9H299;Q5T123 SH3L3_HUMAN SH3BGRL3 SH3 domain-binding glutamic acid-rich-like protein 3 84051.6 128485 103190 123251 107408 124004 65378.9 70069.1 119472 98274.1 58984.1 97773.2 98706.5 142683 133035 123716 143005 160294 +Q9H2G2 O94804;Q9H2G2 SLK_HUMAN SLK STE20-like serine/threonine-protein kinase 25902.5 29462.7 27700.2 37312.8 45307.4 27072.5 31970.2 19595.5 20944.8 40813.9 34922.5 38029.7 +Q9H2H8 Q9H2H8;C9K058;H7BZ14 PPIL3_HUMAN PPIL3 Peptidyl-prolyl cis-trans isomerase-like 3 14959.1 25896.9 23648.9 34481.1 20164 31183.3 +Q9H2P9 Q9H2P9 DPH5_HUMAN DPH5 Diphthine methyl ester synthase 23271 47248.3 +Q9H2U2 Q9H2U2;D6RGI1;D6RGV9;H0Y9D8;H0YAK2 IPYR2_HUMAN PPA2 Inorganic pyrophosphatase 2, mitochondrial 451232 476076 318544 515657 630771 549081 554228 612402 520176 515966 586668 533493 614531 466708 623315 462180 504673 407850 490678 412452 +Q9H3K6 Q9H3K6;A0A0B4J295;A0A499FJE1;H3BTW0;H3BVE0 BOLA2_HUMAN BOLA2B BolA-like protein 2 58005.7 145371 93790.4 66231.4 98537.6 65854 118560 141406 83631.4 78646.6 34457 99542.4 80221.1 130497 83255.1 92493.8 137456 61099.4 +Q9H3P7 Q9H3P7 GCP60_HUMAN ACBD3 Golgi resident protein GCP60 39855.7 96316.5 37348.3 63727.1 72114.7 103855 37359.5 89025 35174.5 69574.8 50791.7 51552.6 32193.7 67886.7 28244.6 51777.2 46261.7 61470.1 64896.5 +Q9H3U1 Q9H3U1;A0A1W2PNX8 UN45A_HUMAN UNC45A Protein unc-45 homolog A 43797.6 53528.7 46036.7 54874.5 45636.6 20663.4 54053.3 55938.8 24888.6 46497.4 26810.9 48030.8 29862.6 51312.5 52803.4 +Q9H444 Q9H444 CHM4B_HUMAN CHMP4B Charged multivesicular body protein 4b 71614.7 155595 117881 206236 171385 214951 189196 186335 154789 214284 115348 164120 151781 183061 168555 140226 104827 118232 156719 106936 +Q9H488 Q9H488 OFUT1_HUMAN POFUT1 GDP-fucose protein O-fucosyltransferase 1 40065.1 57028.6 37388.8 42106.1 130542 59825.6 31130.7 23492.5 56238.7 36771.6 134640 18292.1 47462 75068.6 29306.8 24512.3 41227.2 +Q9H497 Q9H497;Q5W0C9 TOR3A_HUMAN TOR3A Torsin-3A 55741.3 64591.8 84704.5 70179.8 174992 60828.9 188942 80634.5 143803 61641.1 179572 84314.8 191710 48759.5 156668 61432.7 190378 43912.2 +Q9H4A4 Q9H4A4;A6NKB8;C9JMZ3 AMPB_HUMAN RNPEP Aminopeptidase B 325594 603327 312163 568171 325521 499130 359434 422590 303835 574056 310886 446291 366710 614915 374696 657635 396868 635066 304515 673043 +Q9H4F8 Q9H4F8 SMOC1_HUMAN SMOC1 SPARC-related modular calcium-binding protein 1 2.10871e+06 806125 2.14272e+06 1.48638e+06 2.44555e+06 1.23085e+06 2.51719e+06 1.20674e+06 2.58931e+06 1.10692e+06 2.56823e+06 1.6501e+06 2.27657e+06 1.35703e+06 2.62359e+06 1.35416e+06 1.95291e+06 1.12777e+06 2.02763e+06 1.05483e+06 +Q9H552 Q9H552 TREMBL:Q9H552 TREMBL:Q9H552 278508 +Q9H5K3 Q9H5K3;A0A6Q8PFG8;A0A6Q8PFH3 SG196_HUMAN POMK Protein O-mannose kinase 29629.1 42812.2 24702.1 31859.4 63531.6 +Q9H6B4 Q9H6B4 CLMP_HUMAN CLMP CXADR-like membrane protein 49981.5 87789.2 58837 151338 62437.6 135030 63934.7 103407 184429 88710.8 154772 105686 96995.5 181061 107444 166498 63565.7 224398 +Q9H6F5 Q9H6F5 CCD86_HUMAN CCDC86 Coiled-coil domain-containing protein 86 50274 169506 24796.5 120542 87525.6 134357 121738 122467 52375.6 140455 63713.8 115552 33964.9 100350 105661 118877 31261.2 95600 57898.4 53302.2 +Q9H6S3 Q9H6S3;A0A3B3ISQ4;H0YD62;H0YF37 ES8L2_HUMAN EPS8L2 Epidermal growth factor receptor kinase substrate 8-like protein 2 85600.6 88939.5 45506.2 129635 140249 121934 117116 38430.3 88276.6 115959 127040 64452.9 116732 +Q9H6T3 Q9H6T3 RPAP3_HUMAN RPAP3 RNA polymerase II-associated protein 3 23953.9 20224.7 42015.8 46034.7 47244.3 62181.5 36367.6 27933.9 12571.6 33472.7 37399.6 42274.3 +Q9H6X2 Q9H6X2;A0A7P0T9B0;A0A7P0Z463;H0YC24 ANTR1_HUMAN ANTXR1 Anthrax toxin receptor 1 339937 146006 399777 181395 471393 247097 526762 191202 581480 248981 474302 193187 607844 269823 502865 148056 442072 254092 293205 264458 +Q9H6Z4 Q9H6Z4;B7Z7F3;K7EIJ4 RANB3_HUMAN RANBP3 Ran-binding protein 3 49306.6 35274.2 59272.1 72019.3 78818.4 47963.2 81211.5 47446 58383.1 45705.1 56976.3 102105 131051 58605.4 76200.8 +Q9H773 Q9H773 DCTP1_HUMAN DCTPP1 dCTP pyrophosphatase 1 100278 152537 68449.7 91450.3 114004 130287 125628 105504 103255 160735 156946 88548.9 79740.6 124540 163691 179575 97993.6 71762 161899 67077.6 +Q9H910 Q9H910;A6NGP5;H3BMT0;H3BMV3;H3BU16;H3BTV5 JUPI2_HUMAN JPT2 Jupiter microtubule associated homolog 2 143626 105180 101351 129417 111267 73121.2 97838 95878.6 94222.7 47975.4 52544.7 105812 87008.2 +Q9H9Q2 Q9H9Q2;J3KQ34;J3KQ41 CSN7B_HUMAN COPS7B COP9 signalosome complex subunit 7b 53463.1 4746.82 26662.9 36043.7 34551.9 +Q9HAT2 Q9HAT2 SIAE_HUMAN SIAE Sialate O-acetylesterase 449172 351024 959140 560941 635511 481984 793655 379150 841478 642876 682189 460791 875395 570047 824359 600278 892423 747459 447890 751589 +Q9HAV0 Q9HAV0;A0A6Q8PFE0;A0A6Q8PFV8;A0A6Q8PFW8;A0A6Q8PG88;A0A7I2S2S9;H7C5J5 GBB4_HUMAN GNB4 Guanine nucleotide-binding protein subunit beta-4 328173 192420 322106 244138 258058 167364 268805 165578 279350 152226 337350 247799 348816 191048 265169 201906 332801 218186 355961 309640 +Q9HAV4 Q9HAV4 XPO5_HUMAN XPO5 Exportin-5 118966 86034.4 47972.8 59200 92934.5 57096.2 77619.8 32024.2 66352.6 40466.6 75923.1 65154 52856.4 58875.5 63317.3 100400 +Q9HB07 Q9HB07;F8VQQ3;F8VR84 MYG1_HUMAN MYG1 MYG1 exonuclease 117281 149396 117018 114815 179977 186346 158268 176518 171463 206829 162546 154374 206092 177950 162802 157513 162294 124893 127259 142657 +Q9HB40 Q9HB40;I3L4Z3;I3L506 RISC_HUMAN SCPEP1 Retinoid-inducible serine carboxypeptidase 236616 119067 94510 161200 264092 196310 155134 220142 291714 233798 270707 292729 249060 261956 121954 150858 239000 254133 215485 160559 +Q9HB63 Q9HB63;A0A6I8PS14;F8W0I7 NET4_HUMAN NTN4 Netrin-4 150788 81608.1 129239 194230 206459 179738 207044 243299 187058 170307 216070 229310 143342 179394 187334 224741 149178 247909 164059 232030 +Q9HB71 Q9HB71 CYBP_HUMAN CACYBP Calcyclin-binding protein 494091 707944 386178 489762 574570 597491 558926 582566 369245 634000 567401 604687 525366 545023 635350 705760 514708 447820 700522 680599 +Q9HC35 Q9HC35;B5MBZ0 EMAL4_HUMAN EML4 Echinoderm microtubule-associated protein-like 4 99623.6 74392 87562.8 97079 84199.1 71565.5 82009.3 109791 74006.2 94958.3 69543.9 92526 80391.6 132920 132385 +Q9HC38 Q9HC38 GLOD4_HUMAN GLOD4 Glyoxalase domain-containing protein 4 774997 876028 897196 764826 759302 753120 930013 604306 750955 877705 758451 608457 824575 879457 993748 952008 975103 1.05702e+06 718302 1.07806e+06 +Q9HCE1 Q9HCE1;A0A8I5KXC3;A0A8I5KZ07;Q5JR04 MOV10_HUMAN MOV10 Helicase MOV-10 46228.1 39558.4 46481.9 27597.4 25514.7 40070.9 34499.6 37390.5 48072.6 28846.6 60323.5 52188.3 +Q9HCN8 Q9HCN8 SDF2L_HUMAN SDF2L1 Stromal cell-derived factor 2-like protein 1 38785.5 15912.1 43898.1 6727.85 54519.8 23809.4 20674.7 19888.1 26986.7 23554 +Q9HCU4 Q9HCU4 CELR2_HUMAN CELSR2 Cadherin EGF LAG seven-pass G-type receptor 2 153801 84907.4 167728 164234 177629 168588 161437 174608 203013 175398 179791 255175 216337 214083 166642 182596 154716 196239 100442 140487 +Q9HCY8 Q9HCY8 S10AE_HUMAN S100A14 Protein S100-A14 95827.9 76734.7 18597.6 +Q9HD42 Q9HD42;A0A6Q8PF35;A0A6Q8PFF9;A0A6Q8PG85;A0A6Q8PGM8;F5H875;F8VUA2;F8VVT7 CHM1A_HUMAN CHMP1A Charged multivesicular body protein 1a 57876.9 67128.7 84922.1 111660 75104.9 86877.3 95042.2 87556.6 83344 90926.3 79832.2 68111.6 108664 68791.3 42098.8 106783 +Q9N2I2 Q9N2I2 SWISS-PROT:Q9N2I2 SWISS-PROT:Q9N2I2 414920 368592 783579 496020 463471 404144 607926 387486 556126 430572 662594 397696 471230 436660 427073 524248 459513 324316 335755 390930 +Q9NP58 Q9NP58 ABCB6_HUMAN ABCB6 ATP-binding cassette sub-family B member 6 819756 1.25944e+06 1.18913e+06 1.52517e+06 983154 624212 +Q9NP79 Q9NP79;A0A087WY55;Q5TGM0 VTA1_HUMAN VTA1 Vacuolar protein sorting-associated protein VTA1 homolog 167435 124800 56621.1 116887 162774 159639 115662 127882 173639 119414 155817 106589 170492 161268 179529 137825 95039.8 149100 137420 101081 +Q9NPD8 Q9NPD8;A0A8V8TN93;A0A8V8TNE2 UBE2T_HUMAN UBE2T Ubiquitin-conjugating enzyme E2 T 78924.5 68781.8 76682.8 55252.1 65494.2 53853.8 36397.7 57523.4 63127.4 70815.5 63391 72124.7 62770 120265 91075.5 49544.5 +Q9NPF0 Q9NPF0;M0R100 CD320_HUMAN CD320 CD320 antigen 266741 145548 355073 246703 121942 324270 211558 167666 250697 248800 292324 216445 285692 298105 335837 352555 260826 +Q9NPH2 Q9NPH2;J3QS51 INO1_HUMAN ISYNA1 Inositol-3-phosphate synthase 1 396317 624189 449204 544997 440777 411063 480355 532596 599317 335425 +Q9NPH3 Q9NPH3;C9J1D9 IL1AP_HUMAN IL1RAP Interleukin-1 receptor accessory protein 112137 192379 147175 304181 149574 251930 200029 253151 114224 293986 164799 315377 118461 305026 177415 371127 136841 344712 130026 360099 +Q9NQ29 Q9NQ29;A8MYV2;B8ZZ09;B8ZZ10 LUC7L_HUMAN LUC7L Putative RNA-binding protein Luc7-like 1 13269.7 10215.6 14053.7 23232 +Q9NQ55 Q9NQ55;A0A0B4J1V8;A0A0A6YYI3;A8MV53;H7C446 SSF1_HUMAN PPAN Suppressor of SWI4 1 homolog 47162 45754 13130.9 46335.8 88193 13505.1 68657.7 61478 53444.6 30891.7 24067.2 +Q9NQ88 Q9NQ88 TIGAR_HUMAN TIGAR Fructose-2,6-bisphosphatase TIGAR 37314.7 18492.7 24310.2 33583.9 +Q9NQC3 Q9NQC3 RTN4_HUMAN RTN4 Reticulon-4 435711 300565 402168 360734 371859 351088 361251 299542 361180 338297 334983 319238 273621 308932 258201 248997 400422 298979 295252 223224 +Q9NQG5 Q9NQG5;A2A2M0 RPR1B_HUMAN RPRD1B Regulation of nuclear pre-mRNA domain-containing protein 1B 19967.3 53534 44013.1 67793.9 55154.9 48889.6 39946.7 42644.9 45527.7 27046.7 32710.7 34175.8 53915.4 +Q9NQH7 Q9NQH7 XPP3_HUMAN XPNPEP3 Xaa-Pro aminopeptidase 3 2.94502e+06 3.5602e+06 2.48214e+06 3.20522e+06 3.99408e+06 3.40596e+06 2.2891e+06 1.87963e+06 +Q9NQP4 Q9NQP4;E9PQY2 PFD4_HUMAN PFDN4 Prefoldin subunit 4 135599 151228 154838 135989 231188 181661 171624 81955.6 122552 251312 175776 +Q9NQR4 Q9NQR4;F8WF70;H7C579 NIT2_HUMAN NIT2 Omega-amidase NIT2 43272.5 49688.9 47161.3 58430.8 77871.1 52568.5 62913.6 31801.7 45829.1 42812.8 36759.9 57306.9 32062.8 +Q9NQT4 Q9NQT4 EXOS5_HUMAN EXOSC5 Exosome complex component RRP46 32097.1 92254.9 85171.8 95307 59778.7 54238.1 79226.5 60834 +Q9NQW7 Q9NQW7;Q5T6H7 XPP1_HUMAN XPNPEP1 Xaa-Pro aminopeptidase 1 55288.6 43284.1 22296.4 52090.9 52667.5 51488.8 49658.5 45958.1 40580.5 42485.6 31407.4 25424.4 34051.7 81841 35508.9 44046 52867.2 36452.6 +Q9NR30 Q9NR30;A0A8I5KNN2;A0A8I5KNP3;A0A8I5KYZ4 DDX21_HUMAN DDX21 Nucleolar RNA helicase 2 337421 445437 562357 664801 305335 482850 316198 418532 289145 508792 344565 476121 325570 407978 226827 442300 252103 402993 305274 325575 +Q9NR45 Q9NR45;Q5TBR0 SIAS_HUMAN NANS Sialic acid synthase 68701 95908.4 30205.3 66418 109624 128634 91119 119912 92432.9 94344.8 112267 98727 80704.1 114094 109761 136187 64469.4 98794 85254.8 59463.4 +Q9NR50 Q9NR50;H0Y580 EI2BG_HUMAN EIF2B3 Translation initiation factor eIF-2B subunit gamma 35246.8 35136.6 21125.4 38788.2 30546 30943.2 31284.8 25401.9 18307.5 36817.7 20092.2 37651 +Q9NRG0 Q9NRG0;E5RGS9 CHRC1_HUMAN CHRAC1 Chromatin accessibility complex protein 1 28791.8 38263.7 63404.2 65452.9 51484 51788 49028.7 47021.9 58472.2 68709.4 72270.6 85565.8 +Q9NS15 Q9NS15;A0A8I5KYV1;E9PKW1;H0YC99 LTBP3_HUMAN LTBP3 Latent-transforming growth factor beta-binding protein 3 127179 50481.5 137034 137165 237530 148094 181566 168486 277913 118119 215552 195908 209007 166837 216741 144067 164680 165625 126943 155444 +Q9NSB4 Q9NSB4 SWISS-PROT:Q9NSB4 SWISS-PROT:Q9NSB4 1.37063e+06 365864 +Q9NSD9 Q9NSD9 SYFB_HUMAN FARSB Phenylalanine--tRNA ligase beta subunit 212521 133906 176731 134586 328528 234684 251699 240786 269046 167344 283636 182525 263661 193993 275867 176054 189897 143635 389858 78913.4 +Q9NTJ3 Q9NTJ3;E9PD53;C9JR83 SMC4_HUMAN SMC4 Structural maintenance of chromosomes protein 4 101975 128004 34852.8 132091 105749 160468 93838.3 140290 108234 138574 110667 120574 54228.1 148228 145616 113222 46990.7 107087 172910 98605.5 +Q9NTK5 Q9NTK5;J3KQ32 OLA1_HUMAN OLA1 Obg-like ATPase 1 298398 369035 161317 268401 324437 284055 279943 254790 306158 326714 314159 263808 240730 315577 205814 345762 344887 304003 382196 265815 +Q9NTM9 Q9NTM9 CUTC_HUMAN CUTC Copper homeostasis protein cutC homolog 447052 408486 862054 362543 488362 237294 597680 241811 470323 245196 492669 215844 437011 284378 321338 400487 583509 275616 481229 381341 +Q9NTX5 Q9NTX5;H0Y5L2 ECHD1_HUMAN ECHDC1 Ethylmalonyl-CoA decarboxylase 175864 32833.4 +Q9NTZ6 Q9NTZ6 RBM12_HUMAN RBM12 RNA-binding protein 12 55439.6 41749.9 47102.7 55541.8 55805.8 50535.3 59989.1 34855.6 39836.9 +Q9NUP9 Q9NUP9;G3V1D4 LIN7C_HUMAN LIN7C Protein lin-7 homolog C 81489.6 88832.8 131828 137646 108189 66446.2 113678 83819.3 86192.5 99995.3 93027.7 144288 105807 120433 104086 91940.2 93999.1 54683.8 +Q9NUQ3 Q9NUQ3 TXLNG_HUMAN TXLNG Gamma-taxilin 54600.7 135232 40499.5 105565 56227.4 120315 45953.4 124034 108625 46534.8 96111.6 77638.9 114014 81646.6 119148 41484.1 101168 +Q9NUQ9 Q9NUQ9;Q9H0Q0;A0A8V8TMP7 CYRIB_HUMAN CYRIB CYFIP-related Rac1 interactor B 190275 165474 150485 183868 207578 158005 242965 131406 152570 141628 225518 141382 151810 147296 187549 222806 208356 218049 251778 123811 +Q9NUU7;Q9UMR2 Q9NUU7;Q9UMR2;F6QDS0;H3BQK0;I3L0H8;I3L352 DD19A_HUMAN;DD19B_HUMAN DDX19A;DDX19B ATP-dependent RNA helicase DDX19A 31440.3 113463 57737.6 79085.7 157544 72358.1 143546 21836.5 63470.6 69035.1 64616.1 83548.3 113695 107427 102808 45281.4 75097.8 138702 +Q9NVA2 Q14141;Q92599;Q9NVA2;A0A087X142;A6NFQ9;A6NMH6;B1AMS2;D6RER5;D6RGI3;F8W8I8;C9JV02;D6RDU5 SEP11_HUMAN SEPTIN11 Septin-11 115495 37864.4 160994 53693.9 101646 52823 99419.9 43205.5 101648 58074.7 114439 57934.7 146008 54314.4 76712.4 137170 +Q9NVM4 Q9NVM4;A0A8I5KPT4;A0A8I5KXS9;A0A8I5KYD6;A0A8I5KZ92;H3BRD3;A0A8I5KZ05;A0A8I5QKZ3 ANM7_HUMAN PRMT7 Protein arginine N-methyltransferase 7 14254.1 9330.67 28163.4 40571.7 21720.3 13196.3 +Q9NVS9 Q9NVS9;A0A286YFL3;J3QQV6;J3QQZ9 PNPO_HUMAN PNPO Pyridoxine-5'-phosphate oxidase 48448.4 88889.4 49448.6 64354 65169.5 35927 40467.1 75219.6 17994.7 50205.7 34099.9 23005.7 88241.3 32221.4 45503.7 61900.5 +Q9NVX2 Q9NVX2 NLE1_HUMAN NLE1 Notchless protein homolog 1 29776.4 27670.3 41712.7 37472.7 27196 43233.6 39921.2 44670.9 38190.6 40785.5 39811.1 +Q9NWV4 Q9NWV4 CZIB_HUMAN CZIB CXXC motif containing zinc binding protein 116390 59303.5 73401.9 69937.6 73521.6 43879.8 71356.8 164887 45919.6 51678.5 99182.3 76520.2 64038.1 98661.5 128587 61303.6 95807.1 51641.6 +Q9NX24 Q9NX24;D6RC52;D6RCB9;J3QSY4 NHP2_HUMAN NHP2 H/ACA ribonucleoprotein complex subunit 2 51004 34764.7 27556.5 59738.5 41609.5 41591.4 19609.7 56008.9 29841.9 25881.8 18204.6 18991.9 +Q9NX46 Q9NX46 ADPRS_HUMAN ADPRS ADP-ribosylhydrolase ARH3 62395.2 87329.7 68415.4 61625.8 50897.1 61605.7 69928.2 42562.7 53433 56310.7 57527.3 54132.5 65809.3 82685.3 111321 87376.9 75427.2 52738 84798 70751.3 +Q9NXG2 Q9NXG2 THUM1_HUMAN THUMPD1 THUMP domain-containing protein 1 59851.2 27151.1 17742.4 33239.6 26472.4 32711.7 44681 44585.3 49400.7 31083.4 41242.1 +Q9NXH9 Q9NXH9 TRM1_HUMAN TRMT1 tRNA (guanine(26)-N(2))-dimethyltransferase 58752.9 73141.4 44982.4 71741.6 89500 90982.1 75859.1 89443.1 83164.2 79537.8 76898.1 78314.9 78270.9 79316.5 87759.8 67957.6 15318.2 81087 95385.9 84438.9 +Q9NXR7 Q9NXR7;A0A075B7D2;C9J2G0;F8W733 BABA2_HUMAN BABAM2 BRISC and BRCA1-A complex member 2 24080.4 34150.4 +Q9NY12 Q9NY12 GAR1_HUMAN GAR1 H/ACA ribonucleoprotein complex subunit 1 96650.2 69847.2 63780.8 109307 61142.8 41854 76484.1 65585.8 52085.8 100799 79903.3 58685.9 63737.2 +Q9NY27 Q9NY27;C9IZ04;F8WDK3 PP4R2_HUMAN PPP4R2 Serine/threonine-protein phosphatase 4 regulatory subunit 2 13649.5 27177.7 46705.6 32513.7 47744.8 33998.3 33881.3 34264.7 20043.1 13969.2 34715.6 18759.7 27228.5 20727.8 +Q9NY33 Q9NY33;G3V180;G3V1D3;E9PKK8;E9PNX5;E9PPK9;E9PQ14 DPP3_HUMAN DPP3 Dipeptidyl peptidase 3 393344 479426 387097 371655 354515 356982 402587 254083 321627 424767 349771 289739 361109 353904 462035 448242 428971 426661 378876 390623 +Q9NYF8 Q9NYF8;A0A1W2PQ43;E9PJA7;E9PK09;E9PK91;E9PKI6;E9PQN2;H0YF00 BCLF1_HUMAN BCLAF1 Bcl-2-associated transcription factor 1 22940 24584.5 66240.2 42222.8 53229.3 45781.4 56681.9 42005.1 37243.6 47561.7 54642.1 66890.4 43270.3 40663.2 +Q9NYL9 Q9NYL9;H0YKU1;H0YNJ8 TMOD3_HUMAN TMOD3 Tropomodulin-3 45663.7 127332 84880.9 140548 72448.2 122269 82635.9 166573 154335 73335.3 127572 81114 115529 119322 93303.2 91508.8 74057.9 50003 +Q9NYU2 Q9NYU2 UGGG1_HUMAN UGGT1 UDP-glucose:glycoprotein glucosyltransferase 1 200444 88627.3 152718 187236 125536 177145 150153 169726 171938 217240 190311 200204 164015 129594 176582 151233 183625 133077 +Q9NZ08 Q9NZ08 ERAP1_HUMAN ERAP1 Endoplasmic reticulum aminopeptidase 1 190665 157253 402359 248885 224275 168874 316315 168041 254521 210674 273309 184054 207398 201885 227377 260687 300747 252860 164894 242926 +Q9NZI8 Q9NZI8 IF2B1_HUMAN IGF2BP1 Insulin-like growth factor 2 mRNA-binding protein 1 14227.9 20521.9 37519.9 +Q9NZL9 Q9NZL9;A0A8Q3SIE5;A0A8Q3WK74;A0A8Q3WK80;A0A8Q3WK84;A0A8Q3WKR9;A0A8Q3WLC0;A0A8Q3WLP1;E5RJR3 MAT2B_HUMAN MAT2B Methionine adenosyltransferase 2 subunit beta 306298 477866 336144 463353 455876 507273 398038 408215 434886 545942 433660 425799 365816 553660 566396 620894 407617 604195 412684 511381 +Q9NZM1 Q9NZM1 MYOF_HUMAN MYOF Myoferlin 29477.9 38973.6 51166.7 51909.5 44807.9 43410.1 45805.9 78462.7 65010.8 47569.4 32376 +Q9NZP8 Q9NZP8;F5GWF3;F5H2Z5;H0YF87;H0YFJ9 C1RL_HUMAN C1RL Complement C1r subcomponent-like protein 15851 34927 50941.2 76778.3 27575.8 69907 87809.6 20230.2 84370.2 42000.9 82075.6 33435.5 96392.3 81534.7 15480 88041.2 +Q9NZT1 Q9NZT1 CALL5_HUMAN CALML5 Calmodulin-like protein 5 282100 42628.6 123234 +Q9NZT2 Q9NZT2;A0A0A0MRN5 OGFR_HUMAN OGFR Opioid growth factor receptor 63658.3 71370 87849.2 63815.5 70511.9 73040.5 62607.2 70679.4 34096.7 78077.8 75267.5 61608.1 43235.1 57367.4 81518.8 51864.7 41066.7 69779.1 79441.8 +Q9NZV1 Q9NZV1;H7C2T6 CRIM1_HUMAN CRIM1 Cysteine-rich motor neuron 1 protein 167634 86793.4 183689 155575 179011 140190 174217 132535 198897 239463 168902 173961 196666 186585 202658 109553 119455 118077 109869 96468.6 +Q9P0V9 Q9P0V9;E7EW69;E7EX04;B5ME97;F5GYV2;F5H1F2 SEP10_HUMAN SEPTIN10 Septin-10 33304.2 17177 49867.8 52531.5 50626.3 29182.7 52809.2 21846.1 81087.5 35074.7 63897.7 46465.7 43285.1 44321.5 +Q9P258 Q9P258 RCC2_HUMAN RCC2 Protein RCC2 345840 319262 295392 328564 299361 318037 358144 306166 227435 242568 360567 313008 227874 287423 385013 380250 205809 241658 384385 245662 +Q9P287 Q9P287 BCCIP_HUMAN BCCIP BRCA2 and CDKN1A-interacting protein 25538.6 43955.7 33414.2 15773 35640.9 36561.3 21368.5 18328.3 +Q9P289 Q9P289;B4E0Y9;Q8NBY1 STK26_HUMAN STK26 Serine/threonine-protein kinase 26 238538 251724 166071 391512 298139 368058 299185 320951 280069 293814 241248 306541 306563 330247 233810 270135 322889 397781 130393 +Q9P2B2 Q9P2B2 FPRP_HUMAN PTGFRN Prostaglandin F2 receptor negative regulator 156786 48257.2 176448 99000.8 192165 72525.4 207744 92753.9 196741 104972 252751 119630 212074 107318 181883 130687 166304 138884 159848 85667.9 +Q9P2E9 Q9P2E9;A2A2S5 RRBP1_HUMAN RRBP1 Ribosome-binding protein 1 101315 319660 181943 477756 119141 341592 104343 293918 98280.3 335531 102235 306326 88223.3 303888 96935.5 296099 137657 279993 100877 168601 +Q9P2J5 Q9P2J5;A0A6I8PIP7;A0A6I8PIT3;A0A6I8PIV1;A0A6I8PL42;A0A6I8PLB3;A0A6I8PLB8;A0A6I8PRS0;A0A6I8PS05;B4DER1 SYLC_HUMAN LARS1 Leucine--tRNA ligase, cytoplasmic 175841 132752 109572 146550 146815 144673 138431 183424 97712.5 140477 154664 133794 112461 134845 151758 133904 136057 109179 213445 71763.3 +Q9P2R7 Q9P2R7;A0A2R8Y6Y7;A0A2R8YDQ9;A0A0U1RQL1 SUCB1_HUMAN SUCLA2 Succinate--CoA ligase [ADP-forming] subunit beta, mitochondrial 707939 368424 270123 270851 329050 298122 293142 226681 378170 294089 308886 441849 351966 191337 297164 594088 318753 380308 467325 +Q9P2T1 Q9P2T1;H0YLV5;H0YMB3;H0YNJ6;H0YK13;H0YK71;H0YLB8 GMPR2_HUMAN GMPR2 GMP reductase 2 102451 123662 111317 97044.2 91062.1 79026.6 131002 106348 68950 96512.9 100604 87814.3 112128 88979.7 136891 130115 80662.9 116775 108153 83452.9 +Q9TRI1 Q9TRI1 TREMBL:Q9TRI1 TREMBL:Q9TRI1 306094 354579 364730 407488 473764 374487 344852 368494 626224 358465 394189 376754 688601 467633 415921 315119 417442 340058 204655 292318 +Q9UBB4 Q9UBB4;A0A1W2PQD2 ATX10_HUMAN ATXN10 Ataxin-10 136097 195391 53705.5 121916 103056 182014 127237 210515 168154 161874 137728 74314.4 163407 100251 131590 94172.8 143917 157322 +Q9UBE0 Q9UBE0;A0A590UK93;A0A590UKA0;A0A590UKC5;B3KNJ4;M0QX65;M0QZS6 SAE1_HUMAN SAE1 SUMO-activating enzyme subunit 1 97637.9 160489 89720.7 81341.8 130213 168159 79983.5 165130 170525 133929 131733 136376 95935.2 129494 58235.4 123961 84730.8 111006 94555.9 61621.1 +Q9UBF2 Q9UBF2 COPG2_HUMAN COPG2 Coatomer subunit gamma-2 78748.6 63764.1 72506.8 76012.1 62907.2 77074.4 +Q9UBK8 Q9UBK8 MTRR_HUMAN MTRR Methionine synthase reductase 22597 44257.4 20006.6 35567.2 26457.2 42355.8 24845.3 28783.7 23456.5 13229.2 32034.9 39159.4 +Q9UBQ0 Q9UBQ0;F8VXU5 VPS29_HUMAN VPS29 Vacuolar protein sorting-associated protein 29 300918 239331 119258 190642 187102 192771 188464 228873 127703 220287 211528 248350 148748 212842 180978 211675 119902 154087 228695 119717 +Q9UBQ5 Q9UBQ5;K7ERF1;K7ES31 EIF3K_HUMAN EIF3K Eukaryotic translation initiation factor 3 subunit K 286284 295762 348823 323336 329931 330451 308514 335385 378038 328024 330501 358463 337041 255277 329234 400371 390111 339637 388442 +Q9UBQ6 Q9UBQ6 EXTL2_HUMAN EXTL2 Exostosin-like 2 182717 170361 345184 230380 279968 225884 358611 229331 400368 226796 342664 235754 407463 247586 185548 297469 359205 252982 200567 322605 +Q9UBQ7 Q9UBQ7;U3KQ56 GRHPR_HUMAN GRHPR Glyoxylate reductase/hydroxypyruvate reductase 68735.7 59089 84405.7 77809 71432.7 78081.8 66599.4 70664.3 65546.9 66844.2 30432.6 +Q9UBR2 Q9UBR2;A0A7P0T8I6;A0A7P0T989;A0A7P0T9U4;A0A7P0T8X2;A0A7P0T900;A0A7P0T926;A0A7P0T9G9;A0A7P0T9U1;A0A7P0TA25;A0A7P0TAD4;A0A7P0TAT6;A0A7P0TB41;A0A7P0TBB5;A0A7P0TBM7;A0A7P0Z469;A0A7P0Z4L7;A0A7P0Z4R9 CATZ_HUMAN CTSZ Cathepsin Z 1.948e+07 5.75674e+06 1.987e+07 7.73277e+06 1.70176e+07 6.9956e+06 2.11423e+07 5.0011e+06 1.68266e+07 9.00266e+06 2.07604e+07 9.41415e+06 1.50531e+07 8.19277e+06 2.76954e+07 9.13478e+06 2.14483e+07 8.5776e+06 1.34221e+07 9.51875e+06 +Q9UBS4 Q9UBS4 DJB11_HUMAN DNAJB11 DnaJ homolog subfamily B member 11 238319 232204 234085 446857 401897 184699 352645 198659 463852 176311 403000 254721 485934 137785 338550 209122 521005 454516 313373 +Q9UBT2 Q9UBT2;K7EPL2 SAE2_HUMAN UBA2 SUMO-activating enzyme subunit 2 199963 212295 176664 205114 185238 217515 156804 239145 159416 177782 209052 219525 140801 227904 183306 219635 136489 215136 196874 101393 +Q9UBW8 Q9UBW8;F5GYF7;F5H248;F5H7C6 CSN7A_HUMAN COPS7A COP9 signalosome complex subunit 7a 81977.4 44001.6 85636 60601 39544.6 74229.5 34764.3 34636.5 45330.9 52399.7 39223.8 28775.5 27117.8 62857 +Q9UBX1 Q9UBX1;A0A024R5G9;A0A7I2V5F8;H0YD65;A0A7I2V2K3;A0A7I2V313;A0A7I2V3E7;A0A7I2V3L9;A0A7I2V3M3;A0A7I2V3V9;A0A7I2V3X1;A0A7I2V411;A0A7I2V4A8;A0A7I2V5B9;A0A7I2V5Q1;A0A7I2YQB3;A0A7I2YQH8;A0A7I2YQW3;H0YE42 CATF_HUMAN CTSF Cathepsin F 117841 91877.5 183679 64231.9 114695 90194.8 199906 50737.3 176878 94881.6 215677 78021.6 106468 74542.2 183618 60399.8 121690 44550.6 +Q9UBX5 Q9UBX5;G3V4U0;G3XA98 FBLN5_HUMAN FBLN5 Fibulin-5 31077.5 49502.4 42127.4 74797 20817.6 26914.8 45128.1 65317.8 42228.7 32796.2 54813.2 46674.4 27780.6 +Q9UDY2 Q9UDY2;A0A1B0GTW1;A0A2R8YDH4;A0A3B3IRI3;A0A3B3IRV6;A0A3B3IS03;A0A3B3ISF1;A0A3B3ISZ5;A0A3B3IT83;A0A3B3ITE1;A0A3B3IU51 ZO2_HUMAN TJP2 Tight junction protein ZO-2 19754.7 38648.7 31316 34729.4 29356.8 24244.6 35808.3 6736.09 47576.9 37051.2 62219.7 36080.7 +Q9UEE9 Q9UEE9 CFDP1_HUMAN CFDP1 Craniofacial development protein 1 80404.1 71145.9 67566 75901.4 58653.7 53423.5 106812 85304.1 62813.6 76582 69963.8 51405.7 31545.8 122010 +Q9UGI8 Q9UGI8;F8W7T0 TES_HUMAN TES Testin 22201 25880.2 32447.3 54036.6 72053.8 14058.8 32945.6 66599.8 53658 29998 23725.3 105770 42978.9 41859.3 37559.5 16000.2 40337.9 +Q9UHB6 Q9UHB6;F8VQE1;F8VS07 LIMA1_HUMAN LIMA1 LIM domain and actin-binding protein 1 218348 214772 409912 287635 521862 368756 408497 327187 585737 361141 334833 370690 557730 364153 355820 260952 387365 330738 334251 377298 +Q9UHD1 Q9UHD1;E9PHZ2 CHRD1_HUMAN CHORDC1 Cysteine and histidine-rich domain-containing protein 1 339917 502111 147341 385744 425938 402960 389333 511665 262922 409323 397014 403506 260163 336094 525367 483701 219303 273150 516864 207707 +Q9UHD8 Q9UHD8;K7ER14 SEPT9_HUMAN SEPTIN9 Septin-9 178022 207087 124666 188700 235551 235808 181560 266325 194359 212776 197079 209375 217864 253720 207310 242109 158795 240564 276116 127895 +Q9UHI8 Q9UHI8;A0A7I2V5J1;A0A7I2YQL5;E5RI60 ATS1_HUMAN ADAMTS1 A disintegrin and metalloproteinase with thrombospondin motifs 1 38348.4 45486.5 13425.3 60835.4 84559.7 56817.3 15173.1 94881.5 76389.4 13889.6 61452.2 9019.54 +Q9UHL4 Q9UHL4;R4GN05;R4GNE8 DPP2_HUMAN DPP7 Dipeptidyl peptidase 2 1.08541e+06 729139 1.59436e+06 1.0008e+06 1.42793e+06 974202 1.56721e+06 851642 1.57648e+06 1.1751e+06 1.29388e+06 926343 1.45779e+06 1.13191e+06 1.50419e+06 1.09712e+06 1.71327e+06 1.23165e+06 877397 1.20746e+06 +Q9UHV9 Q9UHV9 PFD2_HUMAN PFDN2 Prefoldin subunit 2 154546 227563 291439 236060 292597 210603 286859 282571 318312 242543 286311 200234 275540 208777 284798 258476 116518 138882 246686 141297 +Q9UHX1 Q9UHX1;A0A0J9YVP6;A0A0J9YXJ8;A0A0J9YXX5;A0A0J9YYL3;E9PQ56;H0YEM1;A0A0J9YVR6;A0A0J9YWM1;H0YCP8 PUF60_HUMAN PUF60 Poly(U)-binding-splicing factor PUF60 187907 211127 253051 279622 446736 230013 543426 223769 284989 253600 376439 261597 344680 291278 309865 187804 329895 363875 273722 +Q9UHY7 Q9UHY7;D6RA00 ENOPH_HUMAN ENOPH1 Enolase-phosphatase E1 78280.5 111827 105075 72619.3 117033 89584.7 114976 75428.6 135699 123049 111101 90132.7 102328 117891 143971 121825 158954 120722 118682 157736 +Q9UI10 Q9UI10;E7ERK9 EI2BD_HUMAN EIF2B4 Translation initiation factor eIF-2B subunit delta 25853.6 26132.1 16365 37458 27610.2 21535.6 14116.9 12703.7 24207 35632.5 21499.2 +Q9UI30 Q9UI30 TR112_HUMAN TRMT112 Multifunctional methyltransferase subunit TRM112-like protein 87794.8 84471.5 113615 149050 119946 115768 113566 123802 96411.6 129253 92966.9 109851 47782.9 106985 139284 80679.4 51476.1 41228.3 113999 69829 +Q9UI42 Q9UI42;B7Z5J4 CBPA4_HUMAN CPA4 Carboxypeptidase A4 6.11069e+06 3.86835e+06 6.68037e+06 6.10505e+06 6.49627e+06 5.29192e+06 8.26704e+06 4.11131e+06 8.17579e+06 7.00588e+06 7.65805e+06 6.3742e+06 7.4683e+06 6.39238e+06 6.57344e+06 7.57161e+06 8.67313e+06 7.53969e+06 6.06079e+06 7.5163e+06 +Q9UIA9 Q9UIA9;E7ESC6;H0YBE1 XPO7_HUMAN XPO7 Exportin-7 48870.9 57598.5 61748.5 68322.6 61535.6 36606.4 54247.4 52160.2 62350.3 54770 58683.9 48346.5 66066.2 62962.1 +Q9UII2 Q9UII2 ATIF1_HUMAN ATP5IF1 ATPase inhibitor, mitochondrial 33191.1 77875 98063.4 82755.6 152273 88535.8 126504 76518.9 106111 63742.1 94122.1 49394.9 99936.4 110606 96148.5 37650 50944.3 42873.8 +Q9UIQ6 Q9UIQ6 LCAP_HUMAN LNPEP Leucyl-cystinyl aminopeptidase 148231 80357.5 201392 136183 100889 123173 153833 178783 94539.6 112581 38345.4 156622 108962 92933.2 156271 58455 +Q9UJH8 Q9UJH8;I3L2T3;J3KMW6 METRN_HUMAN METRN Meteorin 23505.3 29659.5 19059.9 34315.1 40792.3 45829.2 37027.8 +Q9UK22 Q9UK22;A0A286YF37;R4GNH2 FBX2_HUMAN FBXO2 F-box only protein 2 65244.4 94635.4 60769 126206 52084.6 109180 57080.1 104275 62149.4 97571.7 89653.1 68429.1 72372.1 63331.8 +Q9UK76 Q9UK76;J3KT51;J3KSH8 JUPI1_HUMAN JPT1 Jupiter microtubule associated homolog 1 163100 171873 48236.6 215566 257018 325270 219097 264830 120261 336462 230228 270558 200248 335397 396715 281173 124202 147492 297603 154785 +Q9UKA9 Q9UKA9;A0A6Q8PEZ3;A0A6Q8PFC0;A0A6Q8PFE6;A0A6Q8PH43;A0A7I2RVZ4;B4DI28 PTBP2_HUMAN PTBP2 Polypyrimidine tract-binding protein 2 155073 130108 97134.8 246217 381221 477871 327711 485602 418888 406511 375367 423082 520333 422414 237954 402558 256406 366324 363593 259321 +Q9UKD2 Q9UKD2 MRT4_HUMAN MRTO4 mRNA turnover protein 4 homolog 115401 92982.6 146470 125025 109803 78306.9 118416 101916 106019 97226.6 117713 120027 96168 113284 74555.1 123722 70619.2 76600.7 124891 47086.5 +Q9UKK9 Q9UKK9;A6NCQ0;A6NFX8;A6NJU6;C9JYY9 NUDT5_HUMAN NUDT5 ADP-sugar pyrophosphatase 326087 344570 340259 340036 441350 399721 386344 305416 357316 545814 379270 303903 368924 328122 434756 343819 380973 224114 316110 243855 +Q9UKM9 Q9UKM9;Q5QPL9;Q5QPM0;Q5QPM1 RALY_HUMAN RALY RNA-binding protein Raly 172275 178697 123676 193329 125367 192776 124528 164318 76406.8 152688 120742 202208 181662 92231.5 155148 101969 116115 85059.3 +Q9UKS6 Q9UKS6;A0A0C4DGG1;E9PIY1;E9PJ75;E9PNM9 PACN3_HUMAN PACSIN3 Protein kinase C and casein kinase substrate in neurons protein 3 24079.1 36735.3 76201.4 36928.4 70940.3 71220.9 71037 66052.7 73490.9 56209.9 73872.4 52662 +Q9UKV3 Q9UKV3;E7EQT4;S4R3H4 ACINU_HUMAN ACIN1 Apoptotic chromatin condensation inducer in the nucleus 55296.6 98040.8 78760.6 79741.8 82276.4 111280 72779.4 84191.4 82309.7 90243.1 102407 45984.1 85021.2 98804.9 70217.3 66742.7 +Q9UKV8 Q9H9G7;Q9HCK5;Q9UKV8;Q9UL18;A0A6I8PTZ8;Q5TA58;A0A8V8TNY0 AGO2_HUMAN AGO2 Protein argonaute-2 47759.9 64960.8 56733.1 35785.9 25898.3 39394.6 70708 29183.6 42871 56738.1 29714.5 43531.3 31408 48967.1 44523.4 41653.2 45825.9 106611 +Q9UKY7 Q9UKY7;D6R9V8;A0A8I5KX85;D6RDN0;D6RFH2 CDV3_HUMAN CDV3 Protein CDV3 homolog 272078 177787 218390 196090 391650 266402 390649 231952 400114 408397 375011 264883 449939 223608 479912 232648 401134 220768 434894 267886 +Q9UKZ9 Q9UKZ9;H7C5D5 PCOC2_HUMAN PCOLCE2 Procollagen C-endopeptidase enhancer 2 2.05216e+06 954038 2.34008e+06 2.06618e+06 2.4324e+06 1.5836e+06 2.64304e+06 1.46413e+06 3.16194e+06 1.64618e+06 2.60888e+06 1.97878e+06 2.97871e+06 1.95829e+06 2.16329e+06 1.73016e+06 2.77726e+06 1.50495e+06 1.89025e+06 1.68302e+06 +Q9UL46 Q9UL46;A0A087X1Z3;H0YM70 PSME2_HUMAN PSME2 Proteasome activator complex subunit 2 287221 401941 142892 379081 293096 371506 244173 375287 193134 356044 263512 369687 247062 361322 153743 340603 197201 330228 239212 251537 +Q9ULA0 Q9ULA0;E5RIA4;E7EMB6;E7ETB3 DNPEP_HUMAN DNPEP Aspartyl aminopeptidase 134647 169089 198557 151030 127287 94361.7 158897 86880.8 125436 140214 163263 75143.3 142427 89316.6 182018 148030 191830 129890 189837 119948 +Q9ULF5 Q9ULF5;E7ENT5;E7EV45 S39AA_HUMAN SLC39A10 Zinc transporter ZIP10 327159 178502 361213 570578 451033 498217 388688 602645 427658 635754 607874 583317 607901 693143 558478 519071 585299 406350 694735 +Q9ULV4 Q9ULV4;Q6QEF8;B4E3S0;J3KSS5;J3QLV6;H0YHL7 COR1C_HUMAN CORO1C Coronin-1C 231081 176853 203386 140820 250771 197639 221600 154019 186773 197092 205234 187744 232334 192615 180642 166221 197210 143375 244930 146229 +Q9ULW8 Q9ULW8 PADI3_HUMAN PADI3 Protein-arginine deiminase type-3 401607 +Q9UM22 Q9UM22 EPDR1_HUMAN EPDR1 Mammalian ependymin-related protein 1 716613 473867 1.27732e+06 1.05644e+06 602634 388561 898071 401713 710598 632635 803941 565798 640345 462208 1.11581e+06 647014 1.61986e+06 636350 542969 1.2585e+06 +Q9UMF0 Q9UMF0 ICAM5_HUMAN ICAM5 Intercellular adhesion molecule 5 104263 16270.7 67545.6 104474 197278 105322 168735 90527.2 286225 149443 177159 145753 316296 148887 179531 122932 258568 146617 123101 125351 +Q9UMR5 Q9UMR5;A0A024RCQ8;A0A0G2JH76;A0A140T8X7;G8JLL2;A0A0G2JHB6;A0A0G2JID8;A0A0G2JIU6;A0A0G2JK02;A0A0G2JK07;A0A0G2JKR3;A0A0G2JLA3;A0A0G2JLL3;A0A140T9Y1;A2ABN7 PPT2_HUMAN PPT2 Lysosomal thioesterase PPT2 739596 614462 679586 1.08911e+06 798891 863749 720686 755671 909949 884367 679885 1.14006e+06 880194 955739 476138 852648 701889 919360 410745 851914 +Q9UMS4 Q9UMS4;H0YGZ5 PRP19_HUMAN PRPF19 Pre-mRNA-processing factor 19 334288 383429 370319 518294 393872 389871 341066 434680 326209 371478 366140 432139 351081 365463 285402 408746 332051 380928 335464 311648 +Q9UMX5 Q9UMX5 NENF_HUMAN NENF Neudesin 173067 254620 313399 328600 227531 295117 310432 278573 233344 380802 276128 318843 144416 307448 292000 393459 267844 349363 209464 372650 +Q9UMY4 Q9UMY4;A0A087X0R6 SNX12_HUMAN SNX12 Sorting nexin-12 40250.2 43589.5 21631.6 71731.5 60461.7 53947.1 33971 60751.9 59907.7 79036.2 +Q9UN86 Q9UN86;A0A7I2V3E2;A0A7I2V3P9;A0A7I2V647;A0A7I2YQS2;D6RAC7;D6RB17 G3BP2_HUMAN G3BP2 Ras GTPase-activating protein-binding protein 2 34345.6 46117.8 22069 53341.8 40182.6 54730.9 12120.2 43788.4 24310.2 23803.3 40011 23290.5 41315.7 45212.5 28072.2 34734.5 +Q9UNE7 Q9UNE7 CHIP_HUMAN STUB1 E3 ubiquitin-protein ligase CHIP 103657 128126 106717 101377 148017 85191.4 148312 85418.7 109771 98190.5 100751 82420.9 129840 106097 117331 122045 82460.9 99324 +Q9UNF0 Q9UNF0;A0A0U1RR22 PACN2_HUMAN PACSIN2 Protein kinase C and casein kinase substrate in neurons protein 2 78446.5 97643.3 91835.8 82966.7 95247.3 104993 79019.6 95256.3 134137 114454 97806.3 117627 79612.4 83456 100798 48497.5 123484 +Q9UNF1 Q9UNF1;Q5H909 MAGD2_HUMAN MAGED2 Melanoma-associated antigen D2 116327 120019 59755.1 149919 85031.4 108102 +Q9UNH7 Q9UNH7;A0A0A0MRI2 SNX6_HUMAN SNX6 Sorting nexin-6 36507 47965.2 34167.4 43301.7 30648.7 44111.4 33749.4 34076.2 27674.1 28780 27189.4 22866 47526.5 +Q9UNM6 Q9UNM6;E9PL38;J3KNQ3;E9PQG3 PSD13_HUMAN PSMD13 26S proteasome non-ATPase regulatory subunit 13 356264 313786 109090 284642 316150 339229 250582 333055 229872 261410 243687 317419 223209 292066 257795 301960 182890 272568 333480 234464 +Q9UNN5 Q9UNN5;B3KT28 FAF1_HUMAN FAF1 FAS-associated factor 1 86084.2 76331.5 70816.9 80769.3 97136.6 32233.3 80760.3 91837.7 74337.3 86092.8 46006.9 103367 107061 78661 49626.2 64940.4 90509.8 47841.5 +Q9UNN8 Q9UNN8;A0A0U1RQQ4 EPCR_HUMAN PROCR Endothelial protein C receptor 855872 115480 255202 192265 138446 246124 86620.3 218532 140429 232111 91238.5 298121 109250 314545 128541 325081 199127 120650 +Q9UNP9 Q9UNP9;E9PEQ6;E9PKY5 PPIE_HUMAN PPIE Peptidyl-prolyl cis-trans isomerase E 81213.3 144079 200087 199002 142184 108905 201969 147722 120210 151904 181653 103226 131912 99951.3 102836 208863 89850.3 143363 135856 157243 +Q9UNQ2 Q9UNQ2;A0A494C0G7;A0A7P0T9J1;A0A7P0T9Y7;A0A7P0Z4I0 DIM1_HUMAN DIMT1 Probable dimethyladenosine transferase 45136.4 38161.1 21976.3 30324.3 41076.6 +Q9UNS2 Q9UNS2;H7C3P9 CSN3_HUMAN COPS3 COP9 signalosome complex subunit 3 62995.3 90558.9 81347.8 105340 109060 46442.2 91993.3 84410 86842.6 70529.1 73187.6 89718.8 86505.5 50944.9 63344.5 102426 56903.6 113336 +Q9UNW1 Q9UNW1 MINP1_HUMAN MINPP1 Multiple inositol polyphosphate phosphatase 1 185525 103704 160241 261225 173288 151829 178666 162389 212794 164557 223191 241152 197510 189310 119049 166467 189525 195222 138204 257838 +Q9UNZ2 Q9UNZ2;F2Z2K0 NSF1C_HUMAN NSFL1C NSFL1 cofactor p47 61572.1 83173.3 65367.9 120061 114672 91591.3 87144.1 113483 85157.1 124414 91651.3 103264 96154.4 107201 135992 82192 71269 51374.4 104941 54116.6 +Q9UQ35 Q9UQ35 SRRM2_HUMAN SRRM2 Serine/arginine repetitive matrix protein 2 23184.1 53003.4 51756.1 51533.6 47613.2 49906.6 46881.2 41771.5 62046.8 52139.7 43533.2 53582.1 47509.7 +Q9UQ53 Q9UQ53;H0YB84;H0YB89;H0YC13 MGT4B_HUMAN MGAT4B Alpha-1,3-mannosyl-glycoprotein 4-beta-N-acetylglucosaminyltransferase B 87741.6 79417.8 33030.2 80921.1 38821.2 61620.3 88528.7 90318.5 63928.5 +Q9UQ80 Q9UQ80;F8VR77;F8W0A3 PA2G4_HUMAN PA2G4 Proliferation-associated protein 2G4 2.49378e+06 2.20172e+06 2.09876e+06 2.14356e+06 2.40739e+06 2.01159e+06 2.03624e+06 2.0428e+06 2.32578e+06 2.63537e+06 2.29096e+06 2.55562e+06 2.44071e+06 2.19648e+06 1.54375e+06 2.18734e+06 2.94967e+06 1.72696e+06 2.60681e+06 1.882e+06 +Q9UQE7 Q9UQE7 SMC3_HUMAN SMC3 Structural maintenance of chromosomes protein 3 373320 457163 527363 398931 467169 349040 479035 364910 429090 334791 484971 354157 458169 339284 483410 441096 380630 410878 496748 +Q9UQN3 Q9UQN3;A0A087WW88;C9J0A7 CHM2B_HUMAN CHMP2B Charged multivesicular body protein 2b 29211 45594.6 70959.6 83753.7 60950.3 49194.3 55022.1 55438.6 38273.3 69574.7 +Q9Y223 Q9Y223 GLCNE_HUMAN GNE Bifunctional UDP-N-acetylglucosamine 2-epimerase/N-acetylmannosamine kinase 35990.9 48077.8 33613.2 46318.2 75083.2 31547.9 71654.9 54539.5 54756.2 47479.2 24415.3 69384.8 32816.6 59092.1 51663.5 56598.6 +Q9Y224 Q9Y224;G3V4C6;H0YJB9 RTRAF_HUMAN RTRAF RNA transcription, translation and transport factor protein 18177.9 25478.1 30759.4 22767.7 12478.4 46622.8 30536.8 14263.5 47345.5 21857.4 +Q9Y230 Q9Y230;M0R0Y3;X6R2L4 RUVB2_HUMAN RUVBL2 RuvB-like 2 532373 493798 451066 489726 649783 499803 519146 559767 535645 476716 593291 498994 579911 541426 507501 466913 441331 487697 577741 343222 +Q9Y237 Q9Y237;A0A494C021;H0Y4T6;H0Y610;H0Y8P6;J3KMW3 PIN4_HUMAN PIN4 Peptidyl-prolyl cis-trans isomerase NIMA-interacting 4 114249 117903 79279.6 61540.1 111654 83943.4 99979 75012.2 66473.7 86009.1 63293.9 75191.9 91346.9 139552 87655.6 61335.2 88045 +Q9Y251 Q9Y251;A0A7P0TBD9 HPSE_HUMAN HPSE Heparanase 365880 93366.3 369429 145540 455499 101843 539747 106336 371390 73909.2 472285 113273 423458 601165 172243 349607 169840 394192 219073 +Q9Y262 Q9Y262;B0QY89;B0QY90 EIF3L_HUMAN EIF3L Eukaryotic translation initiation factor 3 subunit L 394819 276663 143812 260417 301541 299367 259198 278331 237056 271806 281625 267890 275405 271790 235545 254315 202457 232269 370505 197878 +Q9Y263 Q9Y263;E5RIM3;H0YBW4 PLAP_HUMAN PLAA Phospholipase A-2-activating protein 171476 24509.8 29813.1 27619.6 35389.9 41649.8 +Q9Y265 Q9Y265;E7ETR0 RUVB1_HUMAN RUVBL1 RuvB-like 1 649258 597322 537321 591881 731813 664546 671683 735887 655691 566238 707037 604091 682899 580410 665405 494283 512442 589832 713244 426174 +Q9Y266 Q9Y266;A0A0A0MSU9 NUDC_HUMAN NUDC Nuclear migration protein nudC 336284 427729 328716 338573 402384 404740 363047 386017 313772 484420 394938 450554 433711 354390 367285 423400 608634 402544 639641 346593 +Q9Y281 Q9Y281 COF2_HUMAN CFL2 Cofilin-2 98275.9 164607 109493 146323 146629 140424 210562 126475 106228 125046 174366 108937 105733 169158 287790 249133 37512.4 122029 228131 233284 +Q9Y285 Q9Y285;K7ER00 SYFA_HUMAN FARSA Phenylalanine--tRNA ligase alpha subunit 131924 93527.5 53127.8 165757 88456 107100 122412 76404.3 86399.3 145784 81562.9 144959 93223.8 135965 92497.5 83942.7 64044.1 215318 70769.9 +Q9Y287 Q9Y287;A0A3B3IUC0;U3KQL7 ITM2B_HUMAN ITM2B Integral membrane protein 2B 189875 73115.9 130998 122124 183926 139121 153115 191712 157468 220523 206235 363668 179040 196051 236479 264184 113483 139542 90952.4 197312 +Q9Y2B0 Q9Y2B0;F8W031;F8VP03;F8VXJ7;F8W1U5;H0YIH9 CNPY2_HUMAN CNPY2 Protein canopy homolog 2 459729 697802 750479 825175 652324 616431 774197 673942 492640 916860 770664 756827 879727 722561 512474 1.03708e+06 701482 923272 670853 900579 +Q9Y2E5 Q9Y2E5;E9PCD7;H0YA68 MA2B2_HUMAN MAN2B2 Epididymis-specific alpha-mannosidase 120688 71786.4 171873 93351 143258 100639 179399 90188.8 175225 124697 103467 83162.7 160536 155068 188161 133040 179233 152140 109541 100570 +Q9Y2L1 Q9Y2L1;G3V1J5;F2Z2C0 RRP44_HUMAN DIS3 Exosome complex exonuclease RRP44 92864.7 92391.6 73321.3 119225 111330 118740 118698 81035.3 94461 105709 89164.4 104793 104346 103980 90511.4 82122.7 96512.2 152879 +Q9Y2L5 Q9Y2L5;J3QQJ5 TPPC8_HUMAN TRAPPC8 Trafficking protein particle complex subunit 8 90149.5 53198.4 112078 +Q9Y2P4 Q9Y2P4 S27A6_HUMAN SLC27A6 Long-chain fatty acid transport protein 6 42152.4 270379 220816 +Q9Y2S6 Q9Y2S6 TMA7_HUMAN TMA7 Translation machinery-associated protein 7 626374 413086 576103 392487 435605 247551 600360 193009 602321 352463 556870 260045 576526 303379 528860 332294 655533 410437 632400 450793 +Q9Y2T3 Q9Y2T3;H0YDZ7;Q5SZC3;Q5SZC6 GUAD_HUMAN GDA Guanine deaminase 859020 875969 1.016e+06 737208 822988 621955 918627 486334 924792 809315 845525 464740 775305 645827 719202 785865 1.18529e+06 730212 789737 788866 +Q9Y2W1 Q9Y2W1;A0A3B3ITZ9 TR150_HUMAN THRAP3 Thyroid hormone receptor-associated protein 3 15608.7 26785.3 33780.3 49570.5 27838.8 42494.8 36571.4 30818.5 46612.9 47710 42380.1 51179.2 31673.6 29699.9 13327 27996.3 51945.8 +Q9Y2X3 Q9Y2X3 NOP58_HUMAN NOP58 Nucleolar protein 58 74752.7 105169 88654.8 169287 58258.4 114921 43929.6 113810 87450.1 93486.3 64208.7 94534.4 114737 46266 108443 127676 100332 62451.1 +Q9Y2Z0 Q9Y2Z0 SGT1_HUMAN SUGT1 Protein SGT1 homolog 32292.4 80260.9 97106.6 52352.2 112472 74371.5 107799 91284 47112.2 58764.9 63238.8 45201.3 70696.3 107055 52629.4 +Q9Y303 Q9Y303;A0A3B3IU33;H3BQ15 NAGA_HUMAN AMDHD2 N-acetylglucosamine-6-phosphate deacetylase 15908.5 29456.3 21731.8 14487.3 37319 24404.4 22230.7 33375.9 +Q9Y315 Q9Y315;E9PPM8;G3V158 DEOC_HUMAN DERA Deoxyribose-phosphate aldolase 17074 19244.7 27809.9 8912.15 40715.4 40451.1 24180.4 26364.1 26797.4 24287.6 +Q9Y316 Q9Y316 MEMO1_HUMAN MEMO1 Protein MEMO1 102906 34758.9 47650.8 47212.9 59176.9 37964.7 23776.6 30604.7 34214.8 82126 41813.4 51441.9 49814.9 71851.6 +Q9Y376 Q9Y376;Q9H9S4;B7ZBJ4;Q5TAW7 CAB39_HUMAN CAB39 Calcium-binding protein 39 70464.7 39220.6 25862.1 64698.1 29701.8 59725.9 27484.1 60691.2 45832.3 47065.5 39311.2 94324 35757.1 98413.6 42376.3 85663.9 55255.3 81936.2 82396 +Q9Y383 Q9Y383;A0A0A6YYJ8 LC7L2_HUMAN LUC7L2 Putative RNA-binding protein Luc7-like 2 130572 172028 171788 160555 129205 113260 137884 160129 86493.6 120398 154300 166687 99172.6 118889 132175 159732 115480 102607 199014 66142.9 +Q9Y3A5 Q9Y3A5;A0A8V8TLR8;A0A8V8TLS5;A0A8V8TMR6;A0A8V8TN15;F8WE72 SBDS_HUMAN SBDS Ribosome maturation protein SBDS 124839 154235 82850.3 115171 100374 107554 143063 125758 53164.7 91002.9 101419 100709 97214.1 115605 189823 167100 69328.4 93202.5 171947 145870 +Q9Y3B4 Q9Y3B4 SF3B6_HUMAN SF3B6 Splicing factor 3B subunit 6 83851.1 46959.9 101604 86926.1 43241.4 78958.2 55145.7 78850 101731 49689.8 94210.3 60482.2 58613 87358.9 +Q9Y3B8 Q9Y3B8;F5GYG5;H0YG54;H0YGR4 ORN_HUMAN REXO2 Oligoribonuclease, mitochondrial 364744 312546 444578 297338 284991 210228 306257 156046 226131 296605 292896 241426 290798 249350 345770 338882 635613 331437 334591 +Q9Y3C1 Q9Y3C1;A0A0C4DGU5;D6RC60;D6RIC3;A0A087WU10 NOP16_HUMAN NOP16 Nucleolar protein 16 76315.7 78879.8 50094.2 90795.6 70336.5 71959.6 48358.7 80778.7 21691.5 70576.1 59267.4 87588.5 47243.1 66418.9 74149.9 76943.2 35754.1 57457.4 +Q9Y3C6 Q9Y3C6 PPIL1_HUMAN PPIL1 Peptidyl-prolyl cis-trans isomerase-like 1 125249 61401.3 37293.5 73784.5 98203.3 24566.6 91222.1 104371 99649.3 106018 63056.6 140936 +Q9Y3C8 Q9Y3C8 UFC1_HUMAN UFC1 Ubiquitin-fold modifier-conjugating enzyme 1 313723 335060 347106 251414 344140 291056 467019 166622 301761 337918 350591 208721 261830 255014 476062 357843 371799 364835 389386 312004 +Q9Y3D8 Q9Y3D8;A0A087WVD7;A0A087WY94 KAD6_HUMAN AK6 Adenylate kinase isoenzyme 6 181286 160772 159811 221563 153976 183408 174941 135155 202166 325581 177563 100830 163835 +Q9Y3F4 Q9Y3F4;H0YH33 STRAP_HUMAN STRAP Serine-threonine kinase receptor-associated protein 683524 508781 404884 506550 718242 576618 572348 552879 550940 580476 537550 547880 516829 498432 613701 438719 548721 459617 708110 268965 +Q9Y3I0 Q9Y3I0 RTCB_HUMAN RTCB RNA-splicing ligase RtcB homolog 92735.9 145075 65256.4 145373 171881 217482 135134 180599 144044 168960 112601 145665 205791 179922 102385 146789 141567 156585 132199 162421 +Q9Y3R4 Q9Y3R4 NEUR2_HUMAN NEU2 Sialidase-2 289658 +Q9Y3U8 Q9Y3U8;J3KTD3;J3QSB5 RL36_HUMAN RPL36 60S ribosomal protein L36 2.47369e+06 2.30532e+06 2.67144e+06 3.26518e+06 1.6463e+06 1.82674e+06 2.14708e+06 2.21863e+06 1.96438e+06 1.63613e+06 2.25307e+06 2.32372e+06 2.10779e+06 1.73588e+06 1.74128e+06 2.04859e+06 1.31234e+06 1.85073e+06 2.12418e+06 1.82718e+06 +Q9Y446 Q9Y446 PKP3_HUMAN PKP3 Plakophilin-3 163904 21863 +Q9Y450 Q9Y450;B7Z524;H0YDX7;J3QT46;H0YES5 HBS1L_HUMAN HBS1L HBS1-like protein 27910.1 +Q9Y490 Q9Y490 TLN1_HUMAN TLN1 Talin-1 78758.8 138811 80985.8 111170 147852 94690.2 149603 86692.6 134738 96620 129350 100079 143657 105900 133792 73974.6 106418 156825 130430 +Q9Y4K0 Q9Y4K0 LOXL2_HUMAN LOXL2 Lysyl oxidase homolog 2 943288 841392 1.27962e+06 1.71172e+06 1.21122e+06 1.34429e+06 1.40265e+06 1.62641e+06 1.33566e+06 1.44585e+06 1.47143e+06 2.01047e+06 1.16449e+06 1.72576e+06 1.27224e+06 2.08944e+06 1.06104e+06 1.94365e+06 869297 2.2712e+06 +Q9Y4K1 Q9Y4K1;A0A0J9YWL0;Q96QW7 CRBG1_HUMAN CRYBG1 Beta/gamma crystallin domain-containing protein 1 395677 +Q9Y4L1 Q9Y4L1;A0A087WWI4;A0A087X054;A0A494C039;E9PJ21;J3QLE9;K7EQK2;A0A0G2JR71;A0A0G2JR88;A0A0G2JRG7;A0A0G2JRS0;A0A0G2JRT9;A0A8Q3SHG4;A0A8Q3SHP7;J3KTF1;J3QL06;J3QQH7;Q9BST8 HYOU1_HUMAN HYOU1 Hypoxia up-regulated protein 1 358812 131933 174034 191711 406655 200401 332732 230407 505163 208393 356489 305073 400495 223246 343346 203773 293765 202844 265898 +Q9Y4X5 Q9Y4X5 ARI1_HUMAN ARIH1 E3 ubiquitin-protein ligase ARIH1 36021.5 65581.7 32220.1 33725.3 32347.4 +Q9Y520 Q9Y520;E7EPN9 PRC2C_HUMAN PRRC2C Protein PRRC2C 5686.95 27221.6 27593 39219.1 36144.5 29236.9 33206.7 41810.6 16224.2 31003 30975.4 24184.4 32073.8 +Q9Y547 Q9Y547;A6NIR2;X6R7Y7 IFT25_HUMAN HSPB11 Intraflagellar transport protein 25 homolog 37435.9 83885.6 43757.8 75336.2 96632.1 38335.4 61318.8 49113.6 73877.9 52277.9 72741.1 51628.7 84522.2 78222.8 41747.9 108732 71813 +Q9Y570 Q9Y570 PPME1_HUMAN PPME1 Protein phosphatase methylesterase 1 45189.2 22672.8 50390.3 34051.3 28533.8 45938.7 26753.7 15880.2 16752 32092.6 +Q9Y5B9 Q9Y5B9 SP16H_HUMAN SUPT16H FACT complex subunit SPT16 604607 587848 677966 669207 506128 544733 618150 566318 411669 440505 566124 541226 380950 460522 350118 506374 328387 461945 579634 415465 +Q9Y5K5 Q9Y5K5;H0Y6Y4;Q5LJA5;Q5LJA9;H0Y4E0;H0Y4K0;H0Y636 UCHL5_HUMAN UCHL5 Ubiquitin carboxyl-terminal hydrolase isozyme L5 153927 237562 219698 166628 207875 108200 256529 187666 276657 109866 208469 213144 229146 176619 271747 124569 162699 210465 227559 +Q9Y5L0 Q9Y5L0;C9J7E5;E9PFH4 TNPO3_HUMAN TNPO3 Transportin-3 81064.6 79501.3 35769.6 83008.2 85889.5 69002.9 72177.2 85822.6 52304.6 78105.6 120939 82105.5 68178.1 76155.3 75692.5 +Q9Y5S9 Q9Y5S9 RBM8A_HUMAN RBM8A RNA-binding protein 8A 197679 212334 109061 159243 180316 286531 209154 187112 175700 291969 153500 225020 146907 204627 183485 266294 102573 246730 204791 160860 +Q9Y5V0 Q9Y5V0;E5RHR6;E5RHV4;H0YAP1 ZN706_HUMAN ZNF706 Zinc finger protein 706 167533 275302 173734 260868 212387 149980 300135 218047 173437 230189 223434 191184 109054 206297 345948 300147 179519 292884 280796 320886 +Q9Y605 Q9Y605 MOFA1_HUMAN MRFAP1 MORF4 family-associated protein 1 24266.6 34278.6 40052.9 30533.1 11074.9 52343.2 51410.2 28496.8 22002 32171.2 21788.8 38262.4 29915.5 +Q9Y617 Q9Y617 SERC_HUMAN PSAT1 Phosphoserine aminotransferase 754690 1.11001e+06 852643 910933 819344 881301 906086 618759 946309 1.09104e+06 727385 602932 847385 833550 637785 976869 1.14537e+06 1.02551e+06 738803 979564 +Q9Y678 Q9Y678 COPG1_HUMAN COPG1 Coatomer subunit gamma-1 137585 232963 203089 187500 274248 146778 292649 119691 222813 128912 231049 128895 252120 138649 202211 113206 203987 169337 97088.8 +Q9Y696 Q9Y696 CLIC4_HUMAN CLIC4 Chloride intracellular channel protein 4 23856.4 41849.3 25912.7 42500.1 46679.7 62543.8 77266.6 30490 56613.1 37102.7 33846.7 93653 52706.9 24202.2 46740.8 +Q9Y6E2 Q9Y6E2;B5MCE7;B5MCH7;E7ETZ4;Q75MG1 5MP1_HUMAN BZW2 eIF5-mimic protein 1 50749 88246.9 58773.6 58060.2 106885 111503 50769.2 84765.7 55418.5 76295.5 62792.4 87539.3 54233.9 107012 64093.5 +Q9Y6G9 Q9Y6G9;E9PHI6 DC1L1_HUMAN DYNC1LI1 Cytoplasmic dynein 1 light intermediate chain 1 76791.1 52387.1 175813 62973.7 145659 123076 167252 155078 142250 89371.6 123524 106739 98734.3 103196 126217 +Q9Y6L7 Q9Y6L7 TLL2_HUMAN TLL2 Tolloid-like protein 2 374536 726886 708629 502297 430504 526423 536892 1.08018e+06 +Q9Y6M5 Q9Y6M5 ZNT1_HUMAN SLC30A1 Proton-coupled zinc antiporter SLC30A1 73180 22017.4 101498 44545.7 116921 170395 55314.4 183476 54340.6 132180 49151.1 181846 77826.8 79647.3 103239 +Q9Y6N7 Q9Y6N7;A0A087WZ85;A0A0A0MSX4 ROBO1_HUMAN ROBO1 Roundabout homolog 1 98627 110082 140365 135441 124097 103822 151795 111991 99189.6 108760 163666 144002 110902 151620 171953 197969 105012 149611 99913.9 141226 +REFSEQ:XP_001252647 A2I7N3;Q9TTE1;A2I7N1;REFSEQ:XP_001252647 (Bos (Bos 523039 1.61423e+06 424135 438681 663142 636365 394174 289294 1.09262e+06 517042 430611 251278 2.11764e+06 569814 567509 292650 2.21078e+06 504288 214072 394074 diff --git a/tests/test_data/DIANN_data/correct_metadata_table.csv b/tests/test_data/DIANN_data/correct_metadata_table.csv new file mode 100644 index 00000000..157f5f8b --- /dev/null +++ b/tests/test_data/DIANN_data/correct_metadata_table.csv @@ -0,0 +1,21 @@ +MS run,sample name,replicate +LM07061,24h wt DMSO,1 +LM07062,24h ko DMSO,1 +LM07063,24h wt Prodi,1 +LM07064,24h ko Prodi,1 +LM07067,24h wt DMSO,2 +LM07068,24h ko DMSO,2 +LM07069,24h wt Prodi,2 +LM07070,24h ko Prodi,2 +LM07073,24h wt DMSO,3 +LM07074,24h ko DMSO,3 +LM07075,24h wt Prodi,3 +LM07076,24h ko Prodi,3 +LM07079,24h wt DMSO,4 +LM07080,24h ko DMSO,4 +LM07081,24h wt Prodi,4 +LM07082,24h ko Prodi,4 +LM07085,24h wt DMSO,5 +LM07086,24h ko DMSO,5 +LM07087,24h wt Prodi,5 +LM07088,24h ko Prodi,5 diff --git a/tests/test_data/DIANN_data/correct_protein_df.csv b/tests/test_data/DIANN_data/correct_protein_df.csv new file mode 100644 index 00000000..6c3c6ddb --- /dev/null +++ b/tests/test_data/DIANN_data/correct_protein_df.csv @@ -0,0 +1,39721 @@ +Sample,Protein ID,Gene,Intensity +LM07061,A0A024QZP7;E5RIU6;P06493,,525657.0 +LM07061,A0A024QZX5;A0A087X1N8;A0A2R8YD12;P35237,,933891.0 +LM07061,A0A024R4E5;C9J5E5;C9JES8;C9JHS7;C9JHZ8;C9JIZ1;C9JZI8;H7C0A4;Q00341,,60915.4 +LM07061,A0A024R5G9;A0A7I2V2K3;A0A7I2V313;A0A7I2V3E7;A0A7I2V3L9;A0A7I2V3M3;A0A7I2V3V9;A0A7I2V3X1;A0A7I2V411;A0A7I2V4A8;A0A7I2V5B9;A0A7I2V5F8;A0A7I2V5Q1;A0A7I2YQB3;A0A7I2YQH8;A0A7I2YQW3;H0YD65;H0YE42;Q9UBX1,,117841.0 +LM07061,A0A024RA28;A0A7I2V323;A0A7I2V3P1;A0A7I2V4I6;A0A7I2V4N1;P22626,,556189.0 +LM07061,A0A024RA52;A0A7I2V2H3;P25787,,2207490.0 +LM07061,A0A024RA87;A0A0U1RRE1;A0A0U1RRH9;E7EPK1;E7ES33;G3V1Q4;Q16181;Q6ZU15,,127109.0 +LM07061,A0A024RCL3;A0A0G2JH58;A0A0G2JH91;A0A0G2JHB5;A0A0G2JJ55;A0A0G2JK11;A0A140T979;A0A140T986;A0A140T9B8;A0A140T9D8;A0A140T9H4;A0A140T9M1;A0A140T9M6;A0A140T9P3;A0A140T9T9;A0A1W2PPN1;A0A1W2PRA6;F5H7Q8;H0Y615;Q29980;Q96QC4,,57262.6 +LM07061,A0A024RCL3;A0A0G2JK11;A0A140T986;A0A1W2PPN1;A0A1W2PRA6;Q29983;Q96QC4,, 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zMv+gAD!%ZcvhbqM(C{K1j#8+P8l2g2wa`iGC)dGH+JI;_#^=Rm@p=Af#>(t{&&k9u zx7IkE0RFzTT`I&q%}%6g2N0e|U^|u(L>wuLbjnYGOe@Pyc@&;PZRt2sXgCM{wWA(W zBRtDL);YHY59$y6AVJgI5}gt5eb zoVKYK5gs=|+nd81Gr2cvg{@;2zFBuoH_(>vAC3$I%M7iG|9x2QKQHb-*MAwEQKfe%L;enUr~73qUPhlk+5@3;Pj0ssz=eu4i#yRQ#<9&W<@ zW-3Dae-H6z(fhmHeaP~#r~aFT6uP+w{VNYU><ONc#6w|Ca{&POKk{hd9{>OV literal 0 HcmV?d00001 diff --git a/tests/test_workflows/example_workflow.yaml b/tests/test_workflows/example_workflow.yaml new file mode 100644 index 00000000..b4028a1a --- /dev/null +++ b/tests/test_workflows/example_workflow.yaml @@ -0,0 +1,70 @@ +steps: + - form_inputs: + intensity_name: iBAQ + map_to_uniprot: false + inputs: { } + type: MaxQuantImport + - form_inputs: + feature_orientation: Columns (samples in rows, features in columns) + inputs: { } + type: MetadataImport + - form_inputs: + percentage: 0.5 + inputs: { } + plot_inputs: + graph_type: Bar chart + type: FilterProteinsBySamplesMissing + - form_inputs: + deviation_threshold: 2.0 + inputs: { } + plot_inputs: + graph_type: Bar chart + type: FilterSamplesByProteinIntensitiesSum + - form_inputs: + number_of_neighbours: 5 + inputs: { } + plot_inputs: + graph_type: Boxplot + graph_type_quantities: Bar chart + group_by: None + visual_transformation: log10 + type: ImputationByKNN + - form_inputs: + number_of_neighbors: 20 + inputs: { } + plot_inputs: { } + type: OutlierDetectionByLocalOutlierFactor + - form_inputs: + percentile: 0.5 + inputs: { } + plot_inputs: + graph_type: Boxplot + group_by: None + visual_transformation: log10 + type: NormalisationByMedian + - form_inputs: + log_base: log2 + inputs: { } + plot_inputs: + graph_type: Histogram + group_by: None + type: TransformationLog + - form_inputs: + similarity_measure: euclidean distance + inputs: { } + type: PlotProtQuant + - form_inputs: + alpha: 0.05 + inputs: { } + type: DifferentialExpressionTTest + - form_inputs: + fc_threshold: 1 + inputs: { } + type: PlotVolcano + - form_inputs: + differential_expression_threshold: 1 + direction: both + gene_sets_restring: [ ] + organism: 9606 + inputs: { } + type: EnrichmentAnalysisGOAnalysisWithString diff --git a/tests/test_workflows/example_workflow_short.yaml b/tests/test_workflows/example_workflow_short.yaml new file mode 100644 index 00000000..32f3b3f9 --- /dev/null +++ b/tests/test_workflows/example_workflow_short.yaml @@ -0,0 +1,27 @@ +steps: + - form_inputs: + intensity_name: iBAQ + map_to_uniprot: false + inputs: { } + type: MaxQuantImport + - form_inputs: + feature_orientation: Columns (samples in rows, features in columns) + inputs: { } + type: MetadataImport + - form_inputs: + percentage: 0.5 + inputs: { } + plot_inputs: + graph_type: Bar chart + type: FilterProteinsBySamplesMissing + - form_inputs: + alpha: 0.05 + inputs: { } + type: DifferentialExpressionTTest + - form_inputs: + differential_expression_threshold: 1 + direction: both + gene_sets_restring: [ ] + organism: 9606 + inputs: { } + type: EnrichmentAnalysisGOAnalysisWithString \ No newline at end of file diff --git a/tests/ui/test_views.py b/tests/ui/test_views.py index ce83b136..a8e41c91 100644 --- a/tests/ui/test_views.py +++ b/tests/ui/test_views.py @@ -1,16 +1,12 @@ -import json import sys -from shutil import rmtree from protzilla.constants.paths import UI_PATH sys.path.append(f"{UI_PATH}") -from protzilla import data_preprocessing -from protzilla.constants.paths import PROJECT_PATH, RUNS_PATH -from protzilla.run_v2 import Run -from protzilla.utilities import random_string -from ui.runs.views import (active_runs, all_button_parameters) +from protzilla.constants.paths import PROJECT_PATH + +""", all_button_parameters def assert_response( @@ -26,10 +22,9 @@ def assert_response( assert data["current_parameters"] == current_parameters assert data["chosen_method"] == chosen_method - def test_all_button_parameters(): run_name = "test_all_button_params" + random_string() - run = Run.create(run_name) + run = Run(run_name) active_runs[run_name] = run assert_response( @@ -107,59 +102,45 @@ def test_all_button_parameters(): chosen_method="samples_missing_filter", ) - rmtree(RUNS_PATH / run_name) + rmtree(RUNS_PATH / run_name)""" -def test_step_finished(): - run_name = "test_step_finished" + random_string() - run = Run.create(run_name) - active_runs[run_name] = run - - assert not run.current_step.finished +def test_step_finished(run_standard): + assert not run_standard.current_step.finished parameters = { "file_path": f"{PROJECT_PATH}/tests/proteinGroups_small_cut.txt", "intensity_name": "Intensity", + "map_to_uniprot": False, } - run.perform_calculation_from_location( - "importing", "ms_data_import", "max_quant_import", parameters - ) + run_standard.step_calculate(parameters) - assert run.current_step.finished + assert run_standard.current_step.finished - run.next_step() - run.step_index = 1 + run_standard.step_next() - assert not run.current_step.finished + assert not run_standard.current_step.finished parameters = { "file_path": f"", "feature_orientation": "Columns (samples in rows, features in columns)", } - run.perform_calculation_from_location( - "importing", "metadata_import", "metadata_import_method", parameters - ) + run_standard.step_calculate(parameters) - assert not run.current_step.finished + assert not run_standard.current_step.finished parameters = { "file_path": f"{PROJECT_PATH}/tests/nonexistent_file.txt", "feature_orientation": "Columns (samples in rows, features in columns)", } - run.perform_calculation_from_location( - "importing", "metadata_import", "metadata_import_method", parameters - ) + run_standard.step_calculate(parameters) - assert not run.current_step.finished + assert not run_standard.current_step.finished parameters = { "file_path": f"{PROJECT_PATH}/tests/metadata_cut_columns.csv", "feature_orientation": "Columns (samples in rows, features in columns)", } - run.perform_calculation_from_location( - "importing", "metadata_import", "metadata_import_method", parameters - ) - - assert run.current_step.finished + run_standard.step_calculate(parameters) - rmtree(RUNS_PATH / run_name) \ No newline at end of file + assert run_standard.current_step.finished diff --git a/tests/ui/test_views_helper.py b/tests/ui/test_views_helper.py deleted file mode 100644 index 13a9ffde..00000000 --- a/tests/ui/test_views_helper.py +++ /dev/null @@ -1,94 +0,0 @@ -import copy - -import pandas as pd -import pytest -from django.test.client import RequestFactory - -from ui.runs.views_helper import ( - convert_str_if_possible, - get_displayed_steps, - parameters_from_post, -) - - -@pytest.fixture -def mock_metadata_df(): - test_metadata_list = ( - ["Sample1", "Group1", "Batch1"], - ["Sample2", "Group1", "Batch1"], - ["Sample3", "Group1", "Batch2"], - ["Sample4", "Group2", "Batch1"], - ["Sample5", "Group2", "Batch2"], - ["Sample6", "Group2", "Batch1"], - ["Sample7", "Group3", "Batch2"], - ) - - return pd.DataFrame( - data=test_metadata_list, - columns=["Sample", "Group", "Batch"], - ) - - -@pytest.fixture -def mock_post(): - rf = RequestFactory() - post_request = rf.post( - "/calculate/", - { - "csrfmiddlewaretoken": ["test_token"], - "chosen_method": ["samples_missing_filter"], - "percentage": ["0.5"], - }, - ) - return post_request.POST - - -def test_parameters_from_post(mock_post): - assert parameters_from_post(mock_post) == { - "chosen_method": "samples_missing_filter", - "percentage": 0.5, - } - - -def test_convert_str_if_possible(): - assert convert_str_if_possible("1.0") == 1 - assert convert_str_if_possible("0.5") == 0.5 - assert convert_str_if_possible("test") == "test" - assert isinstance(convert_str_if_possible("1.00"), int) - - -def test_get_displayed_steps_structure( - workflow_meta, - example_workflow_short, -): - section_keys = {"finished", "id", "name", "possible_steps", "steps", "selected"} - possible_steps_keys = {"id", "methods", "name"} - possible_steps_keys_methods = {"id", "name", "description"} - steps_keys = { - "id", - "name", - "selected", - "index", - "method_name", - "name", - "selected", - "finished", - } - - result = get_displayed_steps(example_workflow_short, workflow_meta, 1) - - assert all(set(section.keys()) == section_keys for section in result) - assert set(result[0]["possible_steps"][0].keys()) == possible_steps_keys - assert ( - set(result[0]["possible_steps"][0]["methods"][0].keys()) - == possible_steps_keys_methods - ) - assert result[0]["steps"][0].keys() == steps_keys - - -def test_get_displayed_steps_no_side_effects(workflow_meta, example_workflow_short): - example_workflow_copy = copy.deepcopy(example_workflow_short) - workflow_meta_copy = copy.deepcopy(workflow_meta) - get_displayed_steps(example_workflow_short, workflow_meta, 0) - assert example_workflow_short == example_workflow_copy - assert workflow_meta == workflow_meta_copy diff --git a/ui/runs/fields.py b/ui/runs/fields.py index 531b9a08..e4dc6f21 100644 --- a/ui/runs/fields.py +++ b/ui/runs/fields.py @@ -11,7 +11,7 @@ sys.path.append(f"{BASE_DIR}/..") import ui.runs.form_mapping as form_map -from protzilla.run_v2 import Run +from protzilla.run import Run from protzilla.utilities import name_to_title from ui.runs.views_helper import get_displayed_steps diff --git a/ui/runs/form_mapping.py b/ui/runs/form_mapping.py index 178132c9..feb5d043 100644 --- a/ui/runs/form_mapping.py +++ b/ui/runs/form_mapping.py @@ -8,7 +8,7 @@ import ui.runs.forms.data_integration as data_integration_forms import ui.runs.forms.data_preprocessing as data_preprocessing_forms import ui.runs.forms.importing as importing_forms -from protzilla.run_v2 import Run +from protzilla.run import Run from protzilla.steps import Step from .forms.base import MethodForm diff --git a/ui/runs/forms/base.py b/ui/runs/forms/base.py index 8a1edd2a..ba6b9385 100644 --- a/ui/runs/forms/base.py +++ b/ui/runs/forms/base.py @@ -4,7 +4,7 @@ from django.forms import Form from django.urls import reverse -from protzilla.run_v2 import Run +from protzilla.run import Run from protzilla.utilities import get_file_name_from_upload_path from ui.runs.forms.custom_fields import CustomCharField, CustomFileField diff --git a/ui/runs/forms/data_analysis.py b/ui/runs/forms/data_analysis.py index 353d7cff..e0a2c58e 100644 --- a/ui/runs/forms/data_analysis.py +++ b/ui/runs/forms/data_analysis.py @@ -5,7 +5,7 @@ DifferentialExpressionTTest, DimensionReductionUMAP, ) -from protzilla.run_v2 import Run +from protzilla.run import Run from protzilla.steps import Step from . import fill_helper diff --git a/ui/runs/forms/data_integration.py b/ui/runs/forms/data_integration.py index 440f4e99..1ed60152 100644 --- a/ui/runs/forms/data_integration.py +++ b/ui/runs/forms/data_integration.py @@ -9,7 +9,7 @@ biomart_database, uniprot_databases, ) -from protzilla.run_v2 import Run +from protzilla.run import Run from protzilla.steps import Step from . import fill_helper diff --git a/ui/runs/forms/fill_helper.py b/ui/runs/forms/fill_helper.py index dbea9279..0b416f8f 100644 --- a/ui/runs/forms/fill_helper.py +++ b/ui/runs/forms/fill_helper.py @@ -1,4 +1,4 @@ -from protzilla.run_v2 import Run +from protzilla.run import Run from protzilla.steps import Step diff --git a/ui/runs/forms/importing.py b/ui/runs/forms/importing.py index fb7323bb..ec0a93fe 100644 --- a/ui/runs/forms/importing.py +++ b/ui/runs/forms/importing.py @@ -1,7 +1,7 @@ from enum import Enum from protzilla.methods.importing import ImportingStep -from protzilla.run_v2 import Run +from protzilla.run import Run from .base import MethodForm from .custom_fields import CustomBooleanField, CustomChoiceField, CustomFileField @@ -131,13 +131,19 @@ class PeptideImportForm(MethodForm): ) def fill_form(self, run: Run) -> None: - from protzilla.methods.importing import MaxQuantImport, MsFraggerImport, DiannImport + from protzilla.methods.importing import ( + DiannImport, + MaxQuantImport, + MsFraggerImport, + ) - self.fields["intensity_name"].initial = ( - run.steps.get_step_input([MaxQuantImport, MsFraggerImport, DiannImport], "intensity_name")) + self.fields["intensity_name"].initial = run.steps.get_step_input( + [MaxQuantImport, MsFraggerImport, DiannImport], "intensity_name" + ) - self.fields["map_to_uniprot"].initial = ( - run.steps.get_step_input([MaxQuantImport, MsFraggerImport, DiannImport], "map_to_uniprot")) + self.fields["map_to_uniprot"].initial = run.steps.get_step_input( + [MaxQuantImport, MsFraggerImport, DiannImport], "map_to_uniprot" + ) class EvidenceImportForm(MethodForm): @@ -150,10 +156,16 @@ class EvidenceImportForm(MethodForm): ) def fill_form(self, run: Run) -> None: - from protzilla.methods.importing import MaxQuantImport, MsFraggerImport, DiannImport + from protzilla.methods.importing import ( + DiannImport, + MaxQuantImport, + MsFraggerImport, + ) - self.fields["intensity_name"].initial = ( - run.steps.get_step_input([MaxQuantImport, MsFraggerImport, DiannImport], "intensity_name")) + self.fields["intensity_name"].initial = run.steps.get_step_input( + [MaxQuantImport, MsFraggerImport, DiannImport], "intensity_name" + ) - self.fields["map_to_uniprot"].initial = ( - run.steps.get_step_input([MaxQuantImport, MsFraggerImport, DiannImport], "map_to_uniprot")) \ No newline at end of file + self.fields["map_to_uniprot"].initial = run.steps.get_step_input( + [MaxQuantImport, MsFraggerImport, DiannImport], "map_to_uniprot" + ) diff --git a/ui/runs/views.py b/ui/runs/views.py index a786e0c5..87aa685d 100644 --- a/ui/runs/views.py +++ b/ui/runs/views.py @@ -19,7 +19,7 @@ from django.urls import reverse from protzilla.run_helper import log_messages -from protzilla.run_v2 import Run, get_available_run_names +from protzilla.run import Run, get_available_run_names from protzilla.stepfactory import StepFactory from protzilla.steps import Step from protzilla.utilities.utilities import ( diff --git a/user_data/workflows/only_import.yaml b/user_data/workflows/only_import.yaml new file mode 100644 index 00000000..b3ec0ce1 --- /dev/null +++ b/user_data/workflows/only_import.yaml @@ -0,0 +1,10 @@ +steps: + - form_inputs: + intensity_name: iBAQ + map_to_uniprot: false + inputs: { } + type: MaxQuantImport + - form_inputs: + feature_orientation: Columns (samples in rows, features in columns) + inputs: { } + type: MetadataImport \ No newline at end of file diff --git a/user_data/workflows/only_import_and_filter_proteins.yaml b/user_data/workflows/only_import_and_filter_proteins.yaml new file mode 100644 index 00000000..cfaa9a3a --- /dev/null +++ b/user_data/workflows/only_import_and_filter_proteins.yaml @@ -0,0 +1,16 @@ +steps: + - form_inputs: + intensity_name: iBAQ + map_to_uniprot: false + inputs: { } + type: MaxQuantImport + - form_inputs: + feature_orientation: Columns (samples in rows, features in columns) + inputs: { } + type: MetadataImport + - form_inputs: + percentage: 0.5 + inputs: { } + plot_inputs: + graph_type: Bar chart + type: FilterProteinsBySamplesMissing \ No newline at end of file diff --git a/user_data/workflows/test-run-empty.yaml b/user_data/workflows/test-run-empty.yaml new file mode 100644 index 00000000..e7f50af8 --- /dev/null +++ b/user_data/workflows/test-run-empty.yaml @@ -0,0 +1,2 @@ +steps: [ ] + From edc7d652b2b3af2c499a9a199757b5825fc987e3 Mon Sep 17 00:00:00 2001 From: JanniRoebbecke <50395935+JanniRoebbecke@users.noreply.github.com> Date: Thu, 13 Jun 2024 19:47:04 +0200 Subject: [PATCH 07/22] Filter peptides step (#499) * evidence import * tests * filter proteins * filter sampels * Select Peptides by group or sample for coverage visualisation (#410) * untested working, needs cleanup * linter * remove dupliacte method (merge screw-up) * add doc strings, fix first few tests (miss. args) * metadata df now optional param + adjusted asserts, fixed test * formatting * conditional metadata input for protein graphs * import logging levels, formatting * add grouping tests for peptides, add error handling * spelling, formatting * remove unused import, format * fix error msgs in tests * fix line length * Convert has_metadata to property * Use logger instead of print statement * An attempt at consistent text-casing * formatting --------- Co-authored-by: henninggaertner * tests for filter_proteins with peptide file * tests for filter_proteins with peptide file, better mock dataframe * output types * added testing that only filtered Proteins are removed from peptides_df * tests for filter_samples with peptide file * extract test that peptide filtering matches protein filtering into Method * refactor: shortened tests by integrating extracted method and mock data * exclude peptides * tests * minor fixes in test * minor fixes in test * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * match mock protein dataframe to mock peptide dataframe * extract test that peptide filtering matches protein filtering into Method * implementation * tests * tests improve error logging * tests git testing * peptide import added missing column * implements form, form_mapping, method and the actual functionality (untested) * merge peptide filtering and outlier_detection * merge peptide transformation * implement passing of peptide data between preprocessing steps * complete implement passing of peptide data between preprocessing steps * implement overhead for new step evidence import * enable selecting multiple proteins of interest and implements tests * Fix EvidenceImport class (now the right method is called) * implement option to choose protein from list of significant proteins * implement option to let the system choose the most expressed protein * include messages for peptide filtering * remove selecting multiple proteins and add substring search * add cleaning of protein groups to evidence import * implement suggested changes: remove default values and correct grammar mistake * make preprocessing work without peptide_df * clean up * add cleaning of protein groups to evidence import * rename * clean Protein IDs for peptide_import and name intensity column Intensity * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used * unify into one method * cleanup old names * cleanup names * cleanup names * small refactor: utilize fill_helper better * enable selecting multiple proteins * rename file and fix selecting multiple proteins * complete merge (tix tests after merge) * move fixture to conftest.py * remove importing of fixture in conftest.py * cleanup * implement requested changes --------- Co-authored-by: janni.roebbecke --- protzilla/data_analysis/ptm_analysis.py | 30 +++++ protzilla/methods/data_analysis.py | 42 +++++++ protzilla/steps.py | 2 +- .../test_filter_peptites_of_protein.py | 13 +++ .../test_outlier_detection.py | 3 +- .../test_peptide_preprocessing.py | 2 + ui/runs/form_mapping.py | 1 + ui/runs/forms/data_analysis.py | 106 +++++++++++++++--- 8 files changed, 181 insertions(+), 18 deletions(-) create mode 100644 protzilla/data_analysis/ptm_analysis.py create mode 100644 tests/protzilla/data_analysis/test_filter_peptites_of_protein.py diff --git a/protzilla/data_analysis/ptm_analysis.py b/protzilla/data_analysis/ptm_analysis.py new file mode 100644 index 00000000..e2b3d565 --- /dev/null +++ b/protzilla/data_analysis/ptm_analysis.py @@ -0,0 +1,30 @@ +import logging + +import pandas as pd + + +def filter_peptides_of_protein( + peptide_df: pd.DataFrame, protein_ids: list[str], +) -> dict: + """ + This function filters out all peptides with a PEP value (assigned to all samples + together for each peptide) below a certain threshold. + + :param peptide_df: the pandas dataframe containing the peptide information + :param protein_ids: the protein ID to filter the corresponding peptides for + + :return: dict of the filtered peptide dataframe + """ + + filtered_peptide_dfs = [pd.DataFrame] * len(protein_ids) + for i, protein_id in enumerate(protein_ids): + filtered_peptide_dfs[i] = peptide_df[peptide_df["Protein ID"].str.contains(protein_id)] + filtered_peptides = pd.concat(filtered_peptide_dfs) + + return dict( + peptide_df=filtered_peptides, + messages=[{ + "level": logging.INFO if len(filtered_peptides) > 0 else logging.WARNING, + "msg": f"Selected {len(filtered_peptides)} entry's from the peptide dataframe." + }], + ) \ No newline at end of file diff --git a/protzilla/methods/data_analysis.py b/protzilla/methods/data_analysis.py index f74909dc..79982ea2 100644 --- a/protzilla/methods/data_analysis.py +++ b/protzilla/methods/data_analysis.py @@ -1,3 +1,5 @@ +import logging + from protzilla.data_analysis.classification import random_forest, svm from protzilla.data_analysis.clustering import ( expectation_maximisation, @@ -8,6 +10,7 @@ from protzilla.data_analysis.differential_expression_linear_model import linear_model from protzilla.data_analysis.differential_expression_t_test import t_test from protzilla.data_analysis.dimension_reduction import t_sne, umap +from protzilla.data_analysis.ptm_analysis import filter_peptides_of_protein from protzilla.data_analysis.model_evaluation import evaluate_classification_model from protzilla.data_analysis.plots import ( clustergram_plot, @@ -259,6 +262,7 @@ class PlotPrecisionRecallCurve(PlotStep): # TODO: Input "plot_title", ] + # Todo: output_keys def method(self, inputs: dict) -> dict: @@ -278,6 +282,7 @@ class PlotROC(PlotStep): # TODO: Input "plot_title", ] + # Todo: output_keys def method(self, inputs: dict) -> dict: @@ -599,3 +604,40 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: inputs["peptide_df"] = steps.peptide_df inputs["isoform_df"] = steps.isoform_df return inputs + + +class SelectPeptidesForProtein(DataAnalysisStep): + display_name = "Filter Peptides of Protein" + operation = "Peptide analysis" + method_description = "Filter peptides for the a selected Protein of Interest from a peptide dataframe" + + input_keys = [ + "peptide_df", + "protein_ids", + ] + output_keys = [ + "peptide_df", + ] + + def method(self, inputs: dict) -> dict: + return filter_peptides_of_protein(**inputs) + + def insert_dataframes(self, steps: StepManager, inputs) -> dict: + inputs["peptide_df"] = steps.get_step_output( + Step, "peptide_df", inputs["peptide_df"] + ) + + if inputs["auto_select"]: + significant_proteins = ( + steps.get_step_output(DataAnalysisStep, "significant_proteins_df", inputs["protein_list"])) + index_of_most_significant_protein = significant_proteins['corrected_p_value'].idxmin() + most_significant_protein = significant_proteins.loc[index_of_most_significant_protein] + inputs["protein_id"] = [most_significant_protein["Protein ID"]] + self.messages.append({ + "level": logging.INFO, + "msg": + f"Selected the most significant Protein: {most_significant_protein['Protein ID']}, " + f"from {inputs['protein_list']}" + }) + + return inputs diff --git a/protzilla/steps.py b/protzilla/steps.py index 8f943925..ad898d9e 100644 --- a/protzilla/steps.py +++ b/protzilla/steps.py @@ -69,6 +69,7 @@ def calculate(self, steps: StepManager, inputs: dict) -> None: self.form_inputs = self.inputs.copy() try: + self.messages.clear() self.insert_dataframes(steps, self.inputs) self.validate_inputs() @@ -141,7 +142,6 @@ def handle_messages(self, outputs: dict) -> None: :param outputs: A dictionary received after the calculation :return: None """ - self.messages.clear() messages = outputs.get("messages", []) self.messages.extend(messages) diff --git a/tests/protzilla/data_analysis/test_filter_peptites_of_protein.py b/tests/protzilla/data_analysis/test_filter_peptites_of_protein.py new file mode 100644 index 00000000..b191f335 --- /dev/null +++ b/tests/protzilla/data_analysis/test_filter_peptites_of_protein.py @@ -0,0 +1,13 @@ +import pytest + +from protzilla.data_analysis.ptm_analysis import filter_peptides_of_protein + + +def test_filter_peptides_of_protein(peptides_df): + filtered_peptides_df = filter_peptides_of_protein(peptides_df, ["Protein2"])["peptide_df"] + + assert len(filtered_peptides_df) == 6 + assert filtered_peptides_df["Sequence"].tolist() == [ + "SEQB", "SEQC", "SEQD", "SEQK", "SEQP", "SEQU" + ] + assert (filtered_peptides_df["Protein ID"] == "Protein2").all() \ No newline at end of file diff --git a/tests/protzilla/data_preprocessing/test_outlier_detection.py b/tests/protzilla/data_preprocessing/test_outlier_detection.py index f21e18e0..e94f84b2 100644 --- a/tests/protzilla/data_preprocessing/test_outlier_detection.py +++ b/tests/protzilla/data_preprocessing/test_outlier_detection.py @@ -65,7 +65,8 @@ def outlier_detection_df_with_nan(): def test_outlier_detection_with_isolation_forest( - show_figures, outlier_detection_df, peptides_df + show_figures, outlier_detection_df, + peptides_df ): method_inputs = { "protein_df": outlier_detection_df, diff --git a/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py b/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py index 1de769f5..f3900fdd 100644 --- a/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py +++ b/tests/protzilla/data_preprocessing/test_peptide_preprocessing.py @@ -1,4 +1,5 @@ import pandas as pd +import pytest from protzilla.constants.paths import TEST_DATA_PATH from protzilla.data_preprocessing.peptide_filter import by_pep_value, by_pep_value_plot @@ -56,3 +57,4 @@ def test_pep_filter(show_figures, leftover_peptide_df, filtered_peptides_list): pd.testing.assert_frame_equal(method_outputs["peptide_df"], leftover_peptide_df) assert method_outputs["filtered_peptides"] == filtered_peptides_list + diff --git a/ui/runs/form_mapping.py b/ui/runs/form_mapping.py index feb5d043..ab27a219 100644 --- a/ui/runs/form_mapping.py +++ b/ui/runs/form_mapping.py @@ -59,6 +59,7 @@ data_analysis.DimensionReductionUMAP: data_analysis_forms.DimensionReductionUMAPForm, data_analysis.ProteinGraphPeptidesToIsoform: data_analysis_forms.ProteinGraphPeptidesToIsoformForm, data_analysis.ProteinGraphVariationGraph: data_analysis_forms.ProteinGraphVariationGraphForm, + data_analysis.SelectPeptidesForProtein: data_analysis_forms.SelectPeptidesForProteinForm, data_preprocessing.ImputationByMinPerSample: data_preprocessing_forms.ImputationByMinPerSampleForms, data_integration.EnrichmentAnalysisGOAnalysisWithString: data_integration_forms.EnrichmentAnalysisGOAnalysisWithStringForm, data_integration.EnrichmentAnalysisGOAnalysisWithEnrichr: data_integration_forms.EnrichmentAnalysisGOAnalysisWithEnrichrForm, diff --git a/ui/runs/forms/data_analysis.py b/ui/runs/forms/data_analysis.py index e0a2c58e..09c6a73c 100644 --- a/ui/runs/forms/data_analysis.py +++ b/ui/runs/forms/data_analysis.py @@ -1,9 +1,11 @@ +import logging from enum import Enum, StrEnum +from protzilla.methods.data_preprocessing import DataPreprocessingStep from protzilla.methods.data_analysis import ( DifferentialExpressionLinearModel, DifferentialExpressionTTest, - DimensionReductionUMAP, + DimensionReductionUMAP, DataAnalysisStep, ) from protzilla.run import Run from protzilla.steps import Step @@ -15,7 +17,7 @@ CustomChoiceField, CustomFloatField, CustomMultipleChoiceField, - CustomNumberField, + CustomNumberField, CustomBooleanField, ) @@ -219,8 +221,8 @@ def fill_form(self, run: Run) -> None: # Set choices for group2 field based on selected grouping and group1 if ( - "group1" in self.data - and self.data["group1"] in run.steps.metadata_df[grouping].unique() + "group1" in self.data + and self.data["group1"] in run.steps.metadata_df[grouping].unique() ): self.fields["group2"].choices = [ (el, el) @@ -260,8 +262,8 @@ def fill_form(self, run: Run) -> None: # Set choices for group2 field based on selected grouping and group1 if ( - "group1" in self.data - and self.data["group1"] in run.steps.metadata_df[grouping].unique() + "group1" in self.data + and self.data["group1"] in run.steps.metadata_df[grouping].unique() ): self.fields["group2"].choices = [ (el, el) @@ -396,9 +398,9 @@ def fill_form(self, run: Run) -> None: initial=0, ) if ( - "similarity" not in self.data - or float(self.data["similarity"]) < -1 - or float(self.data["similarity"]) > 1 + "similarity" not in self.data + or float(self.data["similarity"]) < -1 + or float(self.data["similarity"]) > 1 ): self.data["similarity"] = 0 else: @@ -410,9 +412,9 @@ def fill_form(self, run: Run) -> None: initial=1, ) if ( - "similarity" not in self.data - or float(self.data["similarity"]) < 0 - or float(self.data["similarity"]) > 999 + "similarity" not in self.data + or float(self.data["similarity"]) < 0 + or float(self.data["similarity"]) > 999 ): self.data["similarity"] = 1 @@ -623,7 +625,7 @@ class ClassificationRandomForestForm(MethodForm): # TODO: Workflow_meta line 1763 train_val_split = CustomNumberField( label="Choose the size of the validation data set (you can either enter the absolute number of validation " - "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", + "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", initial=0.20, ) # TODO: Workflow_meta line 1770 @@ -711,7 +713,7 @@ class ClassificationSVMForm(MethodForm): ) train_val_split = CustomNumberField( label="Choose the size of the validation data set (you can either enter the absolute number of validation " - "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", + "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", initial=0.20, ) # TODO: Workflow_meta line 1973 @@ -828,7 +830,7 @@ class DimensionReductionUMAPForm(MethodForm): ) n_neighbors = CustomNumberField( label="The size of local neighborhood (in terms of number of neighboring sample points) used for manifold " - "approximation", + "approximation", min_value=2, max_value=100, step_size=1, @@ -869,7 +871,7 @@ class ProteinGraphPeptidesToIsoformForm(MethodForm): k = CustomNumberField(label="k-mer length", min_value=1, step_size=1, initial=5) allowed_mismatches = CustomNumberField( label="Number of allowed mismatched amino acids per peptide. For many allowed mismatches, this can take a " - "long time.", + "long time.", min_value=0, step_size=1, initial=2, @@ -881,3 +883,75 @@ class ProteinGraphVariationGraphForm(MethodForm): label="Protein ID", initial="Enter the Uniprot-ID of the protein" ) # TODO: workflow_meta line 2291 - 2295 + + +class SelectPeptidesForProteinForm(MethodForm): + is_dynamic = True + + peptide_df = CustomChoiceField( + choices=[], label="Step to use peptide dataframe from" + ) + + auto_select = CustomBooleanField( + label="Automatically select most significant Protein", + initial=False, + ) + + protein_list = CustomChoiceField( + choices=[], + label="Select a list of Proteins from which you want to choose your Proteins of Interest", + ) + + sort_proteins = CustomBooleanField( + label="Sort Proteins by p-value (requires a list of Proteins from a Differential Expression Analysis to be selected)", + initial=False, + ) + + protein_ids = CustomMultipleChoiceField( + choices=[], + label="Protein IDs", + ) + + def fill_form(self, run: Run) -> None: + self.fields["peptide_df"].choices = fill_helper.get_choices( + run, "peptide_df", Step + ) + self.fields["peptide_df"].initial = run.steps.get_instance_identifiers( + DataPreprocessingStep, "peptide_df" + )[-1] + + selected_auto_select = self.data.get("auto_select") + + choices = fill_helper.to_choices([] if selected_auto_select else ["all proteins"]) + choices.extend(fill_helper.get_choices( + run, "significant_proteins_df", DataAnalysisStep + )) + self.fields["protein_list"].choices = choices + + chosen_list = self.data.get("protein_list", self.fields["protein_list"].choices[0][0]) + if not selected_auto_select: + self.toggle_visibility("sort_proteins", True) + self.toggle_visibility("protein_ids", True) + + if chosen_list == "all proteins": + self.fields["protein_ids"].choices = fill_helper.to_choices( + run.steps.get_step_output( + Step, "protein_df" + )["Protein ID"].unique() + ) + else: + if self.data.get("sort_proteins"): + self.fields["protein_ids"].choices = fill_helper.to_choices( + run.steps.get_step_output( + DataAnalysisStep, "significant_proteins_df", chosen_list + ).sort_values(by="corrected_p_value")["Protein ID"].unique() + ) + else: + self.fields["protein_ids"].choices = fill_helper.to_choices( + run.steps.get_step_output( + DataAnalysisStep, "significant_proteins_df", chosen_list + )["Protein ID"].unique() + ) + else: + self.toggle_visibility("sort_proteins", False) + self.toggle_visibility("protein_ids", False) \ No newline at end of file From d42ec622ad6d0f6cd0b301d1fe9e061be8e579af Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henning=20G=C3=A4rtner?= <104069093+henninggaertner@users.noreply.github.com> Date: Mon, 17 Jun 2024 11:58:43 +0200 Subject: [PATCH 08/22] =?UTF-8?q?Improve=20error=20handling=20to=20only=20?= =?UTF-8?q?handle=20specific=20errors,=20also=20print=20sta=E2=80=A6=20(#5?= =?UTF-8?q?07)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Improve error handling to only handle specific errors, also print stack_trace based on debug mode of Django * Remove outdated tests * Fix correct step index * Improve error handling to only handle specific errors, also print stack_trace based on debug mode of Django * Remove outdated tests * Fix correct step index * Improve the reading of the steps, remove a deprecated TODO --- protzilla/disk_operator.py | 34 ++++++++++++++++++++++++++-------- 1 file changed, 26 insertions(+), 8 deletions(-) diff --git a/protzilla/disk_operator.py b/protzilla/disk_operator.py index c059fc71..ce7f07ad 100644 --- a/protzilla/disk_operator.py +++ b/protzilla/disk_operator.py @@ -1,5 +1,6 @@ from __future__ import annotations +import traceback from dataclasses import dataclass from pathlib import Path @@ -12,6 +13,13 @@ from protzilla.constants.protzilla_logging import logger from protzilla.steps import Messages, Output, Plots, Step, StepManager +try: + from django.conf import settings + + DEBUG_MODE = settings.DEBUG +except ImportError: + DEBUG_MODE = False + class ErrorHandler: def __enter__(self): @@ -23,9 +31,10 @@ def __exit__(self, exc_type, exc_val, exc_tb): logger.error(f"File not found: {exc_val}") elif issubclass(exc_type, PermissionError): logger.error(f"Permission denied: {exc_val}") - else: - logger.error("An error occurred", exc_info=(exc_type, exc_val, exc_tb)) - return False + if DEBUG_MODE: + traceback.print_exception(exc_type, exc_val, exc_tb) + return False + return True class YamlOperator: @@ -100,9 +109,20 @@ def read_run(self, file: Path | None = None) -> StepManager: step_manager = StepManager() step_manager.df_mode = run.get(KEYS.DF_MODE, "disk") for step_data in run[KEYS.STEPS]: - step = self._read_step(step_data, step_manager) + try: + step = self._read_step(step_data, step_manager) + except Exception as e: + logger.error(f"Error reading step: {e}") + continue step_manager.add_step(step) - step_manager.current_step_index = run.get(KEYS.CURRENT_STEP_INDEX, 0) + + # this expression ensures that the current step index is within the bounds of the steps list, and at least 0 + step_manager.current_step_index = max( + 0, + min( + run.get(KEYS.CURRENT_STEP_INDEX, 0), len(step_manager.all_steps) - 1 + ), + ) return step_manager def write_run(self, step_manager: StepManager) -> None: @@ -117,9 +137,7 @@ def write_run(self, step_manager: StepManager) -> None: run[KEYS.DF_MODE] = step_manager.df_mode run[KEYS.STEPS] = [] for step in step_manager.all_steps: - run[KEYS.STEPS].append( - self._write_step(step) - ) # TODO this takes a long time + run[KEYS.STEPS].append(self._write_step(step)) self.yaml_operator.write(self.run_file, run) def read_workflow(self) -> StepManager: From 685fb8022a7e5c6c69a639c615b65f53691ba590 Mon Sep 17 00:00:00 2001 From: JanniRoebbecke <50395935+JanniRoebbecke@users.noreply.github.com> Date: Mon, 24 Jun 2024 21:02:46 +0200 Subject: [PATCH 09/22] Single protein (#509) * single protein is selected for all methods of ms data import and peptide import * maintain order of protein ids in protein group during transform and clean * make the aggregation method optional * fix circular import * fix key word arguments for median and mean * fix tests * fix issue with empty choice ("-------") in aggregation_method multiple choice field * implements suggested changes --------- Co-authored-by: janni.roebbecke --- protzilla/importing/ms_data_import.py | 51 +- protzilla/importing/peptide_import.py | 8 +- protzilla/methods/importing.py | 6 +- tests/conftest.py | 11 +- tests/proteinGroups_small_cut.txt | 4 +- .../importing/test_metadata_import.py | 1 + .../importing/test_ms_data_import.py | 73 +- .../importing/test_peptide_import.py | 22 +- tests/protzilla/test_run.py | 1 + tests/protzilla/test_runner.py | 4 +- .../DIANN_data/correct_protein_df.csv | 51150 ++++++++-------- .../maxquant_small_noproteincolumn.tsv | 60 +- tests/ui/test_views.py | 1 + ui/runs/forms/importing.py | 15 + user_data/workflows/only_import.yaml | 1 + .../only_import_and_filter_proteins.yaml | 1 + user_data/workflows/standard.yaml | 1 + 17 files changed, 25540 insertions(+), 25870 deletions(-) diff --git a/protzilla/importing/ms_data_import.py b/protzilla/importing/ms_data_import.py index d102d7ca..c3d9136f 100644 --- a/protzilla/importing/ms_data_import.py +++ b/protzilla/importing/ms_data_import.py @@ -1,14 +1,18 @@ import logging import re +import traceback from collections import defaultdict import numpy as np import pandas as pd from protzilla.data_integration.database_query import biomart_query +from protzilla.utilities import format_trace -def max_quant_import(file_path: str, intensity_name: str, map_to_uniprot=False) -> dict: +def max_quant_import( + file_path: str, intensity_name: str, map_to_uniprot=False, aggregation_method: str ="Sum" +) -> dict: assert intensity_name in ["Intensity", "iBAQ", "LFQ intensity"] try: df = pd.read_csv( @@ -18,7 +22,7 @@ def max_quant_import(file_path: str, intensity_name: str, map_to_uniprot=False) na_values=["", 0], keep_default_na=True, ) - protein_groups = df["Protein IDs"] + protein_groups = df["Majority protein IDs"] intensity_df = df.filter(regex=f"^{intensity_name} ", axis=1) intensity_df = intensity_df.filter(regex=r"^(?!.*peptides).*$", axis=1) @@ -30,15 +34,15 @@ def max_quant_import(file_path: str, intensity_name: str, map_to_uniprot=False) c[len(intensity_name) + 1 :] for c in intensity_df.columns ] intensity_df = intensity_df.assign(**{"Protein ID": protein_groups}) - return transform_and_clean(intensity_df, intensity_name, map_to_uniprot) + return transform_and_clean(intensity_df, intensity_name, map_to_uniprot, aggregation_method) except Exception as e: msg = f"An error occurred while reading the file: {e.__class__.__name__} {e}. Please provide a valid Max Quant file." - return dict(messages=[dict(level=logging.ERROR, msg=msg)]) + return dict(messages=[dict(level=logging.ERROR, msg=msg, trace=format_trace(traceback.format_exception(e)))]) def ms_fragger_import( - file_path: str, intensity_name: str, map_to_uniprot=False + file_path: str, intensity_name: str, map_to_uniprot=False, aggregation_method: str ="Sum" ) -> dict: assert intensity_name in [ "Intensity", @@ -83,13 +87,13 @@ def ms_fragger_import( ) intensity_df = intensity_df.assign(**{"Protein ID": protein_groups}) - return transform_and_clean(intensity_df, intensity_name, map_to_uniprot) + return transform_and_clean(intensity_df, intensity_name, map_to_uniprot, aggregation_method) except Exception as e: msg = f"An error occurred while reading the file: {e.__class__.__name__} {e}. Please provide a valid MS Fragger file." - return dict(None, messages=[dict(level=logging.ERROR, msg=msg)]) + return dict(messages=[dict(level=logging.ERROR, msg=msg, trace=format_trace(traceback.format_exception(e)))]) -def diann_import(file_path, map_to_uniprot=False) -> dict: +def diann_import(file_path, map_to_uniprot=False, aggregation_method: str ="Sum") -> dict: try: df = pd.read_csv( file_path, @@ -113,14 +117,14 @@ def diann_import(file_path, map_to_uniprot=False) -> dict: intensity_name = "Intensity" - return transform_and_clean(intensity_df, intensity_name, map_to_uniprot) + return transform_and_clean(intensity_df, intensity_name, map_to_uniprot, aggregation_method) except Exception as e: msg = f"An error occurred while reading the file: {e.__class__.__name__} {e}. Please provide a valid DIA-NN MS file." - return dict(None, messages=[dict(level=logging.ERROR, msg=msg)]) + return dict(messages=[dict(level=logging.ERROR, msg=msg, trace=format_trace(traceback.format_exception(e)))]) def transform_and_clean( - df: pd.DataFrame, intensity_name: str, map_to_uniprot: bool + df: pd.DataFrame, intensity_name: str, map_to_uniprot: bool, aggregation_method: str ="Sum" ) -> dict: """ Transforms a dataframe that is read from a file in wide format into long format, @@ -151,8 +155,10 @@ def transform_and_clean( has_valid_protein_id = df["Protein ID"].map(bool) df = df[has_valid_protein_id] - # sum intensities for duplicate protein groups, NaN if all are NaN, sum of numbers otherwise - df = df.groupby("Protein ID", as_index=False).sum(min_count=1) + # applies the selected aggregation to duplicate protein groups, NaN if all are NaN, aggregation of numbers otherwise + aggregation_method = aggregation_method.lower() + agg_kwargs = {"sum": {"min_count": 1}, "median": {}, "mean": {}} + df = df.groupby("Protein ID", as_index=False).agg(aggregation_method, **agg_kwargs[aggregation_method]) df = df.assign(Gene=lambda _: np.nan) # add deprecated genes column @@ -192,18 +198,18 @@ def clean_protein_groups(protein_groups, map_to_uniprot=True): # go through all groups and find the valid proteins # non uniprot ids are put into extracted_ids, so they can be mapped for group in protein_groups: - found_in_group = set() + found_in_group = [] for protein_id in group.split(";"): if not protein_id.startswith("ENSP") and ( match := uniprot_regex.search(protein_id) ): - found_in_group.add(match.group(0)) + found_in_group.append(match.group(0)) continue for identifier, pattern in regex.items(): if match := pattern.search(protein_id): found_id = match.group(0) extracted_ids[identifier].add(found_id) - found_in_group.add(found_id) + found_in_group.append(found_id) break # can only match one regex else: removed_protein_ids.append(protein_id) @@ -214,16 +220,17 @@ def clean_protein_groups(protein_groups, map_to_uniprot=True): new_groups = [] for group in found_ids_per_group: - all_ids_of_group = set() + all_ids_of_group = [] for old_id in group: if uniprot_regex.search(old_id): - all_ids_of_group.add(old_id) + all_ids_of_group.append(old_id) elif map_to_uniprot: - new_ids = id_to_uniprot.get(old_id, []) - all_ids_of_group.update(new_ids) + new_ids = pd.Series(id_to_uniprot.get(old_id, [])) + new_ids = new_ids[~new_ids.isin(all_ids_of_group)] + all_ids_of_group.extend(new_ids) else: - all_ids_of_group.add(old_id) - new_groups.append(";".join(sorted(all_ids_of_group))) + all_ids_of_group.append(old_id) + new_groups.append(all_ids_of_group[0] if all_ids_of_group else '') return new_groups, removed_protein_ids diff --git a/protzilla/importing/peptide_import.py b/protzilla/importing/peptide_import.py index 2a393fbd..d38495dd 100644 --- a/protzilla/importing/peptide_import.py +++ b/protzilla/importing/peptide_import.py @@ -24,7 +24,7 @@ def peptide_import(file_path, intensity_name, map_to_uniprot) -> dict: "LFQ intensity" if intensity_name == "iBAQ" else intensity_name ) - id_columns = ["Proteins", "Sequence", "Missed cleavages", "PEP"] + id_columns = ["Leading razor protein", "Sequence", "Missed cleavages", "PEP"] read = pd.read_csv( file_path, sep="\t", @@ -46,7 +46,7 @@ def peptide_import(file_path, intensity_name, map_to_uniprot) -> dict: value_name="Intensity", ) - molten = molten.rename(columns={"Proteins": "Protein ID"}) + molten = molten.rename(columns={"Leading razor protein": "Protein ID"}) ordered = molten[ ["Sample", "Protein ID", "Sequence", "Intensity", "PEP"] ] @@ -79,7 +79,7 @@ def evidence_import(file_path, intensity_name, map_to_uniprot) -> dict: id_columns = [ "Experiment", - "Proteins", + "Leading razor protein", "Sequence", peptide_intensity_name, "Modifications", @@ -99,7 +99,7 @@ def evidence_import(file_path, intensity_name, map_to_uniprot) -> dict: df = read[id_columns] - df = df.rename(columns={"Proteins": "Protein ID"}) + df = df.rename(columns={"Leading razor protein": "Protein ID"}) df = df.rename(columns={"Experiment": "Sample"}) df.dropna(subset=["Protein ID"], inplace=True) diff --git a/protzilla/methods/importing.py b/protzilla/methods/importing.py index 9f79e739..7cde1ba0 100644 --- a/protzilla/methods/importing.py +++ b/protzilla/methods/importing.py @@ -29,7 +29,7 @@ class MaxQuantImport(ImportingStep): operation = "msdataimport" method_description = "Import MaxQuant data" - input_keys = ["file_path", "map_to_uniprot", "intensity_name"] + input_keys = ["file_path", "map_to_uniprot", "intensity_name", "aggregation_method"] output_keys = ["protein_df"] def method(self, inputs): @@ -41,7 +41,7 @@ class DiannImport(ImportingStep): operation = "msdataimport" method_description = "DIA-NN data import" - input_keys = ["file_path", "map_to_uniprot"] + input_keys = ["file_path", "map_to_uniprot", "aggregation_method"] output_keys = ["protein_df"] def method(self, inputs): @@ -53,7 +53,7 @@ class MsFraggerImport(ImportingStep): operation = "msdataimport" method_description = "MS Fragger data import" - input_keys = ["file_path", "intensity_name", "map_to_uniprot"] + input_keys = ["file_path", "intensity_name", "map_to_uniprot", "aggregation_method"] output_keys = ["protein_df"] def method(self, inputs): diff --git a/tests/conftest.py b/tests/conftest.py index dc04046b..d94bc40f 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -69,6 +69,7 @@ def run_imported(run_name_and_cleanup, maxquant_data_file): "file_path": str(maxquant_data_file), "intensity_name": "iBAQ", "map_to_uniprot": False, + "aggregation_method": "Sum", } ) yield run @@ -148,11 +149,11 @@ def df_with_nan(): def leftover_peptide_df(): # sample, protein id, sequence, intensity, pep leftover_peptide_protein_list = ( - ["Sample01", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", np.NAN, 0.037779], - ["Sample02", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", np.NAN, 0.037779], - ["Sample03", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 6923600.0, 0.037779], - ["Sample04", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", np.NAN, 0.037779], - ["Sample05", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 37440000.0, 0.037779], + ["Sample01", "Q13748", "EDLAALEK", np.NAN, 0.037779], + ["Sample02", "Q13748", "EDLAALEK", np.NAN, 0.037779], + ["Sample03", "Q13748", "EDLAALEK", 6923600.0, 0.037779], + ["Sample04", "Q13748", "EDLAALEK", np.NAN, 0.037779], + ["Sample05", "Q13748", "EDLAALEK", 37440000.0, 0.037779], ) peptide_df = pd.DataFrame( diff --git a/tests/proteinGroups_small_cut.txt b/tests/proteinGroups_small_cut.txt index f88e30d0..a9e00698 100644 --- a/tests/proteinGroups_small_cut.txt +++ b/tests/proteinGroups_small_cut.txt @@ -1,7 +1,7 @@ Protein IDs Majority protein IDs Peptide counts (all) Peptide counts (razor+unique) Peptide counts (unique) Protein names Gene names Fasta headers Number of proteins Peptides Razor + unique peptides Unique peptides Peptides AD01_C1_INSOLUBLE_01 Peptides AD01_C1_INSOLUBLE_02 Peptides AD01_C1_INSOLUBLE_03 Peptides AD01_C2_INSOLUBLE_01 Peptides AD02_C1_INSOLUBLE_01 Peptides AD02_C1_INSOLUBLE_02 Peptides AD02_C2_INSOLUBLE_01 Peptides AD03_C1_INSOLUBLE_01 Peptides AD03_C1_INSOLUBLE_02 Peptides AD03_C1_INSOLUBLE_03 Peptides AD03_C2_INSOLUBLE_01 Peptides AD04_C1_INSOLUBLE_01 Peptides AD04_C2_INSOLUBLE_01 Peptides AD05_C2_INSOLUBLE_01 Peptides AD06_C1_INSOLUBLE_01 Peptides AD07_C1_INSOLUBLE_01 Peptides AD07_C1_INSOLUBLE_02 Peptides AD07_C1_INSOLUBLE_03 Peptides AD07_C2_INSOLUBLE_01 Peptides AD08_C2_INSOLUBLE_01 Peptides AD09_C1_INSOLUBLE_01 Peptides AD10_C1_INSOLUBLE_01 Peptides AD10_C2_INSOLUBLE_01 Peptides AD11_C2_INSOLUBLE_01 Peptides AD12_C2_INSOLUBLE_01 Peptides AD13_C2_INSOLUBLE_01 Peptides AD14_C2_INSOLUBLE_01 Peptides AD15_C2_INSOLUBLE_01 Peptides AD16_C2_INSOLUBLE_01 Peptides AD17_C2_INSOLUBLE_01 Peptides AD18_C2_INSOLUBLE_01 Peptides AD19_C2_INSOLUBLE_01 Peptides AD20_C1_INSOLUBLE_01 Peptides AD21_C1_INSOLUBLE_01 Peptides AD21_C2_INSOLUBLE_01 Peptides AD22_C1_INSOLUBLE_01 Peptides AD23_C1_INSOLUBLE_01 Peptides AD23_C1_INSOLUBLE_02 Peptides AD23_C2_INSOLUBLE_01 Peptides AD24_C1_INSOLUBLE_01 Peptides AD24_C1_INSOLUBLE_02 Peptides AD25_C1_INSOLUBLE_01 Peptides AD26_C1_INSOLUBLE_01 Peptides AD27_C1_INSOLUBLE_01 Peptides AD27_C1_INSOLUBLE_02 Peptides AD28_C2_INSOLUBLE_01 Peptides AD29_C1_INSOLUBLE_01 Peptides AD30_C1_INSOLUBLE_01 Peptides AD30_C1_INSOLUBLE_02 Peptides AD30_C2_INSOLUBLE_01 Peptides AD31_C2_INSOLUBLE_01 Peptides AD32_C2_INSOLUBLE_01 Peptides AD33_C2_INSOLUBLE_01 Peptides AD34_C1_INSOLUBLE_01 Peptides AD34_C1_INSOLUBLE_02 Peptides AD35_C1_INSOLUBLE_01 Peptides AD35_C1_INSOLUBLE_02 Peptides AD36_C1_INSOLUBLE_01 Peptides AD37_C1_INSOLUBLE_01 Peptides AD37_C2_INSOLUBLE_01 Peptides AD38_C1_INSOLUBLE_01 Peptides AD38_C1_INSOLUBLE_02 Peptides AD38_C1_INSOLUBLE_03 Peptides AD39_C2_INSOLUBLE_01 Peptides AD40_C2_INSOLUBLE_01 Peptides AD41_C2_INSOLUBLE_01 Peptides AD42_C2_INSOLUBLE_01 Peptides AD43_C1_INSOLUBLE_01 Peptides AD44_C1_INSOLUBLE_01 Peptides AD44_C1_INSOLUBLE_02 Peptides AD44_C1_INSOLUBLE_03 Peptides AD44_C1_INSOLUBLE_04 Peptides AD45_C1_INSOLUBLE_01 Peptides AD45_C1_INSOLUBLE_02 Peptides AD46_C1_INSOLUBLE_01 Peptides AD46_C1_INSOLUBLE_02 Peptides AD46_C1_INSOLUBLE_03 Peptides AD46_C2_INSOLUBLE_01 Peptides AD47_C1_INSOLUBLE_01 Peptides AD48_C2_INSOLUBLE_01 Peptides AD49_C2_INSOLUBLE_01 Peptides CTR01_C1_INSOLUBLE_01 Peptides CTR02_C1_INSOLUBLE_01 Peptides CTR03_C1_INSOLUBLE_01 Peptides CTR04_C1_INSOLUBLE_01 Peptides CTR05_C2_INSOLUBLE_01 Peptides CTR06_C2_INSOLUBLE_01 Peptides CTR07_C1_INSOLUBLE_01 Peptides CTR08_C1_INSOLUBLE_01 Peptides CTR08_C2_INSOLUBLE_01 Peptides CTR09_C2_INSOLUBLE_01 Peptides CTR10_C1_INSOLUBLE_01 Peptides CTR10_C2_INSOLUBLE_01 Peptides CTR11_C2_INSOLUBLE_01 Peptides CTR12_C2_INSOLUBLE_01 Peptides CTR13_C2_INSOLUBLE_01 Peptides CTR14_C2_INSOLUBLE_01 Peptides CTR15_C2_INSOLUBLE_01 Peptides CTR16_C2_INSOLUBLE_01 Peptides CTR17_C2_INSOLUBLE_01 Peptides CTR18_C2_INSOLUBLE_01 Peptides CTR19_C1_INSOLUBLE_01 Peptides CTR20_C1_INSOLUBLE_01 Peptides CTR21_C2_INSOLUBLE_01 Peptides CTR22_C2_INSOLUBLE_01 Peptides CTR23_C2_INSOLUBLE_01 Peptides CTR24_C1_INSOLUBLE_01 Peptides CTR25_C1_INSOLUBLE_01 Peptides CTR26_C2_INSOLUBLE_01 Peptides CTR27_C1_INSOLUBLE_01 Peptides CTR28_C1_INSOLUBLE_01 Peptides CTR28_C1_INSOLUBLE_02 Peptides CTR28_C2_INSOLUBLE_01 Peptides CTR29_C1_INSOLUBLE_01 Peptides CTR29_C1_INSOLUBLE_02 Peptides CTR29_C1_INSOLUBLE_03 Peptides CTR30_C1_INSOLUBLE_01 Peptides CTR30_C1_INSOLUBLE_02 Peptides CTR30_C2_INSOLUBLE_01 Peptides CTR31_C1_INSOLUBLE_01 Peptides CTR31_C2_INSOLUBLE_01 Peptides CTR32_C1_INSOLUBLE_01 Peptides CTR32_C2_INSOLUBLE_01 Peptides CTR33_C1_INSOLUBLE_01 Peptides CTR34_C1_INSOLUBLE_01 Peptides CTR34_C2_INSOLUBLE_01 Peptides CTR35_C1_INSOLUBLE_01 Peptides CTR36_C1_INSOLUBLE_01 Peptides CTR36_C1_INSOLUBLE_02 Peptides CTR37_C1_INSOLUBLE_01 Peptides CTR38_C1_INSOLUBLE_01 Peptides CTR39_C1_INSOLUBLE_01 Peptides CTR40_C1_INSOLUBLE_01 Peptides CTR40_C1_INSOLUBLE_02 Peptides CTR40_C1_INSOLUBLE_03 Peptides CTR41_C1_INSOLUBLE_01 Peptides CTR41_C1_INSOLUBLE_02 Peptides CTR41_C1_INSOLUBLE_03 Peptides CTR42_C1_INSOLUBLE_01 Peptides CTR42_C1_INSOLUBLE_02 Peptides CTR42_C1_INSOLUBLE_03 Peptides CTR43_C2_INSOLUBLE_01 Peptides CTR44_C1_INSOLUBLE_01 Razor + unique peptides AD01_C1_INSOLUBLE_01 Razor + unique peptides AD01_C1_INSOLUBLE_02 Razor + unique peptides AD01_C1_INSOLUBLE_03 Razor + unique peptides AD01_C2_INSOLUBLE_01 Razor + unique peptides AD02_C1_INSOLUBLE_01 Razor + unique peptides AD02_C1_INSOLUBLE_02 Razor + unique peptides AD02_C2_INSOLUBLE_01 Razor + unique peptides AD03_C1_INSOLUBLE_01 Razor + unique peptides AD03_C1_INSOLUBLE_02 Razor + unique peptides AD03_C1_INSOLUBLE_03 Razor + unique peptides AD03_C2_INSOLUBLE_01 Razor + unique peptides AD04_C1_INSOLUBLE_01 Razor + unique peptides AD04_C2_INSOLUBLE_01 Razor + unique peptides AD05_C2_INSOLUBLE_01 Razor + unique peptides AD06_C1_INSOLUBLE_01 Razor + unique peptides AD07_C1_INSOLUBLE_01 Razor + unique peptides AD07_C1_INSOLUBLE_02 Razor + unique peptides AD07_C1_INSOLUBLE_03 Razor + unique peptides AD07_C2_INSOLUBLE_01 Razor + unique peptides AD08_C2_INSOLUBLE_01 Razor + unique peptides AD09_C1_INSOLUBLE_01 Razor + unique peptides AD10_C1_INSOLUBLE_01 Razor + unique peptides AD10_C2_INSOLUBLE_01 Razor + unique peptides AD11_C2_INSOLUBLE_01 Razor + unique peptides AD12_C2_INSOLUBLE_01 Razor + unique peptides AD13_C2_INSOLUBLE_01 Razor + unique peptides AD14_C2_INSOLUBLE_01 Razor + unique peptides AD15_C2_INSOLUBLE_01 Razor + unique peptides AD16_C2_INSOLUBLE_01 Razor + unique peptides AD17_C2_INSOLUBLE_01 Razor + unique peptides AD18_C2_INSOLUBLE_01 Razor + unique peptides AD19_C2_INSOLUBLE_01 Razor + unique peptides AD20_C1_INSOLUBLE_01 Razor + unique peptides AD21_C1_INSOLUBLE_01 Razor + unique peptides AD21_C2_INSOLUBLE_01 Razor + unique peptides AD22_C1_INSOLUBLE_01 Razor + unique peptides AD23_C1_INSOLUBLE_01 Razor + unique peptides AD23_C1_INSOLUBLE_02 Razor + unique peptides AD23_C2_INSOLUBLE_01 Razor + unique peptides AD24_C1_INSOLUBLE_01 Razor + unique peptides AD24_C1_INSOLUBLE_02 Razor + unique peptides AD25_C1_INSOLUBLE_01 Razor + unique peptides AD26_C1_INSOLUBLE_01 Razor + unique peptides AD27_C1_INSOLUBLE_01 Razor + unique peptides AD27_C1_INSOLUBLE_02 Razor + unique peptides AD28_C2_INSOLUBLE_01 Razor + unique peptides AD29_C1_INSOLUBLE_01 Razor + unique peptides AD30_C1_INSOLUBLE_01 Razor + unique peptides AD30_C1_INSOLUBLE_02 Razor + unique peptides AD30_C2_INSOLUBLE_01 Razor + unique peptides AD31_C2_INSOLUBLE_01 Razor + unique peptides AD32_C2_INSOLUBLE_01 Razor + unique peptides AD33_C2_INSOLUBLE_01 Razor + unique peptides AD34_C1_INSOLUBLE_01 Razor + unique peptides AD34_C1_INSOLUBLE_02 Razor + unique peptides AD35_C1_INSOLUBLE_01 Razor + unique peptides AD35_C1_INSOLUBLE_02 Razor + unique peptides AD36_C1_INSOLUBLE_01 Razor + unique peptides AD37_C1_INSOLUBLE_01 Razor + unique peptides AD37_C2_INSOLUBLE_01 Razor + unique peptides AD38_C1_INSOLUBLE_01 Razor + unique peptides AD38_C1_INSOLUBLE_02 Razor + unique peptides AD38_C1_INSOLUBLE_03 Razor + unique peptides AD39_C2_INSOLUBLE_01 Razor + unique peptides AD40_C2_INSOLUBLE_01 Razor + unique peptides AD41_C2_INSOLUBLE_01 Razor + unique peptides AD42_C2_INSOLUBLE_01 Razor + unique peptides AD43_C1_INSOLUBLE_01 Razor + unique peptides AD44_C1_INSOLUBLE_01 Razor + unique peptides AD44_C1_INSOLUBLE_02 Razor + unique peptides AD44_C1_INSOLUBLE_03 Razor + unique peptides AD44_C1_INSOLUBLE_04 Razor + unique peptides AD45_C1_INSOLUBLE_01 Razor + unique peptides AD45_C1_INSOLUBLE_02 Razor + unique peptides AD46_C1_INSOLUBLE_01 Razor + unique peptides AD46_C1_INSOLUBLE_02 Razor + unique peptides AD46_C1_INSOLUBLE_03 Razor + unique peptides AD46_C2_INSOLUBLE_01 Razor + unique peptides AD47_C1_INSOLUBLE_01 Razor + unique peptides AD48_C2_INSOLUBLE_01 Razor + unique peptides AD49_C2_INSOLUBLE_01 Razor + unique peptides CTR01_C1_INSOLUBLE_01 Razor + unique peptides CTR02_C1_INSOLUBLE_01 Razor + unique peptides CTR03_C1_INSOLUBLE_01 Razor + unique peptides CTR04_C1_INSOLUBLE_01 Razor + unique peptides CTR05_C2_INSOLUBLE_01 Razor + unique peptides CTR06_C2_INSOLUBLE_01 Razor + unique peptides CTR07_C1_INSOLUBLE_01 Razor + unique peptides CTR08_C1_INSOLUBLE_01 Razor + unique peptides CTR08_C2_INSOLUBLE_01 Razor + unique peptides CTR09_C2_INSOLUBLE_01 Razor + unique peptides CTR10_C1_INSOLUBLE_01 Razor + unique peptides CTR10_C2_INSOLUBLE_01 Razor + unique peptides CTR11_C2_INSOLUBLE_01 Razor + unique peptides CTR12_C2_INSOLUBLE_01 Razor + unique peptides CTR13_C2_INSOLUBLE_01 Razor + unique peptides CTR14_C2_INSOLUBLE_01 Razor + unique peptides CTR15_C2_INSOLUBLE_01 Razor + unique peptides CTR16_C2_INSOLUBLE_01 Razor + unique peptides CTR17_C2_INSOLUBLE_01 Razor + unique peptides CTR18_C2_INSOLUBLE_01 Razor + unique peptides CTR19_C1_INSOLUBLE_01 Razor + unique peptides CTR20_C1_INSOLUBLE_01 Razor + unique peptides CTR21_C2_INSOLUBLE_01 Razor + unique peptides CTR22_C2_INSOLUBLE_01 Razor + unique peptides CTR23_C2_INSOLUBLE_01 Razor + unique peptides CTR24_C1_INSOLUBLE_01 Razor + unique peptides CTR25_C1_INSOLUBLE_01 Razor + unique peptides CTR26_C2_INSOLUBLE_01 Razor + unique peptides CTR27_C1_INSOLUBLE_01 Razor + unique peptides CTR28_C1_INSOLUBLE_01 Razor + unique peptides CTR28_C1_INSOLUBLE_02 Razor + unique peptides CTR28_C2_INSOLUBLE_01 Razor + unique peptides CTR29_C1_INSOLUBLE_01 Razor + unique peptides CTR29_C1_INSOLUBLE_02 Razor + unique peptides CTR29_C1_INSOLUBLE_03 Razor + unique peptides CTR30_C1_INSOLUBLE_01 Razor + unique peptides CTR30_C1_INSOLUBLE_02 Razor + unique peptides CTR30_C2_INSOLUBLE_01 Razor + unique peptides CTR31_C1_INSOLUBLE_01 Razor + unique peptides CTR31_C2_INSOLUBLE_01 Razor + unique peptides CTR32_C1_INSOLUBLE_01 Razor + unique peptides CTR32_C2_INSOLUBLE_01 Razor + unique peptides CTR33_C1_INSOLUBLE_01 Razor + unique peptides CTR34_C1_INSOLUBLE_01 Razor + unique peptides CTR34_C2_INSOLUBLE_01 Razor + unique peptides CTR35_C1_INSOLUBLE_01 Razor + unique peptides CTR36_C1_INSOLUBLE_01 Razor + unique peptides CTR36_C1_INSOLUBLE_02 Razor + unique peptides CTR37_C1_INSOLUBLE_01 Razor + unique peptides CTR38_C1_INSOLUBLE_01 Razor + unique peptides CTR39_C1_INSOLUBLE_01 Razor + unique peptides CTR40_C1_INSOLUBLE_01 Razor + unique peptides CTR40_C1_INSOLUBLE_02 Razor + unique peptides CTR40_C1_INSOLUBLE_03 Razor + unique peptides CTR41_C1_INSOLUBLE_01 Razor + unique peptides CTR41_C1_INSOLUBLE_02 Razor + unique peptides CTR41_C1_INSOLUBLE_03 Razor + unique peptides CTR42_C1_INSOLUBLE_01 Razor + unique peptides CTR42_C1_INSOLUBLE_02 Razor + unique peptides CTR42_C1_INSOLUBLE_03 Razor + unique peptides CTR43_C2_INSOLUBLE_01 Razor + unique peptides CTR44_C1_INSOLUBLE_01 Unique peptides AD01_C1_INSOLUBLE_01 Unique peptides AD01_C1_INSOLUBLE_02 Unique peptides AD01_C1_INSOLUBLE_03 Unique peptides AD01_C2_INSOLUBLE_01 Unique peptides AD02_C1_INSOLUBLE_01 Unique peptides AD02_C1_INSOLUBLE_02 Unique peptides AD02_C2_INSOLUBLE_01 Unique peptides AD03_C1_INSOLUBLE_01 Unique peptides AD03_C1_INSOLUBLE_02 Unique peptides AD03_C1_INSOLUBLE_03 Unique peptides AD03_C2_INSOLUBLE_01 Unique peptides AD04_C1_INSOLUBLE_01 Unique peptides AD04_C2_INSOLUBLE_01 Unique peptides AD05_C2_INSOLUBLE_01 Unique peptides AD06_C1_INSOLUBLE_01 Unique peptides AD07_C1_INSOLUBLE_01 Unique peptides AD07_C1_INSOLUBLE_02 Unique peptides AD07_C1_INSOLUBLE_03 Unique peptides AD07_C2_INSOLUBLE_01 Unique peptides AD08_C2_INSOLUBLE_01 Unique peptides AD09_C1_INSOLUBLE_01 Unique peptides AD10_C1_INSOLUBLE_01 Unique peptides AD10_C2_INSOLUBLE_01 Unique peptides AD11_C2_INSOLUBLE_01 Unique peptides AD12_C2_INSOLUBLE_01 Unique peptides AD13_C2_INSOLUBLE_01 Unique peptides AD14_C2_INSOLUBLE_01 Unique peptides AD15_C2_INSOLUBLE_01 Unique peptides AD16_C2_INSOLUBLE_01 Unique peptides AD17_C2_INSOLUBLE_01 Unique peptides AD18_C2_INSOLUBLE_01 Unique peptides AD19_C2_INSOLUBLE_01 Unique peptides AD20_C1_INSOLUBLE_01 Unique peptides AD21_C1_INSOLUBLE_01 Unique peptides AD21_C2_INSOLUBLE_01 Unique peptides AD22_C1_INSOLUBLE_01 Unique peptides AD23_C1_INSOLUBLE_01 Unique peptides AD23_C1_INSOLUBLE_02 Unique peptides AD23_C2_INSOLUBLE_01 Unique peptides AD24_C1_INSOLUBLE_01 Unique peptides AD24_C1_INSOLUBLE_02 Unique peptides AD25_C1_INSOLUBLE_01 Unique peptides AD26_C1_INSOLUBLE_01 Unique peptides AD27_C1_INSOLUBLE_01 Unique peptides AD27_C1_INSOLUBLE_02 Unique peptides AD28_C2_INSOLUBLE_01 Unique peptides AD29_C1_INSOLUBLE_01 Unique peptides AD30_C1_INSOLUBLE_01 Unique peptides AD30_C1_INSOLUBLE_02 Unique peptides AD30_C2_INSOLUBLE_01 Unique peptides AD31_C2_INSOLUBLE_01 Unique peptides AD32_C2_INSOLUBLE_01 Unique peptides AD33_C2_INSOLUBLE_01 Unique peptides AD34_C1_INSOLUBLE_01 Unique peptides AD34_C1_INSOLUBLE_02 Unique peptides AD35_C1_INSOLUBLE_01 Unique peptides AD35_C1_INSOLUBLE_02 Unique peptides AD36_C1_INSOLUBLE_01 Unique peptides AD37_C1_INSOLUBLE_01 Unique peptides AD37_C2_INSOLUBLE_01 Unique peptides AD38_C1_INSOLUBLE_01 Unique peptides AD38_C1_INSOLUBLE_02 Unique peptides AD38_C1_INSOLUBLE_03 Unique peptides AD39_C2_INSOLUBLE_01 Unique peptides AD40_C2_INSOLUBLE_01 Unique peptides AD41_C2_INSOLUBLE_01 Unique peptides AD42_C2_INSOLUBLE_01 Unique peptides AD43_C1_INSOLUBLE_01 Unique peptides AD44_C1_INSOLUBLE_01 Unique peptides AD44_C1_INSOLUBLE_02 Unique peptides AD44_C1_INSOLUBLE_03 Unique peptides AD44_C1_INSOLUBLE_04 Unique peptides AD45_C1_INSOLUBLE_01 Unique peptides AD45_C1_INSOLUBLE_02 Unique peptides AD46_C1_INSOLUBLE_01 Unique peptides AD46_C1_INSOLUBLE_02 Unique peptides AD46_C1_INSOLUBLE_03 Unique peptides AD46_C2_INSOLUBLE_01 Unique peptides AD47_C1_INSOLUBLE_01 Unique peptides AD48_C2_INSOLUBLE_01 Unique peptides AD49_C2_INSOLUBLE_01 Unique peptides CTR01_C1_INSOLUBLE_01 Unique peptides CTR02_C1_INSOLUBLE_01 Unique peptides CTR03_C1_INSOLUBLE_01 Unique peptides CTR04_C1_INSOLUBLE_01 Unique peptides CTR05_C2_INSOLUBLE_01 Unique peptides CTR06_C2_INSOLUBLE_01 Unique peptides CTR07_C1_INSOLUBLE_01 Unique peptides CTR08_C1_INSOLUBLE_01 Unique peptides CTR08_C2_INSOLUBLE_01 Unique peptides CTR09_C2_INSOLUBLE_01 Unique peptides CTR10_C1_INSOLUBLE_01 Unique peptides CTR10_C2_INSOLUBLE_01 Unique peptides CTR11_C2_INSOLUBLE_01 Unique peptides CTR12_C2_INSOLUBLE_01 Unique peptides CTR13_C2_INSOLUBLE_01 Unique peptides CTR14_C2_INSOLUBLE_01 Unique peptides CTR15_C2_INSOLUBLE_01 Unique peptides CTR16_C2_INSOLUBLE_01 Unique peptides CTR17_C2_INSOLUBLE_01 Unique peptides CTR18_C2_INSOLUBLE_01 Unique peptides CTR19_C1_INSOLUBLE_01 Unique peptides CTR20_C1_INSOLUBLE_01 Unique peptides CTR21_C2_INSOLUBLE_01 Unique peptides CTR22_C2_INSOLUBLE_01 Unique peptides CTR23_C2_INSOLUBLE_01 Unique peptides CTR24_C1_INSOLUBLE_01 Unique peptides CTR25_C1_INSOLUBLE_01 Unique peptides CTR26_C2_INSOLUBLE_01 Unique peptides CTR27_C1_INSOLUBLE_01 Unique peptides CTR28_C1_INSOLUBLE_01 Unique peptides CTR28_C1_INSOLUBLE_02 Unique peptides CTR28_C2_INSOLUBLE_01 Unique peptides CTR29_C1_INSOLUBLE_01 Unique peptides CTR29_C1_INSOLUBLE_02 Unique peptides CTR29_C1_INSOLUBLE_03 Unique peptides CTR30_C1_INSOLUBLE_01 Unique peptides CTR30_C1_INSOLUBLE_02 Unique peptides CTR30_C2_INSOLUBLE_01 Unique peptides CTR31_C1_INSOLUBLE_01 Unique peptides CTR31_C2_INSOLUBLE_01 Unique peptides CTR32_C1_INSOLUBLE_01 Unique peptides CTR32_C2_INSOLUBLE_01 Unique peptides CTR33_C1_INSOLUBLE_01 Unique peptides CTR34_C1_INSOLUBLE_01 Unique peptides CTR34_C2_INSOLUBLE_01 Unique peptides CTR35_C1_INSOLUBLE_01 Unique peptides CTR36_C1_INSOLUBLE_01 Unique peptides CTR36_C1_INSOLUBLE_02 Unique peptides CTR37_C1_INSOLUBLE_01 Unique peptides CTR38_C1_INSOLUBLE_01 Unique peptides CTR39_C1_INSOLUBLE_01 Unique peptides CTR40_C1_INSOLUBLE_01 Unique peptides CTR40_C1_INSOLUBLE_02 Unique peptides CTR40_C1_INSOLUBLE_03 Unique peptides CTR41_C1_INSOLUBLE_01 Unique peptides CTR41_C1_INSOLUBLE_02 Unique peptides CTR41_C1_INSOLUBLE_03 Unique peptides CTR42_C1_INSOLUBLE_01 Unique peptides CTR42_C1_INSOLUBLE_02 Unique peptides CTR42_C1_INSOLUBLE_03 Unique peptides CTR43_C2_INSOLUBLE_01 Unique peptides CTR44_C1_INSOLUBLE_01 Sequence coverage [%] Unique + razor sequence coverage [%] Unique sequence coverage [%] Mol. weight [kDa] Sequence length Sequence lengths Q-value Score Identification type AD01_C1_INSOLUBLE_01 Identification type AD01_C1_INSOLUBLE_02 Identification type AD01_C1_INSOLUBLE_03 Identification type AD01_C2_INSOLUBLE_01 Identification type AD02_C1_INSOLUBLE_01 Identification type AD02_C1_INSOLUBLE_02 Identification type AD02_C2_INSOLUBLE_01 Identification type AD03_C1_INSOLUBLE_01 Identification type AD03_C1_INSOLUBLE_02 Identification type AD03_C1_INSOLUBLE_03 Identification type AD03_C2_INSOLUBLE_01 Identification type AD04_C1_INSOLUBLE_01 Identification type AD04_C2_INSOLUBLE_01 Identification type AD05_C2_INSOLUBLE_01 Identification type AD06_C1_INSOLUBLE_01 Identification type AD07_C1_INSOLUBLE_01 Identification type AD07_C1_INSOLUBLE_02 Identification type AD07_C1_INSOLUBLE_03 Identification type AD07_C2_INSOLUBLE_01 Identification type AD08_C2_INSOLUBLE_01 Identification type AD09_C1_INSOLUBLE_01 Identification type AD10_C1_INSOLUBLE_01 Identification type AD10_C2_INSOLUBLE_01 Identification type AD11_C2_INSOLUBLE_01 Identification type AD12_C2_INSOLUBLE_01 Identification type AD13_C2_INSOLUBLE_01 Identification type AD14_C2_INSOLUBLE_01 Identification type AD15_C2_INSOLUBLE_01 Identification type AD16_C2_INSOLUBLE_01 Identification type AD17_C2_INSOLUBLE_01 Identification type AD18_C2_INSOLUBLE_01 Identification type AD19_C2_INSOLUBLE_01 Identification type AD20_C1_INSOLUBLE_01 Identification type AD21_C1_INSOLUBLE_01 Identification type AD21_C2_INSOLUBLE_01 Identification type AD22_C1_INSOLUBLE_01 Identification type AD23_C1_INSOLUBLE_01 Identification type AD23_C1_INSOLUBLE_02 Identification type AD23_C2_INSOLUBLE_01 Identification type AD24_C1_INSOLUBLE_01 Identification type AD24_C1_INSOLUBLE_02 Identification type AD25_C1_INSOLUBLE_01 Identification type AD26_C1_INSOLUBLE_01 Identification type AD27_C1_INSOLUBLE_01 Identification type AD27_C1_INSOLUBLE_02 Identification type AD28_C2_INSOLUBLE_01 Identification type AD29_C1_INSOLUBLE_01 Identification type AD30_C1_INSOLUBLE_01 Identification type AD30_C1_INSOLUBLE_02 Identification type AD30_C2_INSOLUBLE_01 Identification type AD31_C2_INSOLUBLE_01 Identification type AD32_C2_INSOLUBLE_01 Identification type AD33_C2_INSOLUBLE_01 Identification type AD34_C1_INSOLUBLE_01 Identification type AD34_C1_INSOLUBLE_02 Identification type AD35_C1_INSOLUBLE_01 Identification type AD35_C1_INSOLUBLE_02 Identification type AD36_C1_INSOLUBLE_01 Identification type AD37_C1_INSOLUBLE_01 Identification type AD37_C2_INSOLUBLE_01 Identification type AD38_C1_INSOLUBLE_01 Identification type AD38_C1_INSOLUBLE_02 Identification type AD38_C1_INSOLUBLE_03 Identification type AD39_C2_INSOLUBLE_01 Identification type AD40_C2_INSOLUBLE_01 Identification type AD41_C2_INSOLUBLE_01 Identification type AD42_C2_INSOLUBLE_01 Identification type AD43_C1_INSOLUBLE_01 Identification type AD44_C1_INSOLUBLE_01 Identification type AD44_C1_INSOLUBLE_02 Identification type AD44_C1_INSOLUBLE_03 Identification type AD44_C1_INSOLUBLE_04 Identification type AD45_C1_INSOLUBLE_01 Identification type AD45_C1_INSOLUBLE_02 Identification type AD46_C1_INSOLUBLE_01 Identification type AD46_C1_INSOLUBLE_02 Identification type AD46_C1_INSOLUBLE_03 Identification type AD46_C2_INSOLUBLE_01 Identification type AD47_C1_INSOLUBLE_01 Identification type AD48_C2_INSOLUBLE_01 Identification type AD49_C2_INSOLUBLE_01 Identification type CTR01_C1_INSOLUBLE_01 Identification type CTR02_C1_INSOLUBLE_01 Identification type CTR03_C1_INSOLUBLE_01 Identification type CTR04_C1_INSOLUBLE_01 Identification type CTR05_C2_INSOLUBLE_01 Identification type CTR06_C2_INSOLUBLE_01 Identification type CTR07_C1_INSOLUBLE_01 Identification type CTR08_C1_INSOLUBLE_01 Identification type CTR08_C2_INSOLUBLE_01 Identification type CTR09_C2_INSOLUBLE_01 Identification type CTR10_C1_INSOLUBLE_01 Identification type CTR10_C2_INSOLUBLE_01 Identification type CTR11_C2_INSOLUBLE_01 Identification type CTR12_C2_INSOLUBLE_01 Identification type CTR13_C2_INSOLUBLE_01 Identification type CTR14_C2_INSOLUBLE_01 Identification type CTR15_C2_INSOLUBLE_01 Identification type CTR16_C2_INSOLUBLE_01 Identification type CTR17_C2_INSOLUBLE_01 Identification type CTR18_C2_INSOLUBLE_01 Identification type CTR19_C1_INSOLUBLE_01 Identification type CTR20_C1_INSOLUBLE_01 Identification type CTR21_C2_INSOLUBLE_01 Identification type CTR22_C2_INSOLUBLE_01 Identification type CTR23_C2_INSOLUBLE_01 Identification type CTR24_C1_INSOLUBLE_01 Identification type CTR25_C1_INSOLUBLE_01 Identification type CTR26_C2_INSOLUBLE_01 Identification type CTR27_C1_INSOLUBLE_01 Identification type CTR28_C1_INSOLUBLE_01 Identification type CTR28_C1_INSOLUBLE_02 Identification type CTR28_C2_INSOLUBLE_01 Identification type CTR29_C1_INSOLUBLE_01 Identification type CTR29_C1_INSOLUBLE_02 Identification type CTR29_C1_INSOLUBLE_03 Identification type CTR30_C1_INSOLUBLE_01 Identification type CTR30_C1_INSOLUBLE_02 Identification type CTR30_C2_INSOLUBLE_01 Identification type CTR31_C1_INSOLUBLE_01 Identification type CTR31_C2_INSOLUBLE_01 Identification type CTR32_C1_INSOLUBLE_01 Identification type CTR32_C2_INSOLUBLE_01 Identification type CTR33_C1_INSOLUBLE_01 Identification type CTR34_C1_INSOLUBLE_01 Identification type CTR34_C2_INSOLUBLE_01 Identification type CTR35_C1_INSOLUBLE_01 Identification type CTR36_C1_INSOLUBLE_01 Identification type CTR36_C1_INSOLUBLE_02 Identification type CTR37_C1_INSOLUBLE_01 Identification type CTR38_C1_INSOLUBLE_01 Identification type CTR39_C1_INSOLUBLE_01 Identification type CTR40_C1_INSOLUBLE_01 Identification type CTR40_C1_INSOLUBLE_02 Identification type CTR40_C1_INSOLUBLE_03 Identification type CTR41_C1_INSOLUBLE_01 Identification type CTR41_C1_INSOLUBLE_02 Identification type CTR41_C1_INSOLUBLE_03 Identification type CTR42_C1_INSOLUBLE_01 Identification type CTR42_C1_INSOLUBLE_02 Identification type CTR42_C1_INSOLUBLE_03 Identification type CTR43_C2_INSOLUBLE_01 Identification type CTR44_C1_INSOLUBLE_01 Sequence coverage AD01_C1_INSOLUBLE_01 [%] Sequence coverage AD01_C1_INSOLUBLE_02 [%] Sequence coverage AD01_C1_INSOLUBLE_03 [%] Sequence coverage AD01_C2_INSOLUBLE_01 [%] Sequence coverage AD02_C1_INSOLUBLE_01 [%] Sequence coverage AD02_C1_INSOLUBLE_02 [%] Sequence coverage AD02_C2_INSOLUBLE_01 [%] Sequence coverage AD03_C1_INSOLUBLE_01 [%] Sequence coverage AD03_C1_INSOLUBLE_02 [%] Sequence coverage AD03_C1_INSOLUBLE_03 [%] Sequence coverage AD03_C2_INSOLUBLE_01 [%] Sequence coverage AD04_C1_INSOLUBLE_01 [%] Sequence coverage AD04_C2_INSOLUBLE_01 [%] Sequence coverage AD05_C2_INSOLUBLE_01 [%] Sequence coverage AD06_C1_INSOLUBLE_01 [%] Sequence coverage AD07_C1_INSOLUBLE_01 [%] Sequence coverage AD07_C1_INSOLUBLE_02 [%] Sequence coverage AD07_C1_INSOLUBLE_03 [%] Sequence coverage AD07_C2_INSOLUBLE_01 [%] Sequence coverage AD08_C2_INSOLUBLE_01 [%] Sequence coverage AD09_C1_INSOLUBLE_01 [%] Sequence coverage AD10_C1_INSOLUBLE_01 [%] Sequence coverage AD10_C2_INSOLUBLE_01 [%] Sequence coverage AD11_C2_INSOLUBLE_01 [%] Sequence coverage AD12_C2_INSOLUBLE_01 [%] Sequence coverage AD13_C2_INSOLUBLE_01 [%] Sequence coverage AD14_C2_INSOLUBLE_01 [%] Sequence coverage AD15_C2_INSOLUBLE_01 [%] Sequence coverage AD16_C2_INSOLUBLE_01 [%] Sequence coverage AD17_C2_INSOLUBLE_01 [%] Sequence coverage AD18_C2_INSOLUBLE_01 [%] Sequence coverage AD19_C2_INSOLUBLE_01 [%] Sequence coverage AD20_C1_INSOLUBLE_01 [%] Sequence coverage AD21_C1_INSOLUBLE_01 [%] Sequence coverage AD21_C2_INSOLUBLE_01 [%] Sequence coverage AD22_C1_INSOLUBLE_01 [%] Sequence coverage AD23_C1_INSOLUBLE_01 [%] Sequence coverage AD23_C1_INSOLUBLE_02 [%] Sequence coverage AD23_C2_INSOLUBLE_01 [%] Sequence coverage AD24_C1_INSOLUBLE_01 [%] Sequence coverage AD24_C1_INSOLUBLE_02 [%] Sequence coverage AD25_C1_INSOLUBLE_01 [%] Sequence coverage AD26_C1_INSOLUBLE_01 [%] Sequence coverage AD27_C1_INSOLUBLE_01 [%] Sequence coverage AD27_C1_INSOLUBLE_02 [%] Sequence coverage AD28_C2_INSOLUBLE_01 [%] Sequence coverage AD29_C1_INSOLUBLE_01 [%] Sequence coverage AD30_C1_INSOLUBLE_01 [%] Sequence coverage AD30_C1_INSOLUBLE_02 [%] Sequence coverage AD30_C2_INSOLUBLE_01 [%] Sequence coverage AD31_C2_INSOLUBLE_01 [%] Sequence coverage AD32_C2_INSOLUBLE_01 [%] Sequence coverage AD33_C2_INSOLUBLE_01 [%] Sequence coverage AD34_C1_INSOLUBLE_01 [%] Sequence coverage AD34_C1_INSOLUBLE_02 [%] Sequence coverage AD35_C1_INSOLUBLE_01 [%] Sequence coverage AD35_C1_INSOLUBLE_02 [%] Sequence coverage AD36_C1_INSOLUBLE_01 [%] Sequence coverage AD37_C1_INSOLUBLE_01 [%] Sequence coverage AD37_C2_INSOLUBLE_01 [%] Sequence coverage AD38_C1_INSOLUBLE_01 [%] Sequence coverage AD38_C1_INSOLUBLE_02 [%] Sequence coverage AD38_C1_INSOLUBLE_03 [%] Sequence coverage AD39_C2_INSOLUBLE_01 [%] Sequence coverage AD40_C2_INSOLUBLE_01 [%] Sequence coverage AD41_C2_INSOLUBLE_01 [%] Sequence coverage AD42_C2_INSOLUBLE_01 [%] Sequence coverage AD43_C1_INSOLUBLE_01 [%] Sequence coverage AD44_C1_INSOLUBLE_01 [%] Sequence coverage AD44_C1_INSOLUBLE_02 [%] Sequence coverage AD44_C1_INSOLUBLE_03 [%] Sequence coverage AD44_C1_INSOLUBLE_04 [%] Sequence coverage AD45_C1_INSOLUBLE_01 [%] Sequence coverage AD45_C1_INSOLUBLE_02 [%] Sequence coverage AD46_C1_INSOLUBLE_01 [%] Sequence coverage AD46_C1_INSOLUBLE_02 [%] Sequence coverage AD46_C1_INSOLUBLE_03 [%] Sequence coverage AD46_C2_INSOLUBLE_01 [%] Sequence coverage AD47_C1_INSOLUBLE_01 [%] Sequence coverage AD48_C2_INSOLUBLE_01 [%] Sequence coverage AD49_C2_INSOLUBLE_01 [%] Sequence coverage CTR01_C1_INSOLUBLE_01 [%] Sequence coverage CTR02_C1_INSOLUBLE_01 [%] Sequence coverage CTR03_C1_INSOLUBLE_01 [%] Sequence coverage CTR04_C1_INSOLUBLE_01 [%] Sequence coverage CTR05_C2_INSOLUBLE_01 [%] Sequence coverage CTR06_C2_INSOLUBLE_01 [%] Sequence coverage CTR07_C1_INSOLUBLE_01 [%] Sequence coverage CTR08_C1_INSOLUBLE_01 [%] Sequence coverage CTR08_C2_INSOLUBLE_01 [%] Sequence coverage CTR09_C2_INSOLUBLE_01 [%] Sequence coverage CTR10_C1_INSOLUBLE_01 [%] Sequence coverage CTR10_C2_INSOLUBLE_01 [%] Sequence coverage CTR11_C2_INSOLUBLE_01 [%] Sequence coverage CTR12_C2_INSOLUBLE_01 [%] Sequence coverage CTR13_C2_INSOLUBLE_01 [%] Sequence coverage CTR14_C2_INSOLUBLE_01 [%] Sequence coverage CTR15_C2_INSOLUBLE_01 [%] Sequence coverage CTR16_C2_INSOLUBLE_01 [%] Sequence coverage CTR17_C2_INSOLUBLE_01 [%] Sequence coverage CTR18_C2_INSOLUBLE_01 [%] Sequence coverage CTR19_C1_INSOLUBLE_01 [%] Sequence coverage CTR20_C1_INSOLUBLE_01 [%] Sequence coverage CTR21_C2_INSOLUBLE_01 [%] Sequence coverage CTR22_C2_INSOLUBLE_01 [%] Sequence coverage CTR23_C2_INSOLUBLE_01 [%] Sequence coverage CTR24_C1_INSOLUBLE_01 [%] Sequence coverage CTR25_C1_INSOLUBLE_01 [%] Sequence coverage CTR26_C2_INSOLUBLE_01 [%] Sequence coverage CTR27_C1_INSOLUBLE_01 [%] Sequence coverage CTR28_C1_INSOLUBLE_01 [%] Sequence coverage CTR28_C1_INSOLUBLE_02 [%] Sequence coverage CTR28_C2_INSOLUBLE_01 [%] Sequence coverage CTR29_C1_INSOLUBLE_01 [%] Sequence coverage CTR29_C1_INSOLUBLE_02 [%] Sequence coverage CTR29_C1_INSOLUBLE_03 [%] Sequence coverage CTR30_C1_INSOLUBLE_01 [%] Sequence coverage CTR30_C1_INSOLUBLE_02 [%] Sequence coverage CTR30_C2_INSOLUBLE_01 [%] Sequence coverage CTR31_C1_INSOLUBLE_01 [%] Sequence coverage CTR31_C2_INSOLUBLE_01 [%] Sequence coverage CTR32_C1_INSOLUBLE_01 [%] Sequence coverage CTR32_C2_INSOLUBLE_01 [%] Sequence coverage CTR33_C1_INSOLUBLE_01 [%] Sequence coverage CTR34_C1_INSOLUBLE_01 [%] Sequence coverage CTR34_C2_INSOLUBLE_01 [%] Sequence coverage CTR35_C1_INSOLUBLE_01 [%] Sequence coverage CTR36_C1_INSOLUBLE_01 [%] Sequence coverage CTR36_C1_INSOLUBLE_02 [%] Sequence coverage CTR37_C1_INSOLUBLE_01 [%] Sequence coverage CTR38_C1_INSOLUBLE_01 [%] Sequence coverage CTR39_C1_INSOLUBLE_01 [%] Sequence coverage CTR40_C1_INSOLUBLE_01 [%] Sequence coverage CTR40_C1_INSOLUBLE_02 [%] Sequence coverage CTR40_C1_INSOLUBLE_03 [%] Sequence coverage CTR41_C1_INSOLUBLE_01 [%] Sequence coverage CTR41_C1_INSOLUBLE_02 [%] Sequence coverage CTR41_C1_INSOLUBLE_03 [%] Sequence coverage CTR42_C1_INSOLUBLE_01 [%] Sequence coverage CTR42_C1_INSOLUBLE_02 [%] Sequence coverage CTR42_C1_INSOLUBLE_03 [%] Sequence coverage CTR43_C2_INSOLUBLE_01 [%] Sequence coverage CTR44_C1_INSOLUBLE_01 [%] Intensity Intensity AD01_C1_INSOLUBLE_01 Intensity AD01_C1_INSOLUBLE_02 Intensity AD01_C1_INSOLUBLE_03 Intensity AD01_C2_INSOLUBLE_01 Intensity AD02_C1_INSOLUBLE_01 Intensity AD02_C1_INSOLUBLE_02 Intensity AD02_C2_INSOLUBLE_01 Intensity AD03_C1_INSOLUBLE_01 Intensity AD03_C1_INSOLUBLE_02 Intensity AD03_C1_INSOLUBLE_03 Intensity AD03_C2_INSOLUBLE_01 Intensity AD04_C1_INSOLUBLE_01 Intensity AD04_C2_INSOLUBLE_01 Intensity AD05_C2_INSOLUBLE_01 Intensity AD06_C1_INSOLUBLE_01 Intensity AD07_C1_INSOLUBLE_01 Intensity AD07_C1_INSOLUBLE_02 Intensity AD07_C1_INSOLUBLE_03 Intensity AD07_C2_INSOLUBLE_01 Intensity AD08_C2_INSOLUBLE_01 Intensity AD09_C1_INSOLUBLE_01 Intensity AD10_C1_INSOLUBLE_01 Intensity AD10_C2_INSOLUBLE_01 Intensity AD11_C2_INSOLUBLE_01 Intensity AD12_C2_INSOLUBLE_01 Intensity AD13_C2_INSOLUBLE_01 Intensity AD14_C2_INSOLUBLE_01 Intensity AD15_C2_INSOLUBLE_01 Intensity AD16_C2_INSOLUBLE_01 Intensity AD17_C2_INSOLUBLE_01 Intensity AD18_C2_INSOLUBLE_01 Intensity AD19_C2_INSOLUBLE_01 Intensity AD20_C1_INSOLUBLE_01 Intensity AD21_C1_INSOLUBLE_01 Intensity AD21_C2_INSOLUBLE_01 Intensity AD22_C1_INSOLUBLE_01 Intensity AD23_C1_INSOLUBLE_01 Intensity AD23_C1_INSOLUBLE_02 Intensity AD23_C2_INSOLUBLE_01 Intensity AD24_C1_INSOLUBLE_01 Intensity AD24_C1_INSOLUBLE_02 Intensity AD25_C1_INSOLUBLE_01 Intensity AD26_C1_INSOLUBLE_01 Intensity AD27_C1_INSOLUBLE_01 Intensity AD27_C1_INSOLUBLE_02 Intensity AD28_C2_INSOLUBLE_01 Intensity AD29_C1_INSOLUBLE_01 Intensity AD30_C1_INSOLUBLE_01 Intensity AD30_C1_INSOLUBLE_02 Intensity AD30_C2_INSOLUBLE_01 Intensity AD31_C2_INSOLUBLE_01 Intensity AD32_C2_INSOLUBLE_01 Intensity AD33_C2_INSOLUBLE_01 Intensity AD34_C1_INSOLUBLE_01 Intensity AD34_C1_INSOLUBLE_02 Intensity AD35_C1_INSOLUBLE_01 Intensity AD35_C1_INSOLUBLE_02 Intensity AD36_C1_INSOLUBLE_01 Intensity AD37_C1_INSOLUBLE_01 Intensity AD37_C2_INSOLUBLE_01 Intensity AD38_C1_INSOLUBLE_01 Intensity AD38_C1_INSOLUBLE_02 Intensity AD38_C1_INSOLUBLE_03 Intensity AD39_C2_INSOLUBLE_01 Intensity AD40_C2_INSOLUBLE_01 Intensity AD41_C2_INSOLUBLE_01 Intensity AD42_C2_INSOLUBLE_01 Intensity AD43_C1_INSOLUBLE_01 Intensity AD44_C1_INSOLUBLE_01 Intensity AD44_C1_INSOLUBLE_02 Intensity AD44_C1_INSOLUBLE_03 Intensity AD44_C1_INSOLUBLE_04 Intensity AD45_C1_INSOLUBLE_01 Intensity AD45_C1_INSOLUBLE_02 Intensity AD46_C1_INSOLUBLE_01 Intensity AD46_C1_INSOLUBLE_02 Intensity AD46_C1_INSOLUBLE_03 Intensity AD46_C2_INSOLUBLE_01 Intensity AD47_C1_INSOLUBLE_01 Intensity AD48_C2_INSOLUBLE_01 Intensity AD49_C2_INSOLUBLE_01 Intensity CTR01_C1_INSOLUBLE_01 Intensity CTR02_C1_INSOLUBLE_01 Intensity CTR03_C1_INSOLUBLE_01 Intensity CTR04_C1_INSOLUBLE_01 Intensity CTR05_C2_INSOLUBLE_01 Intensity CTR06_C2_INSOLUBLE_01 Intensity CTR07_C1_INSOLUBLE_01 Intensity CTR08_C1_INSOLUBLE_01 Intensity CTR08_C2_INSOLUBLE_01 Intensity CTR09_C2_INSOLUBLE_01 Intensity CTR10_C1_INSOLUBLE_01 Intensity CTR10_C2_INSOLUBLE_01 Intensity CTR11_C2_INSOLUBLE_01 Intensity CTR12_C2_INSOLUBLE_01 Intensity CTR13_C2_INSOLUBLE_01 Intensity CTR14_C2_INSOLUBLE_01 Intensity CTR15_C2_INSOLUBLE_01 Intensity CTR16_C2_INSOLUBLE_01 Intensity CTR17_C2_INSOLUBLE_01 Intensity CTR18_C2_INSOLUBLE_01 Intensity CTR19_C1_INSOLUBLE_01 Intensity CTR20_C1_INSOLUBLE_01 Intensity CTR21_C2_INSOLUBLE_01 Intensity CTR22_C2_INSOLUBLE_01 Intensity CTR23_C2_INSOLUBLE_01 Intensity CTR24_C1_INSOLUBLE_01 Intensity CTR25_C1_INSOLUBLE_01 Intensity CTR26_C2_INSOLUBLE_01 Intensity CTR27_C1_INSOLUBLE_01 Intensity CTR28_C1_INSOLUBLE_01 Intensity CTR28_C1_INSOLUBLE_02 Intensity CTR28_C2_INSOLUBLE_01 Intensity CTR29_C1_INSOLUBLE_01 Intensity CTR29_C1_INSOLUBLE_02 Intensity CTR29_C1_INSOLUBLE_03 Intensity CTR30_C1_INSOLUBLE_01 Intensity CTR30_C1_INSOLUBLE_02 Intensity CTR30_C2_INSOLUBLE_01 Intensity CTR31_C1_INSOLUBLE_01 Intensity CTR31_C2_INSOLUBLE_01 Intensity CTR32_C1_INSOLUBLE_01 Intensity CTR32_C2_INSOLUBLE_01 Intensity CTR33_C1_INSOLUBLE_01 Intensity CTR34_C1_INSOLUBLE_01 Intensity CTR34_C2_INSOLUBLE_01 Intensity CTR35_C1_INSOLUBLE_01 Intensity CTR36_C1_INSOLUBLE_01 Intensity CTR36_C1_INSOLUBLE_02 Intensity CTR37_C1_INSOLUBLE_01 Intensity CTR38_C1_INSOLUBLE_01 Intensity CTR39_C1_INSOLUBLE_01 Intensity CTR40_C1_INSOLUBLE_01 Intensity CTR40_C1_INSOLUBLE_02 Intensity CTR40_C1_INSOLUBLE_03 Intensity CTR41_C1_INSOLUBLE_01 Intensity CTR41_C1_INSOLUBLE_02 Intensity CTR41_C1_INSOLUBLE_03 Intensity CTR42_C1_INSOLUBLE_01 Intensity CTR42_C1_INSOLUBLE_02 Intensity CTR42_C1_INSOLUBLE_03 Intensity CTR43_C2_INSOLUBLE_01 Intensity CTR44_C1_INSOLUBLE_01 iBAQ peptides iBAQ iBAQ AD01_C1_INSOLUBLE_01 iBAQ AD01_C1_INSOLUBLE_02 iBAQ AD01_C1_INSOLUBLE_03 iBAQ AD01_C2_INSOLUBLE_01 iBAQ AD02_C1_INSOLUBLE_01 iBAQ AD02_C1_INSOLUBLE_02 iBAQ AD02_C2_INSOLUBLE_01 iBAQ AD03_C1_INSOLUBLE_01 iBAQ AD03_C1_INSOLUBLE_02 iBAQ AD03_C1_INSOLUBLE_03 iBAQ AD03_C2_INSOLUBLE_01 iBAQ AD04_C1_INSOLUBLE_01 iBAQ AD04_C2_INSOLUBLE_01 iBAQ AD05_C2_INSOLUBLE_01 iBAQ AD06_C1_INSOLUBLE_01 iBAQ AD07_C1_INSOLUBLE_01 iBAQ AD07_C1_INSOLUBLE_02 iBAQ AD07_C1_INSOLUBLE_03 iBAQ AD07_C2_INSOLUBLE_01 iBAQ AD08_C2_INSOLUBLE_01 iBAQ AD09_C1_INSOLUBLE_01 iBAQ AD10_C1_INSOLUBLE_01 iBAQ AD10_C2_INSOLUBLE_01 iBAQ AD11_C2_INSOLUBLE_01 iBAQ AD12_C2_INSOLUBLE_01 iBAQ AD13_C2_INSOLUBLE_01 iBAQ AD14_C2_INSOLUBLE_01 iBAQ AD15_C2_INSOLUBLE_01 iBAQ AD16_C2_INSOLUBLE_01 iBAQ AD17_C2_INSOLUBLE_01 iBAQ AD18_C2_INSOLUBLE_01 iBAQ AD19_C2_INSOLUBLE_01 iBAQ AD20_C1_INSOLUBLE_01 iBAQ AD21_C1_INSOLUBLE_01 iBAQ AD21_C2_INSOLUBLE_01 iBAQ AD22_C1_INSOLUBLE_01 iBAQ AD23_C1_INSOLUBLE_01 iBAQ AD23_C1_INSOLUBLE_02 iBAQ AD23_C2_INSOLUBLE_01 iBAQ AD24_C1_INSOLUBLE_01 iBAQ AD24_C1_INSOLUBLE_02 iBAQ AD25_C1_INSOLUBLE_01 iBAQ AD26_C1_INSOLUBLE_01 iBAQ AD27_C1_INSOLUBLE_01 iBAQ AD27_C1_INSOLUBLE_02 iBAQ AD28_C2_INSOLUBLE_01 iBAQ AD29_C1_INSOLUBLE_01 iBAQ AD30_C1_INSOLUBLE_01 iBAQ AD30_C1_INSOLUBLE_02 iBAQ AD30_C2_INSOLUBLE_01 iBAQ AD31_C2_INSOLUBLE_01 iBAQ AD32_C2_INSOLUBLE_01 iBAQ AD33_C2_INSOLUBLE_01 iBAQ AD34_C1_INSOLUBLE_01 iBAQ AD34_C1_INSOLUBLE_02 iBAQ AD35_C1_INSOLUBLE_01 iBAQ AD35_C1_INSOLUBLE_02 iBAQ AD36_C1_INSOLUBLE_01 iBAQ AD37_C1_INSOLUBLE_01 iBAQ AD37_C2_INSOLUBLE_01 iBAQ AD38_C1_INSOLUBLE_01 iBAQ AD38_C1_INSOLUBLE_02 iBAQ AD38_C1_INSOLUBLE_03 iBAQ AD39_C2_INSOLUBLE_01 iBAQ AD40_C2_INSOLUBLE_01 iBAQ AD41_C2_INSOLUBLE_01 iBAQ AD42_C2_INSOLUBLE_01 iBAQ AD43_C1_INSOLUBLE_01 iBAQ AD44_C1_INSOLUBLE_01 iBAQ AD44_C1_INSOLUBLE_02 iBAQ AD44_C1_INSOLUBLE_03 iBAQ AD44_C1_INSOLUBLE_04 iBAQ AD45_C1_INSOLUBLE_01 iBAQ AD45_C1_INSOLUBLE_02 iBAQ AD46_C1_INSOLUBLE_01 iBAQ AD46_C1_INSOLUBLE_02 iBAQ AD46_C1_INSOLUBLE_03 iBAQ AD46_C2_INSOLUBLE_01 iBAQ AD47_C1_INSOLUBLE_01 iBAQ AD48_C2_INSOLUBLE_01 iBAQ AD49_C2_INSOLUBLE_01 iBAQ CTR01_C1_INSOLUBLE_01 iBAQ CTR02_C1_INSOLUBLE_01 iBAQ CTR03_C1_INSOLUBLE_01 iBAQ CTR04_C1_INSOLUBLE_01 iBAQ CTR05_C2_INSOLUBLE_01 iBAQ CTR06_C2_INSOLUBLE_01 iBAQ CTR07_C1_INSOLUBLE_01 iBAQ CTR08_C1_INSOLUBLE_01 iBAQ CTR08_C2_INSOLUBLE_01 iBAQ CTR09_C2_INSOLUBLE_01 iBAQ CTR10_C1_INSOLUBLE_01 iBAQ CTR10_C2_INSOLUBLE_01 iBAQ CTR11_C2_INSOLUBLE_01 iBAQ CTR12_C2_INSOLUBLE_01 iBAQ CTR13_C2_INSOLUBLE_01 iBAQ CTR14_C2_INSOLUBLE_01 iBAQ CTR15_C2_INSOLUBLE_01 iBAQ CTR16_C2_INSOLUBLE_01 iBAQ CTR17_C2_INSOLUBLE_01 iBAQ CTR18_C2_INSOLUBLE_01 iBAQ CTR19_C1_INSOLUBLE_01 iBAQ CTR20_C1_INSOLUBLE_01 iBAQ CTR21_C2_INSOLUBLE_01 iBAQ CTR22_C2_INSOLUBLE_01 iBAQ CTR23_C2_INSOLUBLE_01 iBAQ CTR24_C1_INSOLUBLE_01 iBAQ CTR25_C1_INSOLUBLE_01 iBAQ CTR26_C2_INSOLUBLE_01 iBAQ CTR27_C1_INSOLUBLE_01 iBAQ CTR28_C1_INSOLUBLE_01 iBAQ CTR28_C1_INSOLUBLE_02 iBAQ CTR28_C2_INSOLUBLE_01 iBAQ CTR29_C1_INSOLUBLE_01 iBAQ CTR29_C1_INSOLUBLE_02 iBAQ CTR29_C1_INSOLUBLE_03 iBAQ CTR30_C1_INSOLUBLE_01 iBAQ CTR30_C1_INSOLUBLE_02 iBAQ CTR30_C2_INSOLUBLE_01 iBAQ CTR31_C1_INSOLUBLE_01 iBAQ CTR31_C2_INSOLUBLE_01 iBAQ CTR32_C1_INSOLUBLE_01 iBAQ CTR32_C2_INSOLUBLE_01 iBAQ CTR33_C1_INSOLUBLE_01 iBAQ CTR34_C1_INSOLUBLE_01 iBAQ CTR34_C2_INSOLUBLE_01 iBAQ CTR35_C1_INSOLUBLE_01 iBAQ CTR36_C1_INSOLUBLE_01 iBAQ CTR36_C1_INSOLUBLE_02 iBAQ CTR37_C1_INSOLUBLE_01 iBAQ CTR38_C1_INSOLUBLE_01 iBAQ CTR39_C1_INSOLUBLE_01 iBAQ CTR40_C1_INSOLUBLE_01 iBAQ CTR40_C1_INSOLUBLE_02 iBAQ CTR40_C1_INSOLUBLE_03 iBAQ CTR41_C1_INSOLUBLE_01 iBAQ CTR41_C1_INSOLUBLE_02 iBAQ CTR41_C1_INSOLUBLE_03 iBAQ CTR42_C1_INSOLUBLE_01 iBAQ CTR42_C1_INSOLUBLE_02 iBAQ CTR42_C1_INSOLUBLE_03 iBAQ CTR43_C2_INSOLUBLE_01 iBAQ CTR44_C1_INSOLUBLE_01 LFQ intensity AD01_C1_INSOLUBLE_01 LFQ intensity AD01_C1_INSOLUBLE_02 LFQ intensity AD01_C1_INSOLUBLE_03 LFQ intensity AD01_C2_INSOLUBLE_01 LFQ intensity AD02_C1_INSOLUBLE_01 LFQ intensity AD02_C1_INSOLUBLE_02 LFQ intensity AD02_C2_INSOLUBLE_01 LFQ intensity AD03_C1_INSOLUBLE_01 LFQ intensity AD03_C1_INSOLUBLE_02 LFQ intensity AD03_C1_INSOLUBLE_03 LFQ intensity AD03_C2_INSOLUBLE_01 LFQ intensity AD04_C1_INSOLUBLE_01 LFQ intensity AD04_C2_INSOLUBLE_01 LFQ intensity AD05_C2_INSOLUBLE_01 LFQ intensity AD06_C1_INSOLUBLE_01 LFQ intensity AD07_C1_INSOLUBLE_01 LFQ intensity AD07_C1_INSOLUBLE_02 LFQ intensity AD07_C1_INSOLUBLE_03 LFQ intensity AD07_C2_INSOLUBLE_01 LFQ intensity AD08_C2_INSOLUBLE_01 LFQ intensity AD09_C1_INSOLUBLE_01 LFQ intensity AD10_C1_INSOLUBLE_01 LFQ intensity AD10_C2_INSOLUBLE_01 LFQ intensity AD11_C2_INSOLUBLE_01 LFQ intensity AD12_C2_INSOLUBLE_01 LFQ intensity AD13_C2_INSOLUBLE_01 LFQ intensity AD14_C2_INSOLUBLE_01 LFQ intensity AD15_C2_INSOLUBLE_01 LFQ intensity AD16_C2_INSOLUBLE_01 LFQ intensity AD17_C2_INSOLUBLE_01 LFQ intensity AD18_C2_INSOLUBLE_01 LFQ intensity AD19_C2_INSOLUBLE_01 LFQ intensity AD20_C1_INSOLUBLE_01 LFQ intensity AD21_C1_INSOLUBLE_01 LFQ intensity AD21_C2_INSOLUBLE_01 LFQ intensity AD22_C1_INSOLUBLE_01 LFQ intensity AD23_C1_INSOLUBLE_01 LFQ intensity AD23_C1_INSOLUBLE_02 LFQ intensity AD23_C2_INSOLUBLE_01 LFQ intensity AD24_C1_INSOLUBLE_01 LFQ intensity AD24_C1_INSOLUBLE_02 LFQ intensity AD25_C1_INSOLUBLE_01 LFQ intensity AD26_C1_INSOLUBLE_01 LFQ intensity AD27_C1_INSOLUBLE_01 LFQ intensity AD27_C1_INSOLUBLE_02 LFQ intensity AD28_C2_INSOLUBLE_01 LFQ intensity AD29_C1_INSOLUBLE_01 LFQ intensity AD30_C1_INSOLUBLE_01 LFQ intensity AD30_C1_INSOLUBLE_02 LFQ intensity AD30_C2_INSOLUBLE_01 LFQ intensity AD31_C2_INSOLUBLE_01 LFQ intensity AD32_C2_INSOLUBLE_01 LFQ intensity AD33_C2_INSOLUBLE_01 LFQ intensity AD34_C1_INSOLUBLE_01 LFQ intensity AD34_C1_INSOLUBLE_02 LFQ intensity AD35_C1_INSOLUBLE_01 LFQ intensity AD35_C1_INSOLUBLE_02 LFQ intensity AD36_C1_INSOLUBLE_01 LFQ intensity AD37_C1_INSOLUBLE_01 LFQ intensity AD37_C2_INSOLUBLE_01 LFQ intensity AD38_C1_INSOLUBLE_01 LFQ intensity AD38_C1_INSOLUBLE_02 LFQ intensity AD38_C1_INSOLUBLE_03 LFQ intensity AD39_C2_INSOLUBLE_01 LFQ intensity AD40_C2_INSOLUBLE_01 LFQ intensity AD41_C2_INSOLUBLE_01 LFQ intensity AD42_C2_INSOLUBLE_01 LFQ intensity AD43_C1_INSOLUBLE_01 LFQ intensity AD44_C1_INSOLUBLE_01 LFQ intensity AD44_C1_INSOLUBLE_02 LFQ intensity AD44_C1_INSOLUBLE_03 LFQ intensity AD44_C1_INSOLUBLE_04 LFQ intensity AD45_C1_INSOLUBLE_01 LFQ intensity AD45_C1_INSOLUBLE_02 LFQ intensity AD46_C1_INSOLUBLE_01 LFQ intensity AD46_C1_INSOLUBLE_02 LFQ intensity AD46_C1_INSOLUBLE_03 LFQ intensity AD46_C2_INSOLUBLE_01 LFQ intensity AD47_C1_INSOLUBLE_01 LFQ intensity AD48_C2_INSOLUBLE_01 LFQ intensity AD49_C2_INSOLUBLE_01 LFQ intensity CTR01_C1_INSOLUBLE_01 LFQ intensity CTR02_C1_INSOLUBLE_01 LFQ intensity CTR03_C1_INSOLUBLE_01 LFQ intensity CTR04_C1_INSOLUBLE_01 LFQ intensity CTR05_C2_INSOLUBLE_01 LFQ intensity CTR06_C2_INSOLUBLE_01 LFQ intensity CTR07_C1_INSOLUBLE_01 LFQ intensity CTR08_C1_INSOLUBLE_01 LFQ intensity CTR08_C2_INSOLUBLE_01 LFQ intensity CTR09_C2_INSOLUBLE_01 LFQ intensity CTR10_C1_INSOLUBLE_01 LFQ intensity CTR10_C2_INSOLUBLE_01 LFQ intensity CTR11_C2_INSOLUBLE_01 LFQ intensity CTR12_C2_INSOLUBLE_01 LFQ intensity CTR13_C2_INSOLUBLE_01 LFQ intensity CTR14_C2_INSOLUBLE_01 LFQ intensity CTR15_C2_INSOLUBLE_01 LFQ intensity CTR16_C2_INSOLUBLE_01 LFQ intensity CTR17_C2_INSOLUBLE_01 LFQ intensity CTR18_C2_INSOLUBLE_01 LFQ intensity CTR19_C1_INSOLUBLE_01 LFQ intensity CTR20_C1_INSOLUBLE_01 LFQ intensity CTR21_C2_INSOLUBLE_01 LFQ intensity CTR22_C2_INSOLUBLE_01 LFQ intensity CTR23_C2_INSOLUBLE_01 LFQ intensity CTR24_C1_INSOLUBLE_01 LFQ intensity CTR25_C1_INSOLUBLE_01 LFQ intensity CTR26_C2_INSOLUBLE_01 LFQ intensity CTR27_C1_INSOLUBLE_01 LFQ intensity CTR28_C1_INSOLUBLE_01 LFQ intensity CTR28_C1_INSOLUBLE_02 LFQ intensity CTR28_C2_INSOLUBLE_01 LFQ intensity CTR29_C1_INSOLUBLE_01 LFQ intensity CTR29_C1_INSOLUBLE_02 LFQ intensity CTR29_C1_INSOLUBLE_03 LFQ intensity CTR30_C1_INSOLUBLE_01 LFQ intensity CTR30_C1_INSOLUBLE_02 LFQ intensity CTR30_C2_INSOLUBLE_01 LFQ intensity CTR31_C1_INSOLUBLE_01 LFQ intensity CTR31_C2_INSOLUBLE_01 LFQ intensity CTR32_C1_INSOLUBLE_01 LFQ intensity CTR32_C2_INSOLUBLE_01 LFQ intensity CTR33_C1_INSOLUBLE_01 LFQ intensity CTR34_C1_INSOLUBLE_01 LFQ intensity CTR34_C2_INSOLUBLE_01 LFQ intensity CTR35_C1_INSOLUBLE_01 LFQ intensity CTR36_C1_INSOLUBLE_01 LFQ intensity CTR36_C1_INSOLUBLE_02 LFQ intensity CTR37_C1_INSOLUBLE_01 LFQ intensity CTR38_C1_INSOLUBLE_01 LFQ intensity CTR39_C1_INSOLUBLE_01 LFQ intensity CTR40_C1_INSOLUBLE_01 LFQ intensity CTR40_C1_INSOLUBLE_02 LFQ intensity CTR40_C1_INSOLUBLE_03 LFQ intensity CTR41_C1_INSOLUBLE_01 LFQ intensity CTR41_C1_INSOLUBLE_02 LFQ intensity CTR41_C1_INSOLUBLE_03 LFQ intensity CTR42_C1_INSOLUBLE_01 LFQ intensity CTR42_C1_INSOLUBLE_02 LFQ intensity CTR42_C1_INSOLUBLE_03 LFQ intensity CTR43_C2_INSOLUBLE_01 LFQ intensity CTR44_C1_INSOLUBLE_01 MS/MS count AD01_C1_INSOLUBLE_01 MS/MS count AD01_C1_INSOLUBLE_02 MS/MS count AD01_C1_INSOLUBLE_03 MS/MS count AD01_C2_INSOLUBLE_01 MS/MS count AD02_C1_INSOLUBLE_01 MS/MS count AD02_C1_INSOLUBLE_02 MS/MS count AD02_C2_INSOLUBLE_01 MS/MS count AD03_C1_INSOLUBLE_01 MS/MS count AD03_C1_INSOLUBLE_02 MS/MS count AD03_C1_INSOLUBLE_03 MS/MS count AD03_C2_INSOLUBLE_01 MS/MS count AD04_C1_INSOLUBLE_01 MS/MS count AD04_C2_INSOLUBLE_01 MS/MS count AD05_C2_INSOLUBLE_01 MS/MS count AD06_C1_INSOLUBLE_01 MS/MS count AD07_C1_INSOLUBLE_01 MS/MS count AD07_C1_INSOLUBLE_02 MS/MS count AD07_C1_INSOLUBLE_03 MS/MS count AD07_C2_INSOLUBLE_01 MS/MS count AD08_C2_INSOLUBLE_01 MS/MS count AD09_C1_INSOLUBLE_01 MS/MS count AD10_C1_INSOLUBLE_01 MS/MS count AD10_C2_INSOLUBLE_01 MS/MS count AD11_C2_INSOLUBLE_01 MS/MS count AD12_C2_INSOLUBLE_01 MS/MS count AD13_C2_INSOLUBLE_01 MS/MS count AD14_C2_INSOLUBLE_01 MS/MS count AD15_C2_INSOLUBLE_01 MS/MS count AD16_C2_INSOLUBLE_01 MS/MS count AD17_C2_INSOLUBLE_01 MS/MS count AD18_C2_INSOLUBLE_01 MS/MS count AD19_C2_INSOLUBLE_01 MS/MS count AD20_C1_INSOLUBLE_01 MS/MS count AD21_C1_INSOLUBLE_01 MS/MS count AD21_C2_INSOLUBLE_01 MS/MS count AD22_C1_INSOLUBLE_01 MS/MS count AD23_C1_INSOLUBLE_01 MS/MS count AD23_C1_INSOLUBLE_02 MS/MS count AD23_C2_INSOLUBLE_01 MS/MS count AD24_C1_INSOLUBLE_01 MS/MS count AD24_C1_INSOLUBLE_02 MS/MS count AD25_C1_INSOLUBLE_01 MS/MS count AD26_C1_INSOLUBLE_01 MS/MS count AD27_C1_INSOLUBLE_01 MS/MS count AD27_C1_INSOLUBLE_02 MS/MS count AD28_C2_INSOLUBLE_01 MS/MS count AD29_C1_INSOLUBLE_01 MS/MS count AD30_C1_INSOLUBLE_01 MS/MS count AD30_C1_INSOLUBLE_02 MS/MS count AD30_C2_INSOLUBLE_01 MS/MS count AD31_C2_INSOLUBLE_01 MS/MS count AD32_C2_INSOLUBLE_01 MS/MS count AD33_C2_INSOLUBLE_01 MS/MS count AD34_C1_INSOLUBLE_01 MS/MS count AD34_C1_INSOLUBLE_02 MS/MS count AD35_C1_INSOLUBLE_01 MS/MS count AD35_C1_INSOLUBLE_02 MS/MS count AD36_C1_INSOLUBLE_01 MS/MS count AD37_C1_INSOLUBLE_01 MS/MS count AD37_C2_INSOLUBLE_01 MS/MS count AD38_C1_INSOLUBLE_01 MS/MS count AD38_C1_INSOLUBLE_02 MS/MS count AD38_C1_INSOLUBLE_03 MS/MS count AD39_C2_INSOLUBLE_01 MS/MS count AD40_C2_INSOLUBLE_01 MS/MS count AD41_C2_INSOLUBLE_01 MS/MS count AD42_C2_INSOLUBLE_01 MS/MS count AD43_C1_INSOLUBLE_01 MS/MS count AD44_C1_INSOLUBLE_01 MS/MS count AD44_C1_INSOLUBLE_02 MS/MS count AD44_C1_INSOLUBLE_03 MS/MS count AD44_C1_INSOLUBLE_04 MS/MS count AD45_C1_INSOLUBLE_01 MS/MS count AD45_C1_INSOLUBLE_02 MS/MS count AD46_C1_INSOLUBLE_01 MS/MS count AD46_C1_INSOLUBLE_02 MS/MS count AD46_C1_INSOLUBLE_03 MS/MS count AD46_C2_INSOLUBLE_01 MS/MS count AD47_C1_INSOLUBLE_01 MS/MS count AD48_C2_INSOLUBLE_01 MS/MS count AD49_C2_INSOLUBLE_01 MS/MS count CTR01_C1_INSOLUBLE_01 MS/MS count CTR02_C1_INSOLUBLE_01 MS/MS count CTR03_C1_INSOLUBLE_01 MS/MS count CTR04_C1_INSOLUBLE_01 MS/MS count CTR05_C2_INSOLUBLE_01 MS/MS count CTR06_C2_INSOLUBLE_01 MS/MS count CTR07_C1_INSOLUBLE_01 MS/MS count CTR08_C1_INSOLUBLE_01 MS/MS count CTR08_C2_INSOLUBLE_01 MS/MS count CTR09_C2_INSOLUBLE_01 MS/MS count CTR10_C1_INSOLUBLE_01 MS/MS count CTR10_C2_INSOLUBLE_01 MS/MS count CTR11_C2_INSOLUBLE_01 MS/MS count CTR12_C2_INSOLUBLE_01 MS/MS count CTR13_C2_INSOLUBLE_01 MS/MS count CTR14_C2_INSOLUBLE_01 MS/MS count CTR15_C2_INSOLUBLE_01 MS/MS count CTR16_C2_INSOLUBLE_01 MS/MS count CTR17_C2_INSOLUBLE_01 MS/MS count CTR18_C2_INSOLUBLE_01 MS/MS count CTR19_C1_INSOLUBLE_01 MS/MS count CTR20_C1_INSOLUBLE_01 MS/MS count CTR21_C2_INSOLUBLE_01 MS/MS count CTR22_C2_INSOLUBLE_01 MS/MS count CTR23_C2_INSOLUBLE_01 MS/MS count CTR24_C1_INSOLUBLE_01 MS/MS count CTR25_C1_INSOLUBLE_01 MS/MS count CTR26_C2_INSOLUBLE_01 MS/MS count CTR27_C1_INSOLUBLE_01 MS/MS count CTR28_C1_INSOLUBLE_01 MS/MS count CTR28_C1_INSOLUBLE_02 MS/MS count CTR28_C2_INSOLUBLE_01 MS/MS count CTR29_C1_INSOLUBLE_01 MS/MS count CTR29_C1_INSOLUBLE_02 MS/MS count CTR29_C1_INSOLUBLE_03 MS/MS count CTR30_C1_INSOLUBLE_01 MS/MS count CTR30_C1_INSOLUBLE_02 MS/MS count CTR30_C2_INSOLUBLE_01 MS/MS count CTR31_C1_INSOLUBLE_01 MS/MS count CTR31_C2_INSOLUBLE_01 MS/MS count CTR32_C1_INSOLUBLE_01 MS/MS count CTR32_C2_INSOLUBLE_01 MS/MS count CTR33_C1_INSOLUBLE_01 MS/MS count CTR34_C1_INSOLUBLE_01 MS/MS count CTR34_C2_INSOLUBLE_01 MS/MS count CTR35_C1_INSOLUBLE_01 MS/MS count CTR36_C1_INSOLUBLE_01 MS/MS count CTR36_C1_INSOLUBLE_02 MS/MS count CTR37_C1_INSOLUBLE_01 MS/MS count CTR38_C1_INSOLUBLE_01 MS/MS count CTR39_C1_INSOLUBLE_01 MS/MS count CTR40_C1_INSOLUBLE_01 MS/MS count CTR40_C1_INSOLUBLE_02 MS/MS count CTR40_C1_INSOLUBLE_03 MS/MS count CTR41_C1_INSOLUBLE_01 MS/MS count CTR41_C1_INSOLUBLE_02 MS/MS count CTR41_C1_INSOLUBLE_03 MS/MS count CTR42_C1_INSOLUBLE_01 MS/MS count CTR42_C1_INSOLUBLE_02 MS/MS count CTR42_C1_INSOLUBLE_03 MS/MS count CTR43_C2_INSOLUBLE_01 MS/MS count CTR44_C1_INSOLUBLE_01 MS/MS count Only identified by site Reverse Potential contaminant id Peptide IDs Peptide is razor Mod. peptide IDs Evidence IDs MS/MS IDs Best MS/MS Acetyl (K) site IDs GlyGly (K) site IDs Oxidation (M) site IDs Phospho (STY) site IDs Acetyl (K) site positions GlyGly (K) site positions Oxidation (M) site positions Phospho (STY) site positions Taxonomy IDs A0A075B6I0 A0A075B6I0 2 2 2 IGLV8-61 LV861_HUMAN Immunoglobulin lambda variable 8-61 OS=Homo sapiens GN=IGLV8-61 PE=3 SV=7 1 2 2 2.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 14.8 14.8 14.8 12.814 122 122 0.0026537 3.4118 By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By MS/MS By MS/MS By matching 7.4 7.4 7.4 7.4 14.8 7.4 7.4 7.4 14.8 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 7.4 1078800000.0 10544000.0 51433000.0 1201800.0 4708800.0 18292000.0 10671000.0 68315000.0 23689000.0 35386000.0 31720000.0 3843200.0 32011000.0 3585300.0 5269200.0 5220500.0 4125300.0 5130500.0 511940.0 1225500.0 2597800.0 550180.0 8679700.0 11430000.0 2551000.0 3444800.0 11409000.0 6653700.0 102450000.0 6602900.0 4239100.0 11507000.0 18876000.0 87506000.0 3313300.0 103810000.0 17227000.0 122910000.0 10760000.0 150530000.0 5686500.0 10178000.0 29254000.0 15357000.0 10771000.0 3667500.0 4 269710000.0 2635900.0 12858000.0 300440.0 1177200.0 4573100.0 2667800.0 17079000.0 5922200.0 8846500.0 7930000.0 960790.0 8002700.0 896330.0 1317300.0 1305100.0 1031300.0 1282600.0 127990.0 306370.0 649460.0 137550.0 2169900.0 2857400.0 637760.0 861210.0 2852200.0 1663400.0 25612000.0 1650700.0 1059800.0 2876800.0 4718900.0 21877000.0 828320.0 25952000.0 4306800.0 30728000.0 2690100.0 37632000.0 1421600.0 2544500.0 7313400.0 3839200.0 2692800.0 916880.0 17393000.0 36285000.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 11.0 8748;27013 True;True 9824;30671 547584;547585;547586;547587;547588;547589;547590;547591;547592;547593;547594;547595;547596;547597;547598;547599;547600;547601;547602;547603;547604;547605;547606;547607;547608;547609;547610;547611;547612;547613;547614;547615;547616;547617;547618;547619;547620;547621;547622;547623;547624;547625;547626;547627;547628;547629;547630;547631;1760392;1760393 497771;497772;497773;497774;497775;497776;497777;497778;497779;497780;1621639;1621640 497774;1621640 -1 -A0A075B6I9;P04211 A0A075B6I9;P04211 3;2 3;2 3;2 Ig lambda chain V region 4A IGLV7-46 LV746_HUMAN Immunoglobulin lambda variable 7-46 OS=Homo sapiens GN=IGLV7-46 PE=3 SV=4;LV743_HUMAN Immunoglobulin lambda variable 7-43 OS=Homo sapiens GN=IGLV7-43 PE=3 SV=2 2 3 3 3.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 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MS/MS By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching 7.7 6.0 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 15.4 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 13.7 6.0 7.7 7.7 7.7 6.0 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 750780000.0 670520.0 14278000.0 19252000.0 11737000.0 647510.0 11884000.0 18263000.0 589200.0 1509900.0 13961000.0 20661000.0 1842000.0 11795000.0 7442400.0 10453000.0 8616900.0 222500.0 33750000.0 10718000.0 11050000.0 27094000.0 38027000.0 21644000.0 3084200.0 24772000.0 11338000.0 50737000.0 1458500.0 8521500.0 5386800.0 3945800.0 4124000.0 10904000.0 6535300.0 11468000.0 7564600.0 4429900.0 3560500.0 10312000.0 258680.0 17668000.0 4264000.0 6622900.0 18147000.0 42779000.0 42975000.0 14863000.0 13154000.0 510330.0 18912000.0 6793800.0 25532000.0 23434000.0 7436800.0 18015000.0 9899100.0 15269000.0 4 187700000.0 167630.0 3569400.0 4813000.0 2934100.0 161880.0 2970900.0 4565800.0 147300.0 377480.0 3490100.0 5165200.0 460500.0 2948800.0 1860600.0 2613300.0 2154200.0 55625.0 8437500.0 2679500.0 2762500.0 6773500.0 9506700.0 5411100.0 771060.0 6193000.0 2834500.0 12684000.0 364620.0 2130400.0 1346700.0 986440.0 1031000.0 2726000.0 1633800.0 2867100.0 1891200.0 1107500.0 890120.0 2577900.0 64669.0 4417000.0 1066000.0 1655700.0 4536700.0 10695000.0 10744000.0 3715900.0 3288500.0 127580.0 4727900.0 1698400.0 6383100.0 5858400.0 1859200.0 4503800.0 2474800.0 3817300.0 33750000.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 22.0 1.0 8749;11978;27012 True;True;True 9825;13443;30670 547632;547633;547634;547635;547636;547637;547638;547639;547640;547641;547642;547643;547644;547645;547646;547647;547648;547649;547650;547651;547652;547653;547654;547655;547656;547657;547658;547659;547660;547661;547662;547663;547664;547665;547666;547667;547668;547669;547670;547671;547672;547673;547674;547675;547676;547677;547678;547679;547680;547681;547682;547683;547684;547685;547686;547687;774173;774174;774175;774176;1760389;1760390;1760391 497781;497782;497783;497784;497785;497786;497787;497788;497789;497790;497791;497792;497793;497794;497795;497796;497797;497798;497799;497800;713524;1621638 497800;713524;1621638 -1;-1 -A0A075B6K4;P01715 A0A075B6K4 3;1 3;1 2;1 IGLV3-10 LV310_HUMAN Immunoglobulin lambda variable 3-10 OS=Homo sapiens GN=IGLV3-10 PE=3 SV=2 2 3 3 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 2.0 1.0 1.0 1.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 2.0 2.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 2.0 1.0 1.0 1.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 2.0 2.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 27.8 27.8 19.1 12.441 115 115;115 0.0049772 2.6663 By matching By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By MS/MS By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching 12.2 7.0 7.0 7.0 7.0 7.0 7.0 8.7 7.0 7.0 8.7 7.0 8.7 8.7 8.7 8.7 7.0 7.0 7.0 7.0 15.7 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 15.7 7.0 7.0 15.7 8.7 7.0 7.0 8.7 8.7 7.0 8.7 20.9 8.7 15.7 8.7 8.7 7.0 15.7 15.7 7.0 7.0 7.0 7.0 8.7 15.7 7.0 7.0 15.7 7.0 7.0 7.0 15.7 7.0 15.7 15.7 8.7 15.7 7.0 7.0 1188700000.0 1975200.0 21800000.0 63259000.0 22366000.0 29156000.0 6739000.0 75727000.0 9632200.0 5206200.0 30362000.0 11237000.0 18659000.0 17086000.0 7158200.0 12440000.0 4820000.0 2285900.0 3952300.0 4657200.0 5656100.0 89823000.0 1367200.0 992030.0 2542900.0 988590.0 1291400.0 1567300.0 4838800.0 5588300.0 1142200.0 33123000.0 28798000.0 35389000.0 12225000.0 12716000.0 2801700.0 4008800.0 6449800.0 5482700.0 6458300.0 15456000.0 82390000.0 3339700.0 7996400.0 5730500.0 2451500.0 1843100.0 25631000.0 8150000.0 1318300.0 36792000.0 12478000.0 11798000.0 7310700.0 9316500.0 3895400.0 3707600.0 21587000.0 20971000.0 46864000.0 105580000.0 23442000.0 3885000.0 37766000.0 33793000.0 6113100.0 31659000.0 3028900.0 2670800.0 4 297180000.0 493810.0 5450000.0 15815000.0 5591500.0 7289000.0 1684700.0 18932000.0 2408100.0 1301600.0 7590500.0 2809100.0 4664700.0 4271400.0 1789500.0 3110000.0 1205000.0 571470.0 988080.0 1164300.0 1414000.0 22456000.0 341800.0 248010.0 635720.0 247150.0 322860.0 391830.0 1209700.0 1397100.0 285550.0 8280800.0 7199500.0 8847400.0 3056200.0 3178900.0 700440.0 1002200.0 1612400.0 1370700.0 1614600.0 3864100.0 20597000.0 834920.0 1999100.0 1432600.0 612880.0 460770.0 6407700.0 2037500.0 329580.0 9197900.0 3119500.0 2949500.0 1827700.0 2329100.0 973850.0 926900.0 5396700.0 5242900.0 11716000.0 26395000.0 5860500.0 971240.0 9441500.0 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3;2 Ig lambda chain V region 4A IGLV7-46 LV746_HUMAN Immunoglobulin lambda variable 7-46 OS=Homo sapiens GN=IGLV7-46 PE=3 SV=4;LV743_HUMAN Immunoglobulin lambda variable 7-43 OS=Homo sapiens GN=IGLV7-43 PE=3 SV=2 2 3 3 3.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 21.4 21.4 21.4 12.468 117 117;117 0.0022614 3.7132 By matching By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By matching By matching By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching By matching By matching By matching By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching 7.7 6.0 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 15.4 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 13.7 6.0 7.7 7.7 7.7 6.0 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 7.7 750780000.0 670520.0 14278000.0 19252000.0 11737000.0 647510.0 11884000.0 18263000.0 589200.0 1509900.0 13961000.0 20661000.0 1842000.0 11795000.0 7442400.0 10453000.0 8616900.0 222500.0 33750000.0 10718000.0 11050000.0 27094000.0 38027000.0 21644000.0 3084200.0 24772000.0 11338000.0 50737000.0 1458500.0 8521500.0 5386800.0 3945800.0 4124000.0 10904000.0 6535300.0 11468000.0 7564600.0 4429900.0 3560500.0 10312000.0 258680.0 17668000.0 4264000.0 6622900.0 18147000.0 42779000.0 42975000.0 14863000.0 13154000.0 510330.0 18912000.0 6793800.0 25532000.0 23434000.0 7436800.0 18015000.0 9899100.0 15269000.0 4 187700000.0 167630.0 3569400.0 4813000.0 2934100.0 161880.0 2970900.0 4565800.0 147300.0 377480.0 3490100.0 5165200.0 460500.0 2948800.0 1860600.0 2613300.0 2154200.0 55625.0 8437500.0 2679500.0 2762500.0 6773500.0 9506700.0 5411100.0 771060.0 6193000.0 2834500.0 12684000.0 364620.0 2130400.0 1346700.0 986440.0 1031000.0 2726000.0 1633800.0 2867100.0 1891200.0 1107500.0 890120.0 2577900.0 64669.0 4417000.0 1066000.0 1655700.0 4536700.0 10695000.0 10744000.0 3715900.0 3288500.0 127580.0 4727900.0 1698400.0 6383100.0 5858400.0 1859200.0 4503800.0 2474800.0 3817300.0 33750000.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 22.0 1.0 8749;11978;27012 True;True;True 9825;13443;30670 547632;547633;547634;547635;547636;547637;547638;547639;547640;547641;547642;547643;547644;547645;547646;547647;547648;547649;547650;547651;547652;547653;547654;547655;547656;547657;547658;547659;547660;547661;547662;547663;547664;547665;547666;547667;547668;547669;547670;547671;547672;547673;547674;547675;547676;547677;547678;547679;547680;547681;547682;547683;547684;547685;547686;547687;774173;774174;774175;774176;1760389;1760390;1760391 497781;497782;497783;497784;497785;497786;497787;497788;497789;497790;497791;497792;497793;497794;497795;497796;497797;497798;497799;497800;713524;1621638 497800;713524;1621638 -1;-1 +A0A075B6K4;P01715 A0A075B6K4;P00000 3;1 3;1 2;1 IGLV3-10 LV310_HUMAN Immunoglobulin lambda variable 3-10 OS=Homo sapiens GN=IGLV3-10 PE=3 SV=2 2 3 3 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 2.0 1.0 1.0 1.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 2.0 2.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 2.0 1.0 1.0 1.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 2.0 2.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 27.8 27.8 19.1 12.441 115 115;115 0.0049772 2.6663 By matching By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By MS/MS By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching 12.2 7.0 7.0 7.0 7.0 7.0 7.0 8.7 7.0 7.0 8.7 7.0 8.7 8.7 8.7 8.7 7.0 7.0 7.0 7.0 15.7 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 15.7 7.0 7.0 15.7 8.7 7.0 7.0 8.7 8.7 7.0 8.7 20.9 8.7 15.7 8.7 8.7 7.0 15.7 15.7 7.0 7.0 7.0 7.0 8.7 15.7 7.0 7.0 15.7 7.0 7.0 7.0 15.7 7.0 15.7 15.7 8.7 15.7 7.0 7.0 1188700000.0 1975200.0 21800000.0 63259000.0 22366000.0 29156000.0 6739000.0 75727000.0 9632200.0 5206200.0 30362000.0 11237000.0 18659000.0 17086000.0 7158200.0 12440000.0 4820000.0 2285900.0 3952300.0 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IGLV3-9 LV39_HUMAN Immunoglobulin lambda variable 3-9 OS=Homo sapiens GN=IGLV3-9 PE=3 SV=1 2 5 3 3.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 3.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 49.6 26.1 26.1 12.332 115 115;115 0.0026942 3.5738 By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 23.5 23.5 13.9 13.9 23.5 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 9.6 9.6 9.6 9.6 9.6 9.6 13.9 13.9 13.9 23.5 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 9.6 13.9 13.9 13.9 13.9 30.4 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 30.4 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 9.6 13.9 13.9 193940000.0 12235000.0 11589000.0 25127000.0 25600000.0 8630200.0 9300000.0 48149000.0 3953700.0 49357000.0 6 32324000.0 2039100.0 1931600.0 4187900.0 4266700.0 1438400.0 1550000.0 8024800.0 658940.0 8226200.0 48149000.0 1.0 1.0 1.0 3.0 3.0 4967;8750;13950;21365;21366 True;False;False;True;True 5597;9826;15649;24372;24373 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-1;-1 REV__P10636;CON__P10636 A0A075B6K4 3;1 3;1 2;1 IGLV3-10 LV310_HUMAN Immunoglobulin lambda variable 3-10 OS=Homo sapiens GN=IGLV3-10 PE=3 SV=2 2 3 3 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 2.0 1.0 1.0 1.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 2.0 2.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 2.0 1.0 1.0 1.0 2.0 2.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 2.0 1.0 2.0 2.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 27.8 27.8 19.1 12.441 115 115;115 0.0049772 2.6663 By matching By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By MS/MS By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching 12.2 7.0 7.0 7.0 7.0 7.0 7.0 8.7 7.0 7.0 8.7 7.0 8.7 8.7 8.7 8.7 7.0 7.0 7.0 7.0 15.7 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 7.0 15.7 7.0 7.0 15.7 8.7 7.0 7.0 8.7 8.7 7.0 8.7 20.9 8.7 15.7 8.7 8.7 7.0 15.7 15.7 7.0 7.0 7.0 7.0 8.7 15.7 7.0 7.0 15.7 7.0 7.0 7.0 15.7 7.0 15.7 15.7 8.7 15.7 7.0 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LV39_HUMAN Immunoglobulin lambda variable 3-9 OS=Homo sapiens GN=IGLV3-9 PE=3 SV=1 2 5 3 3.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 2.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 3.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 2.0 1.0 1.0 49.6 26.1 26.1 12.332 115 115;115 0.0026942 3.5738 By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 23.5 23.5 13.9 13.9 23.5 13.9 13.9 13.9 13.9 13.9 13.9 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-1;-1 diff --git a/tests/protzilla/importing/test_metadata_import.py b/tests/protzilla/importing/test_metadata_import.py index c5d9738f..62bb24fd 100644 --- a/tests/protzilla/importing/test_metadata_import.py +++ b/tests/protzilla/importing/test_metadata_import.py @@ -31,6 +31,7 @@ def test_metadata_import_diann(run_empty): { "file_path": f"{PROJECT_PATH}/tests/test_data/DIANN_data/20230605 24h prodi DMSO report.pg_matrix.tsv", "map_to_uniprot": "False", + "aggregation_method": "Sum", } ) assert ( diff --git a/tests/protzilla/importing/test_ms_data_import.py b/tests/protzilla/importing/test_ms_data_import.py index 74292abd..457fe145 100644 --- a/tests/protzilla/importing/test_ms_data_import.py +++ b/tests/protzilla/importing/test_ms_data_import.py @@ -134,21 +134,21 @@ def diann_import_intensity_df(): 14: "LM07063", }, "Protein ID": { - 0: "A0A087WWU8;A0A2R2Y2Q3;A0A494C0P6;J3KN67", - 1: "A0A0B4J2A2;P0DN37", - 2: "A0A0G2JPD3;A0A140T8W8;A0A140T8Y4;A0A1W2PPF8;A0A1W2PR61;Q5SPM2", - 3: "A0A0U1RQV3", - 4: "A0A140T913;A0A140T933;A0A140T955;A0A140T9I0;A0A140T9X5;A0A1W2PPQ2;A0A1W2PRT9;Q53Z42", - 5: "A0A087WWU8;A0A2R2Y2Q3;A0A494C0P6;J3KN67", - 6: "A0A0B4J2A2;P0DN37", - 7: "A0A0G2JPD3;A0A140T8W8;A0A140T8Y4;A0A1W2PPF8;A0A1W2PR61;Q5SPM2", - 8: "A0A0U1RQV3", - 9: "A0A140T913;A0A140T933;A0A140T955;A0A140T9I0;A0A140T9X5;A0A1W2PPQ2;A0A1W2PRT9;Q53Z42", - 10: "A0A087WWU8;A0A2R2Y2Q3;A0A494C0P6;J3KN67", - 11: "A0A0B4J2A2;P0DN37", - 12: "A0A0G2JPD3;A0A140T8W8;A0A140T8Y4;A0A1W2PPF8;A0A1W2PR61;Q5SPM2", - 13: "A0A0U1RQV3", - 14: "A0A140T913;A0A140T933;A0A140T955;A0A140T9I0;A0A140T9X5;A0A1W2PPQ2;A0A1W2PRT9;Q53Z42", + 0: "A0A0B4J2A2", + 1: "A0A0G2JPD3", + 2: "A0A0U1RQV3", + 3: "A0A140T913", + 4: "A0A2R2Y2Q3", + 5: "A0A0B4J2A2", + 6: "A0A0G2JPD3", + 7: "A0A0U1RQV3", + 8: "A0A140T913", + 9: "A0A2R2Y2Q3", + 10: "A0A0B4J2A2", + 11: "A0A0G2JPD3", + 12: "A0A0U1RQV3", + 13: "A0A140T913", + 14: "A0A2R2Y2Q3", }, "Gene": { 0: np.nan, @@ -168,21 +168,21 @@ def diann_import_intensity_df(): 14: np.nan, }, "Intensity": { - 0: 329042.0, - 1: 138322.0, - 2: np.nan, - 3: 122984.0, - 4: 36317.0, - 5: 367477.0, - 6: 572539.0, - 7: np.nan, - 8: 59042.7, - 9: np.nan, - 10: 381325.0, - 11: 96522.7, - 12: np.nan, - 13: 72372.5, - 14: 27456.7, + 0: 138322.0, + 1: np.nan, + 2: 122984.0, + 3: 36317.0, + 4: 329042.0, + 5: 572539.0, + 6: np.nan, + 7: 59042.7, + 8: np.nan, + 9: 367477.0, + 10: 96522.7, + 11: np.nan, + 12: 72372.5, + 13: 27456.7, + 14: 381325.0, }, } return pd.DataFrame(data=diann_intensity_df) @@ -218,7 +218,7 @@ def test_max_quant_import_no_protein_ids_column(): assert "protein_df" not in outputs assert "messages" in outputs assert any(message["level"] == logging.ERROR for message in outputs["messages"]) - assert any("Protein IDs" in message["msg"] for message in outputs["messages"]) + assert any("Majority protein IDs" in message["msg"] for message in outputs["messages"]) def test_max_quant_import_invalid_data(): @@ -263,6 +263,7 @@ def test_ms_fragger_import(intensity_name): def test_diann_import(): outputs = ms_data_import.diann_import( file_path=f"{PROJECT_PATH}/tests/diann_intensities.tsv", + aggregation_method="Sum", ) expected_intensity_df = diann_import_intensity_df() @@ -280,7 +281,7 @@ def test_filter_rev_con(): ) protein_ids = outputs["protein_df"]["Protein ID"].unique().tolist() # not the complete group should be filtered out if contains valid ids - assert "P00000" in protein_ids + assert "P04211" in protein_ids # all instances of rev and con should be filtered out assert all( not any( @@ -324,9 +325,9 @@ def test_transform_and_clean(): def test_clean_protein_groups(): expected = [ - "A0A009IHW8-8-7-6;M99999_7-8;P12345;Q12345-3", - "P00000;P12345;P12345-1;P12345-11;P12345-9", - "ENSP12345678901;NP_123456;NP_123456789;XP_123456789", + "P12345", + "P12345-9", + "ENSP12345678901", "P12345", "", ] @@ -347,7 +348,7 @@ def test_clean_protein_groups(): @patch("protzilla.importing.ms_data_import.map_ids_to_uniprot") def test_clean_protein_groups_map(ids_to_uniprot_mock): ids_to_uniprot_mock.return_value = {"NP_123456": ["P54321", "P12345"]} - expected = ["P12321;P12345;P54321", ""] + expected = ["P54321", ""] test_data = ["NP_123456;P12321", "XP_123456789"] clean_groups, filtered = ms_data_import.clean_protein_groups( test_data, map_to_uniprot=True diff --git a/tests/protzilla/importing/test_peptide_import.py b/tests/protzilla/importing/test_peptide_import.py index 12301d5e..b526c5d7 100644 --- a/tests/protzilla/importing/test_peptide_import.py +++ b/tests/protzilla/importing/test_peptide_import.py @@ -11,16 +11,16 @@ def peptide_df(intensity_name): # sample, protein id, sequence, intensity, pep peptide_protein_list = ( - ["Sample01", "P46459;P46459-2", "AAQSTAMNR", 0.0013764], - ["Sample02", "P46459;P46459-2", "AAQSTAMNR", 0.0013764], - ["Sample03", "P46459;P46459-2", "AAQSTAMNR", 0.0013764], - ["Sample04", "P46459;P46459-2", "AAQSTAMNR", 0.0013764], - ["Sample05", "P46459;P46459-2", "AAQSTAMNR", 0.0013764], - ["Sample01", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 0.037779], - ["Sample02", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 0.037779], - ["Sample03", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 0.037779], - ["Sample04", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 0.037779], - ["Sample05", "Q13748;Q6PEY2;Q9NY65;Q9NY65-2", "EDLAALEK", 0.037779], + ["Sample01", "P46459", "AAQSTAMNR", 0.0013764], + ["Sample02", "P46459", "AAQSTAMNR", 0.0013764], + ["Sample03", "P46459", "AAQSTAMNR", 0.0013764], + ["Sample04", "P46459", "AAQSTAMNR", 0.0013764], + ["Sample05", "P46459", "AAQSTAMNR", 0.0013764], + ["Sample01", "Q13748", "EDLAALEK", 0.037779], + ["Sample02", "Q13748", "EDLAALEK", 0.037779], + ["Sample03", "Q13748", "EDLAALEK", 0.037779], + ["Sample04", "Q13748", "EDLAALEK", 0.037779], + ["Sample05", "Q13748", "EDLAALEK", 0.037779], ) peptide_df = pd.DataFrame( @@ -88,7 +88,7 @@ def evidence_df(intensity_name): ], [ "CTR17_C2_INSOLUBLE_01", - "O75822;O75822-2;O75822-3", + "O75822-2", "AAAAAAAGDSDSWDADAFSVEDPVRK", 13249000, "Acetyl (Protein N-term)", diff --git a/tests/protzilla/test_run.py b/tests/protzilla/test_run.py index fd6276cc..7c9a111c 100644 --- a/tests/protzilla/test_run.py +++ b/tests/protzilla/test_run.py @@ -50,6 +50,7 @@ def test_step_calculate(self, run_empty, maxquant_data_file): "file_path": maxquant_data_file, "map_to_uniprot": False, "intensity_name": "Intensity", + "aggregation_method": "Sum", } ) assert run_empty.current_step.output["protein_df"] is not None diff --git a/tests/protzilla/test_runner.py b/tests/protzilla/test_runner.py index 03f76e60..b5de3148 100644 --- a/tests/protzilla/test_runner.py +++ b/tests/protzilla/test_runner.py @@ -103,7 +103,7 @@ def test_runner_imports( 'PlotGOEnrichmentBarPlot' ] expected_method_parameters = [ - call({'intensity_name': 'iBAQ', 'map_to_uniprot': False, 'file_path': 'tests/proteinGroups_small_cut.txt'}), + call({'intensity_name': 'iBAQ', 'map_to_uniprot': False, 'aggregation_mode': 'Sum', 'file_path': 'tests/proteinGroups_small_cut.txt'}), call({'feature_orientation': 'Columns (samples in rows, features in columns)', 'file_path': 'tests/metadata_cut_columns.csv'}), call({'percentage': 0.5}), call({'deviation_threshold': 2.0}), @@ -168,7 +168,7 @@ def test_runner_calculates(monkeypatch, tests_folder_name, ms_data_path, metadat "FilterProteinsBySamplesMissing", ] assert mock_method.call_args_list == [ - call({'intensity_name': 'iBAQ', 'map_to_uniprot': False, 'file_path': 'tests/proteinGroups_small_cut.txt'}), + call({'intensity_name': 'iBAQ', 'map_to_uniprot': False, 'aggregation_method': 'Sum', 'file_path': 'tests/proteinGroups_small_cut.txt'}), call({'feature_orientation': 'Columns (samples in rows, features in columns)', 'file_path': 'tests/metadata_cut_columns.csv'}), call({'percentage': 0.5}) diff --git a/tests/test_data/DIANN_data/correct_protein_df.csv b/tests/test_data/DIANN_data/correct_protein_df.csv index 6c3c6ddb..016ccc9a 100644 --- a/tests/test_data/DIANN_data/correct_protein_df.csv +++ b/tests/test_data/DIANN_data/correct_protein_df.csv @@ -1,1734 +1,1378 @@ Sample,Protein ID,Gene,Intensity -LM07061,A0A024QZP7;E5RIU6;P06493,,525657.0 -LM07061,A0A024QZX5;A0A087X1N8;A0A2R8YD12;P35237,,933891.0 -LM07061,A0A024R4E5;C9J5E5;C9JES8;C9JHS7;C9JHZ8;C9JIZ1;C9JZI8;H7C0A4;Q00341,,60915.4 -LM07061,A0A024R5G9;A0A7I2V2K3;A0A7I2V313;A0A7I2V3E7;A0A7I2V3L9;A0A7I2V3M3;A0A7I2V3V9;A0A7I2V3X1;A0A7I2V411;A0A7I2V4A8;A0A7I2V5B9;A0A7I2V5F8;A0A7I2V5Q1;A0A7I2YQB3;A0A7I2YQH8;A0A7I2YQW3;H0YD65;H0YE42;Q9UBX1,,117841.0 -LM07061,A0A024RA28;A0A7I2V323;A0A7I2V3P1;A0A7I2V4I6;A0A7I2V4N1;P22626,,556189.0 -LM07061,A0A024RA52;A0A7I2V2H3;P25787,,2207490.0 -LM07061,A0A024RA87;A0A0U1RRE1;A0A0U1RRH9;E7EPK1;E7ES33;G3V1Q4;Q16181;Q6ZU15,,127109.0 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LM07088,Q9GZX9,,36454.4 +LM07088,Q9GZZ1,, +LM07088,Q9GZZ9,, +LM07088,Q9H074,,47567.0 +LM07088,Q9H082,, LM07088,Q9H0A0,, LM07088,Q9H0B6,, LM07088,Q9H0D6,, LM07088,Q9H0S4,, +LM07088,Q9H0W9,,95617.9 +LM07088,Q9H173,,115770.0 LM07088,Q9H1E3,,163955.0 +LM07088,Q9H299,, +LM07088,Q9H2H8,, LM07088,Q9H2P9,, +LM07088,Q9H2U2,,412452.0 +LM07088,Q9H3K6,,61099.4 LM07088,Q9H3P7,, +LM07088,Q9H3U1,, LM07088,Q9H444,,106936.0 LM07088,Q9H488,, +LM07088,Q9H497,, +LM07088,Q9H4A4,,673043.0 LM07088,Q9H4F8,,1054830.0 LM07088,Q9H552,, +LM07088,Q9H5K3,, LM07088,Q9H6B4,,224398.0 LM07088,Q9H6F5,,53302.2 +LM07088,Q9H6S3,,116732.0 LM07088,Q9H6T3,, +LM07088,Q9H6X2,,264458.0 +LM07088,Q9H6Z4,, LM07088,Q9H773,,67077.6 +LM07088,Q9H910,,87008.2 +LM07088,Q9H9G7,,106611.0 +LM07088,Q9H9Q2,, LM07088,Q9HAT2,,751589.0 +LM07088,Q9HAV0,,309640.0 LM07088,Q9HAV4,, +LM07088,Q9HB07,,142657.0 +LM07088,Q9HB40,,160559.0 +LM07088,Q9HB63,,232030.0 LM07088,Q9HB71,,680599.0 +LM07088,Q9HC35,, LM07088,Q9HC38,,1078060.0 +LM07088,Q9HCE1,, LM07088,Q9HCN8,, LM07088,Q9HCU4,,140487.0 LM07088,Q9HCY8,, +LM07088,Q9HD42,, LM07088,Q9N2I2,,390930.0 LM07088,Q9NP58,, +LM07088,Q9NP79,,101081.0 +LM07088,Q9NPD8,,49544.5 +LM07088,Q9NPF0,, +LM07088,Q9NPH2,, +LM07088,Q9NPH3,,360099.0 +LM07088,Q9NQ29,, +LM07088,Q9NQ55,, LM07088,Q9NQ88,, LM07088,Q9NQC3,,223224.0 +LM07088,Q9NQG5,, LM07088,Q9NQH7,,1879630.0 +LM07088,Q9NQP4,, +LM07088,Q9NQR4,, LM07088,Q9NQT4,, +LM07088,Q9NQW7,, +LM07088,Q9NR30,,325575.0 +LM07088,Q9NR45,,59463.4 +LM07088,Q9NR50,, +LM07088,Q9NRG0,, +LM07088,Q9NS15,,155444.0 +LM07088,Q9NSB2,, LM07088,Q9NSB4,, LM07088,Q9NSD9,,78913.4 +LM07088,Q9NTJ3,,98605.5 +LM07088,Q9NTK5,,265815.0 LM07088,Q9NTM9,,381341.0 +LM07088,Q9NTX5,, LM07088,Q9NTZ6,,39836.9 +LM07088,Q9NUP9,,54683.8 LM07088,Q9NUQ3,, +LM07088,Q9NUQ9,,123811.0 +LM07088,Q9NUU7,, +LM07088,Q9NVM4,, +LM07088,Q9NVS9,, LM07088,Q9NVX2,, LM07088,Q9NWV4,,51641.6 +LM07088,Q9NX24,, LM07088,Q9NX46,,70751.3 LM07088,Q9NXG2,, LM07088,Q9NXH9,,84438.9 +LM07088,Q9NXR7,, LM07088,Q9NY12,, +LM07088,Q9NY27,, +LM07088,Q9NY33,,390623.0 +LM07088,Q9NYF8,, +LM07088,Q9NYL9,,50003.0 LM07088,Q9NYU2,, LM07088,Q9NZ08,,242926.0 LM07088,Q9NZI8,, +LM07088,Q9NZL9,,511381.0 LM07088,Q9NZM1,, +LM07088,Q9NZP8,,88041.2 LM07088,Q9NZT1,, +LM07088,Q9NZT2,, +LM07088,Q9NZV1,,96468.6 +LM07088,Q9P0V9,, LM07088,Q9P258,,245662.0 LM07088,Q9P287,, +LM07088,Q9P289,,130393.0 LM07088,Q9P2B2,,85667.9 +LM07088,Q9P2E9,,168601.0 +LM07088,Q9P2J5,,71763.3 +LM07088,Q9P2R7,,467325.0 +LM07088,Q9P2T1,,83452.9 LM07088,Q9TRI1,,292318.0 +LM07088,Q9UBB4,, +LM07088,Q9UBE0,,61621.1 LM07088,Q9UBF2,, LM07088,Q9UBK8,, +LM07088,Q9UBQ0,,119717.0 +LM07088,Q9UBQ5,,388442.0 LM07088,Q9UBQ6,,322605.0 -LM07088,Q9UBQ7;U3KQ56,, +LM07088,Q9UBQ7,, +LM07088,Q9UBR2,,9518750.0 LM07088,Q9UBS4,, +LM07088,Q9UBT2,,101393.0 +LM07088,Q9UBW8,, +LM07088,Q9UBX1,,44550.6 +LM07088,Q9UBX5,, +LM07088,Q9UDY2,, LM07088,Q9UEE9,, -LM07088,Q9UHL4;R4GN05;R4GNE8,,1207460.0 +LM07088,Q9UGI8,, +LM07088,Q9UHB6,,377298.0 +LM07088,Q9UHD1,,207707.0 +LM07088,Q9UHD8,,127895.0 +LM07088,Q9UHI8,, +LM07088,Q9UHL4,,1207460.0 LM07088,Q9UHV9,,141297.0 +LM07088,Q9UHX1,,273722.0 +LM07088,Q9UHY7,,157736.0 +LM07088,Q9UI10,, LM07088,Q9UI30,,69829.0 +LM07088,Q9UI42,,7516300.0 +LM07088,Q9UIA9,, LM07088,Q9UII2,,42873.8 LM07088,Q9UIQ6,, +LM07088,Q9UJH8,, +LM07088,Q9UK22,, +LM07088,Q9UK76,,154785.0 +LM07088,Q9UKA9,,259321.0 LM07088,Q9UKD2,,47086.5 +LM07088,Q9UKK9,,243855.0 +LM07088,Q9UKM9,,85059.3 +LM07088,Q9UKS6,, +LM07088,Q9UKV3,,66742.7 +LM07088,Q9UKY7,,267886.0 +LM07088,Q9UKZ9,,1683020.0 +LM07088,Q9UL46,,251537.0 +LM07088,Q9ULA0,,119948.0 +LM07088,Q9ULF5,,694735.0 +LM07088,Q9ULV4,,146229.0 LM07088,Q9ULW8,, LM07088,Q9UM22,,1258500.0 LM07088,Q9UMF0,,125351.0 +LM07088,Q9UMR5,,851914.0 +LM07088,Q9UMS4,,311648.0 LM07088,Q9UMX5,,372650.0 +LM07088,Q9UMY4,, +LM07088,Q9UN86,, LM07088,Q9UNE7,,99324.0 +LM07088,Q9UNF0,, +LM07088,Q9UNF1,, +LM07088,Q9UNH7,, +LM07088,Q9UNM6,,234464.0 +LM07088,Q9UNN5,,47841.5 +LM07088,Q9UNN8,,120650.0 +LM07088,Q9UNP9,,157243.0 +LM07088,Q9UNQ2,, +LM07088,Q9UNS2,, LM07088,Q9UNW1,,257838.0 +LM07088,Q9UNZ2,,54116.6 LM07088,Q9UQ35,, +LM07088,Q9UQ53,, +LM07088,Q9UQ80,,1882000.0 LM07088,Q9UQE7,,496748.0 +LM07088,Q9UQN3,, LM07088,Q9Y223,, +LM07088,Q9Y224,, +LM07088,Q9Y230,,343222.0 +LM07088,Q9Y237,, +LM07088,Q9Y251,,219073.0 +LM07088,Q9Y262,,197878.0 +LM07088,Q9Y263,, +LM07088,Q9Y265,,426174.0 +LM07088,Q9Y266,,346593.0 LM07088,Q9Y281,,233284.0 +LM07088,Q9Y285,,70769.9 +LM07088,Q9Y287,,197312.0 +LM07088,Q9Y2B0,,900579.0 +LM07088,Q9Y2E5,,100570.0 +LM07088,Q9Y2L1,, +LM07088,Q9Y2L5,, LM07088,Q9Y2P4,, LM07088,Q9Y2S6,,450793.0 +LM07088,Q9Y2T3,,788866.0 +LM07088,Q9Y2W1,, LM07088,Q9Y2X3,,62451.1 LM07088,Q9Y2Z0,,52629.4 +LM07088,Q9Y303,, +LM07088,Q9Y315,, LM07088,Q9Y316,, +LM07088,Q9Y376,,82396.0 +LM07088,Q9Y383,,66142.9 +LM07088,Q9Y3A5,,145870.0 LM07088,Q9Y3B4,, +LM07088,Q9Y3B8,, +LM07088,Q9Y3C1,, LM07088,Q9Y3C6,,140936.0 LM07088,Q9Y3C8,,312004.0 +LM07088,Q9Y3D8,, +LM07088,Q9Y3F4,,268965.0 LM07088,Q9Y3I0,,162421.0 LM07088,Q9Y3R4,, +LM07088,Q9Y3U8,,1827180.0 LM07088,Q9Y446,, +LM07088,Q9Y450,, LM07088,Q9Y490,,130430.0 LM07088,Q9Y4K0,,2271200.0 +LM07088,Q9Y4K1,, +LM07088,Q9Y4L1,, LM07088,Q9Y4X5,, +LM07088,Q9Y520,, +LM07088,Q9Y547,,71813.0 LM07088,Q9Y570,, LM07088,Q9Y5B9,,415465.0 +LM07088,Q9Y5K5,,227559.0 +LM07088,Q9Y5L0,, LM07088,Q9Y5S9,,160860.0 +LM07088,Q9Y5V0,,320886.0 LM07088,Q9Y605,, LM07088,Q9Y617,,979564.0 LM07088,Q9Y678,,97088.8 LM07088,Q9Y696,, +LM07088,Q9Y6E2,, +LM07088,Q9Y6G9,, LM07088,Q9Y6L7,,1080180.0 LM07088,Q9Y6M5,, +LM07088,Q9Y6N7,,141226.0 diff --git a/tests/test_data/data_import/maxquant_small_noproteincolumn.tsv b/tests/test_data/data_import/maxquant_small_noproteincolumn.tsv index 3b73fc32..b116ff47 100644 --- a/tests/test_data/data_import/maxquant_small_noproteincolumn.tsv +++ b/tests/test_data/data_import/maxquant_small_noproteincolumn.tsv @@ -1,30 +1,30 @@ -Majority protein IDs Peptide counts (all) Peptide counts (razor+unique) Peptide counts (unique) Protein names Gene names Fasta headers Number of proteins Peptides Razor + unique peptides Unique peptides Peptides AD01_C1_INSOLUBLE_01 Peptides AD01_C1_INSOLUBLE_02 Peptides AD01_C1_INSOLUBLE_03 Peptides AD01_C2_INSOLUBLE_01 Peptides AD02_C1_INSOLUBLE_01 Peptides AD02_C1_INSOLUBLE_02 Peptides AD02_C2_INSOLUBLE_01 Peptides AD03_C1_INSOLUBLE_01 Peptides AD03_C1_INSOLUBLE_02 Peptides AD03_C1_INSOLUBLE_03 Peptides AD03_C2_INSOLUBLE_01 Peptides AD04_C1_INSOLUBLE_01 Peptides AD04_C2_INSOLUBLE_01 Peptides AD05_C2_INSOLUBLE_01 Peptides AD06_C1_INSOLUBLE_01 Peptides AD07_C1_INSOLUBLE_01 Peptides AD07_C1_INSOLUBLE_02 Peptides AD07_C1_INSOLUBLE_03 Peptides AD07_C2_INSOLUBLE_01 Peptides AD08_C2_INSOLUBLE_01 Peptides AD09_C1_INSOLUBLE_01 Peptides AD10_C1_INSOLUBLE_01 Peptides AD10_C2_INSOLUBLE_01 Peptides AD11_C2_INSOLUBLE_01 Peptides AD12_C2_INSOLUBLE_01 Peptides AD13_C2_INSOLUBLE_01 Peptides AD14_C2_INSOLUBLE_01 Peptides AD15_C2_INSOLUBLE_01 Peptides AD16_C2_INSOLUBLE_01 Peptides AD17_C2_INSOLUBLE_01 Peptides AD18_C2_INSOLUBLE_01 Peptides AD19_C2_INSOLUBLE_01 Peptides AD20_C1_INSOLUBLE_01 Peptides AD21_C1_INSOLUBLE_01 Peptides AD21_C2_INSOLUBLE_01 Peptides AD22_C1_INSOLUBLE_01 Peptides AD23_C1_INSOLUBLE_01 Peptides AD23_C1_INSOLUBLE_02 Peptides AD23_C2_INSOLUBLE_01 Peptides AD24_C1_INSOLUBLE_01 Peptides AD24_C1_INSOLUBLE_02 Peptides AD25_C1_INSOLUBLE_01 Peptides AD26_C1_INSOLUBLE_01 Peptides AD27_C1_INSOLUBLE_01 Peptides AD27_C1_INSOLUBLE_02 Peptides AD28_C2_INSOLUBLE_01 Peptides AD29_C1_INSOLUBLE_01 Peptides AD30_C1_INSOLUBLE_01 Peptides AD30_C1_INSOLUBLE_02 Peptides AD30_C2_INSOLUBLE_01 Peptides AD31_C2_INSOLUBLE_01 Peptides AD32_C2_INSOLUBLE_01 Peptides AD33_C2_INSOLUBLE_01 Peptides AD34_C1_INSOLUBLE_01 Peptides AD34_C1_INSOLUBLE_02 Peptides AD35_C1_INSOLUBLE_01 Peptides AD35_C1_INSOLUBLE_02 Peptides AD36_C1_INSOLUBLE_01 Peptides AD37_C1_INSOLUBLE_01 Peptides AD37_C2_INSOLUBLE_01 Peptides AD38_C1_INSOLUBLE_01 Peptides AD38_C1_INSOLUBLE_02 Peptides AD38_C1_INSOLUBLE_03 Peptides AD39_C2_INSOLUBLE_01 Peptides AD40_C2_INSOLUBLE_01 Peptides AD41_C2_INSOLUBLE_01 Peptides AD42_C2_INSOLUBLE_01 Peptides AD43_C1_INSOLUBLE_01 Peptides AD44_C1_INSOLUBLE_01 Peptides AD44_C1_INSOLUBLE_02 Peptides AD44_C1_INSOLUBLE_03 Peptides AD44_C1_INSOLUBLE_04 Peptides AD45_C1_INSOLUBLE_01 Peptides AD45_C1_INSOLUBLE_02 Peptides AD46_C1_INSOLUBLE_01 Peptides AD46_C1_INSOLUBLE_02 Peptides AD46_C1_INSOLUBLE_03 Peptides AD46_C2_INSOLUBLE_01 Peptides AD47_C1_INSOLUBLE_01 Peptides AD48_C2_INSOLUBLE_01 Peptides AD49_C2_INSOLUBLE_01 Peptides CTR01_C1_INSOLUBLE_01 Peptides CTR02_C1_INSOLUBLE_01 Peptides CTR03_C1_INSOLUBLE_01 Peptides CTR04_C1_INSOLUBLE_01 Peptides CTR05_C2_INSOLUBLE_01 Peptides CTR06_C2_INSOLUBLE_01 Peptides CTR07_C1_INSOLUBLE_01 Peptides CTR08_C1_INSOLUBLE_01 Peptides CTR08_C2_INSOLUBLE_01 Peptides CTR09_C2_INSOLUBLE_01 Peptides CTR10_C1_INSOLUBLE_01 Peptides CTR10_C2_INSOLUBLE_01 Peptides CTR11_C2_INSOLUBLE_01 Peptides CTR12_C2_INSOLUBLE_01 Peptides CTR13_C2_INSOLUBLE_01 Peptides CTR14_C2_INSOLUBLE_01 Peptides CTR15_C2_INSOLUBLE_01 Peptides CTR16_C2_INSOLUBLE_01 Peptides CTR17_C2_INSOLUBLE_01 Peptides CTR18_C2_INSOLUBLE_01 Peptides CTR19_C1_INSOLUBLE_01 Peptides CTR20_C1_INSOLUBLE_01 Peptides CTR21_C2_INSOLUBLE_01 Peptides CTR22_C2_INSOLUBLE_01 Peptides CTR23_C2_INSOLUBLE_01 Peptides CTR24_C1_INSOLUBLE_01 Peptides CTR25_C1_INSOLUBLE_01 Peptides CTR26_C2_INSOLUBLE_01 Peptides CTR27_C1_INSOLUBLE_01 Peptides CTR28_C1_INSOLUBLE_01 Peptides CTR28_C1_INSOLUBLE_02 Peptides CTR28_C2_INSOLUBLE_01 Peptides CTR29_C1_INSOLUBLE_01 Peptides CTR29_C1_INSOLUBLE_02 Peptides CTR29_C1_INSOLUBLE_03 Peptides CTR30_C1_INSOLUBLE_01 Peptides CTR30_C1_INSOLUBLE_02 Peptides CTR30_C2_INSOLUBLE_01 Peptides CTR31_C1_INSOLUBLE_01 Peptides CTR31_C2_INSOLUBLE_01 Peptides CTR32_C1_INSOLUBLE_01 Peptides CTR32_C2_INSOLUBLE_01 Peptides CTR33_C1_INSOLUBLE_01 Peptides CTR34_C1_INSOLUBLE_01 Peptides CTR34_C2_INSOLUBLE_01 Peptides CTR35_C1_INSOLUBLE_01 Peptides CTR36_C1_INSOLUBLE_01 Peptides CTR36_C1_INSOLUBLE_02 Peptides CTR37_C1_INSOLUBLE_01 Peptides CTR38_C1_INSOLUBLE_01 Peptides CTR39_C1_INSOLUBLE_01 Peptides CTR40_C1_INSOLUBLE_01 Peptides CTR40_C1_INSOLUBLE_02 Peptides CTR40_C1_INSOLUBLE_03 Peptides CTR41_C1_INSOLUBLE_01 Peptides CTR41_C1_INSOLUBLE_02 Peptides CTR41_C1_INSOLUBLE_03 Peptides CTR42_C1_INSOLUBLE_01 Peptides CTR42_C1_INSOLUBLE_02 Peptides CTR42_C1_INSOLUBLE_03 Peptides CTR43_C2_INSOLUBLE_01 Peptides CTR44_C1_INSOLUBLE_01 Razor + unique peptides AD01_C1_INSOLUBLE_01 Razor + unique peptides AD01_C1_INSOLUBLE_02 Razor + unique peptides AD01_C1_INSOLUBLE_03 Razor + unique peptides AD01_C2_INSOLUBLE_01 Razor + unique peptides AD02_C1_INSOLUBLE_01 Razor + unique peptides AD02_C1_INSOLUBLE_02 Razor + unique peptides AD02_C2_INSOLUBLE_01 Razor + unique peptides AD03_C1_INSOLUBLE_01 Razor + unique peptides AD03_C1_INSOLUBLE_02 Razor + unique peptides AD03_C1_INSOLUBLE_03 Razor + unique peptides AD03_C2_INSOLUBLE_01 Razor + unique peptides AD04_C1_INSOLUBLE_01 Razor + unique peptides AD04_C2_INSOLUBLE_01 Razor + unique peptides AD05_C2_INSOLUBLE_01 Razor + unique peptides AD06_C1_INSOLUBLE_01 Razor + unique peptides AD07_C1_INSOLUBLE_01 Razor + unique peptides AD07_C1_INSOLUBLE_02 Razor + unique peptides AD07_C1_INSOLUBLE_03 Razor + unique peptides AD07_C2_INSOLUBLE_01 Razor + unique peptides AD08_C2_INSOLUBLE_01 Razor + unique peptides AD09_C1_INSOLUBLE_01 Razor + unique peptides AD10_C1_INSOLUBLE_01 Razor + unique peptides AD10_C2_INSOLUBLE_01 Razor + unique peptides AD11_C2_INSOLUBLE_01 Razor + unique peptides AD12_C2_INSOLUBLE_01 Razor + unique peptides AD13_C2_INSOLUBLE_01 Razor + unique peptides AD14_C2_INSOLUBLE_01 Razor + unique peptides AD15_C2_INSOLUBLE_01 Razor + unique peptides AD16_C2_INSOLUBLE_01 Razor + unique peptides AD17_C2_INSOLUBLE_01 Razor + unique peptides AD18_C2_INSOLUBLE_01 Razor + unique peptides AD19_C2_INSOLUBLE_01 Razor + unique peptides AD20_C1_INSOLUBLE_01 Razor + unique peptides AD21_C1_INSOLUBLE_01 Razor + unique peptides AD21_C2_INSOLUBLE_01 Razor + unique peptides AD22_C1_INSOLUBLE_01 Razor + unique peptides AD23_C1_INSOLUBLE_01 Razor + unique peptides AD23_C1_INSOLUBLE_02 Razor + unique peptides AD23_C2_INSOLUBLE_01 Razor + unique peptides AD24_C1_INSOLUBLE_01 Razor + unique peptides AD24_C1_INSOLUBLE_02 Razor + unique peptides AD25_C1_INSOLUBLE_01 Razor + unique peptides AD26_C1_INSOLUBLE_01 Razor + unique peptides AD27_C1_INSOLUBLE_01 Razor + unique peptides AD27_C1_INSOLUBLE_02 Razor + unique peptides AD28_C2_INSOLUBLE_01 Razor + unique peptides AD29_C1_INSOLUBLE_01 Razor + unique peptides AD30_C1_INSOLUBLE_01 Razor + unique peptides AD30_C1_INSOLUBLE_02 Razor + unique peptides AD30_C2_INSOLUBLE_01 Razor + unique peptides AD31_C2_INSOLUBLE_01 Razor + unique peptides AD32_C2_INSOLUBLE_01 Razor + unique peptides AD33_C2_INSOLUBLE_01 Razor + unique peptides AD34_C1_INSOLUBLE_01 Razor + unique peptides AD34_C1_INSOLUBLE_02 Razor + unique peptides AD35_C1_INSOLUBLE_01 Razor + unique peptides AD35_C1_INSOLUBLE_02 Razor + unique peptides AD36_C1_INSOLUBLE_01 Razor + unique peptides AD37_C1_INSOLUBLE_01 Razor + unique peptides AD37_C2_INSOLUBLE_01 Razor + unique peptides AD38_C1_INSOLUBLE_01 Razor + unique peptides AD38_C1_INSOLUBLE_02 Razor + unique peptides AD38_C1_INSOLUBLE_03 Razor + unique peptides AD39_C2_INSOLUBLE_01 Razor + unique peptides AD40_C2_INSOLUBLE_01 Razor + unique peptides AD41_C2_INSOLUBLE_01 Razor + unique peptides AD42_C2_INSOLUBLE_01 Razor + unique peptides AD43_C1_INSOLUBLE_01 Razor + unique peptides AD44_C1_INSOLUBLE_01 Razor + unique peptides AD44_C1_INSOLUBLE_02 Razor + unique peptides AD44_C1_INSOLUBLE_03 Razor + unique peptides AD44_C1_INSOLUBLE_04 Razor + unique peptides AD45_C1_INSOLUBLE_01 Razor + unique peptides AD45_C1_INSOLUBLE_02 Razor + unique peptides AD46_C1_INSOLUBLE_01 Razor + unique peptides AD46_C1_INSOLUBLE_02 Razor + unique peptides AD46_C1_INSOLUBLE_03 Razor + unique peptides AD46_C2_INSOLUBLE_01 Razor + unique peptides AD47_C1_INSOLUBLE_01 Razor + unique peptides AD48_C2_INSOLUBLE_01 Razor + unique peptides AD49_C2_INSOLUBLE_01 Razor + unique peptides CTR01_C1_INSOLUBLE_01 Razor + unique peptides CTR02_C1_INSOLUBLE_01 Razor + unique peptides CTR03_C1_INSOLUBLE_01 Razor + unique peptides CTR04_C1_INSOLUBLE_01 Razor + unique peptides CTR05_C2_INSOLUBLE_01 Razor + unique peptides CTR06_C2_INSOLUBLE_01 Razor + unique peptides CTR07_C1_INSOLUBLE_01 Razor + unique peptides CTR08_C1_INSOLUBLE_01 Razor + unique peptides CTR08_C2_INSOLUBLE_01 Razor + unique peptides CTR09_C2_INSOLUBLE_01 Razor + unique peptides CTR10_C1_INSOLUBLE_01 Razor + unique peptides CTR10_C2_INSOLUBLE_01 Razor + unique peptides CTR11_C2_INSOLUBLE_01 Razor + unique peptides CTR12_C2_INSOLUBLE_01 Razor + unique peptides CTR13_C2_INSOLUBLE_01 Razor + unique peptides CTR14_C2_INSOLUBLE_01 Razor + unique peptides CTR15_C2_INSOLUBLE_01 Razor + unique peptides CTR16_C2_INSOLUBLE_01 Razor + unique peptides CTR17_C2_INSOLUBLE_01 Razor + unique peptides CTR18_C2_INSOLUBLE_01 Razor + unique peptides CTR19_C1_INSOLUBLE_01 Razor + unique peptides CTR20_C1_INSOLUBLE_01 Razor + unique peptides CTR21_C2_INSOLUBLE_01 Razor + unique peptides CTR22_C2_INSOLUBLE_01 Razor + unique peptides CTR23_C2_INSOLUBLE_01 Razor + unique peptides CTR24_C1_INSOLUBLE_01 Razor + unique peptides CTR25_C1_INSOLUBLE_01 Razor + unique peptides CTR26_C2_INSOLUBLE_01 Razor + unique peptides CTR27_C1_INSOLUBLE_01 Razor + unique peptides CTR28_C1_INSOLUBLE_01 Razor + unique peptides CTR28_C1_INSOLUBLE_02 Razor + unique peptides CTR28_C2_INSOLUBLE_01 Razor + unique peptides CTR29_C1_INSOLUBLE_01 Razor + unique peptides CTR29_C1_INSOLUBLE_02 Razor + unique peptides CTR29_C1_INSOLUBLE_03 Razor + unique peptides CTR30_C1_INSOLUBLE_01 Razor + unique peptides CTR30_C1_INSOLUBLE_02 Razor + unique peptides CTR30_C2_INSOLUBLE_01 Razor + unique peptides CTR31_C1_INSOLUBLE_01 Razor + unique peptides CTR31_C2_INSOLUBLE_01 Razor + unique peptides CTR32_C1_INSOLUBLE_01 Razor + unique peptides CTR32_C2_INSOLUBLE_01 Razor + unique peptides CTR33_C1_INSOLUBLE_01 Razor + unique peptides CTR34_C1_INSOLUBLE_01 Razor + unique peptides CTR34_C2_INSOLUBLE_01 Razor + unique peptides CTR35_C1_INSOLUBLE_01 Razor + unique peptides CTR36_C1_INSOLUBLE_01 Razor + unique peptides CTR36_C1_INSOLUBLE_02 Razor + unique peptides CTR37_C1_INSOLUBLE_01 Razor + unique peptides CTR38_C1_INSOLUBLE_01 Razor + unique peptides CTR39_C1_INSOLUBLE_01 Razor + unique peptides CTR40_C1_INSOLUBLE_01 Razor + unique peptides CTR40_C1_INSOLUBLE_02 Razor + unique peptides CTR40_C1_INSOLUBLE_03 Razor + unique peptides CTR41_C1_INSOLUBLE_01 Razor + unique peptides CTR41_C1_INSOLUBLE_02 Razor + unique peptides CTR41_C1_INSOLUBLE_03 Razor + unique peptides CTR42_C1_INSOLUBLE_01 Razor + unique peptides CTR42_C1_INSOLUBLE_02 Razor + unique peptides CTR42_C1_INSOLUBLE_03 Razor + unique peptides CTR43_C2_INSOLUBLE_01 Razor + unique peptides CTR44_C1_INSOLUBLE_01 Unique peptides AD01_C1_INSOLUBLE_01 Unique peptides AD01_C1_INSOLUBLE_02 Unique peptides AD01_C1_INSOLUBLE_03 Unique peptides AD01_C2_INSOLUBLE_01 Unique peptides AD02_C1_INSOLUBLE_01 Unique peptides AD02_C1_INSOLUBLE_02 Unique peptides AD02_C2_INSOLUBLE_01 Unique peptides AD03_C1_INSOLUBLE_01 Unique peptides AD03_C1_INSOLUBLE_02 Unique peptides AD03_C1_INSOLUBLE_03 Unique peptides AD03_C2_INSOLUBLE_01 Unique peptides AD04_C1_INSOLUBLE_01 Unique peptides AD04_C2_INSOLUBLE_01 Unique peptides AD05_C2_INSOLUBLE_01 Unique peptides AD06_C1_INSOLUBLE_01 Unique peptides AD07_C1_INSOLUBLE_01 Unique peptides AD07_C1_INSOLUBLE_02 Unique peptides AD07_C1_INSOLUBLE_03 Unique peptides AD07_C2_INSOLUBLE_01 Unique peptides AD08_C2_INSOLUBLE_01 Unique peptides AD09_C1_INSOLUBLE_01 Unique peptides AD10_C1_INSOLUBLE_01 Unique peptides AD10_C2_INSOLUBLE_01 Unique peptides AD11_C2_INSOLUBLE_01 Unique peptides AD12_C2_INSOLUBLE_01 Unique peptides AD13_C2_INSOLUBLE_01 Unique peptides AD14_C2_INSOLUBLE_01 Unique peptides AD15_C2_INSOLUBLE_01 Unique peptides AD16_C2_INSOLUBLE_01 Unique peptides AD17_C2_INSOLUBLE_01 Unique peptides AD18_C2_INSOLUBLE_01 Unique peptides AD19_C2_INSOLUBLE_01 Unique peptides AD20_C1_INSOLUBLE_01 Unique peptides AD21_C1_INSOLUBLE_01 Unique peptides AD21_C2_INSOLUBLE_01 Unique peptides AD22_C1_INSOLUBLE_01 Unique peptides AD23_C1_INSOLUBLE_01 Unique peptides AD23_C1_INSOLUBLE_02 Unique peptides AD23_C2_INSOLUBLE_01 Unique peptides AD24_C1_INSOLUBLE_01 Unique peptides AD24_C1_INSOLUBLE_02 Unique peptides AD25_C1_INSOLUBLE_01 Unique peptides AD26_C1_INSOLUBLE_01 Unique peptides AD27_C1_INSOLUBLE_01 Unique peptides AD27_C1_INSOLUBLE_02 Unique peptides AD28_C2_INSOLUBLE_01 Unique peptides AD29_C1_INSOLUBLE_01 Unique peptides AD30_C1_INSOLUBLE_01 Unique peptides AD30_C1_INSOLUBLE_02 Unique peptides AD30_C2_INSOLUBLE_01 Unique peptides AD31_C2_INSOLUBLE_01 Unique peptides AD32_C2_INSOLUBLE_01 Unique peptides AD33_C2_INSOLUBLE_01 Unique peptides AD34_C1_INSOLUBLE_01 Unique peptides AD34_C1_INSOLUBLE_02 Unique peptides AD35_C1_INSOLUBLE_01 Unique peptides AD35_C1_INSOLUBLE_02 Unique peptides AD36_C1_INSOLUBLE_01 Unique peptides AD37_C1_INSOLUBLE_01 Unique peptides AD37_C2_INSOLUBLE_01 Unique peptides AD38_C1_INSOLUBLE_01 Unique peptides AD38_C1_INSOLUBLE_02 Unique peptides AD38_C1_INSOLUBLE_03 Unique peptides AD39_C2_INSOLUBLE_01 Unique peptides AD40_C2_INSOLUBLE_01 Unique peptides AD41_C2_INSOLUBLE_01 Unique peptides AD42_C2_INSOLUBLE_01 Unique peptides AD43_C1_INSOLUBLE_01 Unique peptides AD44_C1_INSOLUBLE_01 Unique peptides AD44_C1_INSOLUBLE_02 Unique peptides AD44_C1_INSOLUBLE_03 Unique peptides AD44_C1_INSOLUBLE_04 Unique peptides AD45_C1_INSOLUBLE_01 Unique peptides AD45_C1_INSOLUBLE_02 Unique peptides AD46_C1_INSOLUBLE_01 Unique peptides AD46_C1_INSOLUBLE_02 Unique peptides AD46_C1_INSOLUBLE_03 Unique peptides AD46_C2_INSOLUBLE_01 Unique peptides AD47_C1_INSOLUBLE_01 Unique peptides AD48_C2_INSOLUBLE_01 Unique peptides AD49_C2_INSOLUBLE_01 Unique peptides CTR01_C1_INSOLUBLE_01 Unique peptides CTR02_C1_INSOLUBLE_01 Unique peptides CTR03_C1_INSOLUBLE_01 Unique peptides CTR04_C1_INSOLUBLE_01 Unique peptides CTR05_C2_INSOLUBLE_01 Unique peptides CTR06_C2_INSOLUBLE_01 Unique peptides CTR07_C1_INSOLUBLE_01 Unique peptides CTR08_C1_INSOLUBLE_01 Unique peptides CTR08_C2_INSOLUBLE_01 Unique peptides CTR09_C2_INSOLUBLE_01 Unique peptides CTR10_C1_INSOLUBLE_01 Unique peptides CTR10_C2_INSOLUBLE_01 Unique peptides CTR11_C2_INSOLUBLE_01 Unique peptides CTR12_C2_INSOLUBLE_01 Unique peptides CTR13_C2_INSOLUBLE_01 Unique peptides CTR14_C2_INSOLUBLE_01 Unique peptides CTR15_C2_INSOLUBLE_01 Unique peptides CTR16_C2_INSOLUBLE_01 Unique peptides CTR17_C2_INSOLUBLE_01 Unique peptides CTR18_C2_INSOLUBLE_01 Unique peptides CTR19_C1_INSOLUBLE_01 Unique peptides CTR20_C1_INSOLUBLE_01 Unique peptides CTR21_C2_INSOLUBLE_01 Unique peptides CTR22_C2_INSOLUBLE_01 Unique peptides CTR23_C2_INSOLUBLE_01 Unique peptides CTR24_C1_INSOLUBLE_01 Unique peptides CTR25_C1_INSOLUBLE_01 Unique peptides CTR26_C2_INSOLUBLE_01 Unique peptides CTR27_C1_INSOLUBLE_01 Unique peptides CTR28_C1_INSOLUBLE_01 Unique peptides CTR28_C1_INSOLUBLE_02 Unique peptides CTR28_C2_INSOLUBLE_01 Unique peptides CTR29_C1_INSOLUBLE_01 Unique peptides CTR29_C1_INSOLUBLE_02 Unique peptides CTR29_C1_INSOLUBLE_03 Unique peptides CTR30_C1_INSOLUBLE_01 Unique peptides CTR30_C1_INSOLUBLE_02 Unique peptides CTR30_C2_INSOLUBLE_01 Unique peptides CTR31_C1_INSOLUBLE_01 Unique peptides CTR31_C2_INSOLUBLE_01 Unique peptides CTR32_C1_INSOLUBLE_01 Unique peptides CTR32_C2_INSOLUBLE_01 Unique peptides CTR33_C1_INSOLUBLE_01 Unique peptides CTR34_C1_INSOLUBLE_01 Unique peptides CTR34_C2_INSOLUBLE_01 Unique peptides CTR35_C1_INSOLUBLE_01 Unique peptides CTR36_C1_INSOLUBLE_01 Unique peptides CTR36_C1_INSOLUBLE_02 Unique peptides CTR37_C1_INSOLUBLE_01 Unique peptides CTR38_C1_INSOLUBLE_01 Unique peptides CTR39_C1_INSOLUBLE_01 Unique peptides CTR40_C1_INSOLUBLE_01 Unique peptides CTR40_C1_INSOLUBLE_02 Unique peptides CTR40_C1_INSOLUBLE_03 Unique peptides CTR41_C1_INSOLUBLE_01 Unique peptides CTR41_C1_INSOLUBLE_02 Unique peptides CTR41_C1_INSOLUBLE_03 Unique peptides CTR42_C1_INSOLUBLE_01 Unique peptides CTR42_C1_INSOLUBLE_02 Unique peptides CTR42_C1_INSOLUBLE_03 Unique peptides CTR43_C2_INSOLUBLE_01 Unique peptides CTR44_C1_INSOLUBLE_01 Sequence coverage [%] Unique + razor sequence coverage [%] Unique sequence coverage [%] Mol. weight [kDa] Sequence length Sequence lengths Q-value Score Identification type AD01_C1_INSOLUBLE_01 Identification type AD01_C1_INSOLUBLE_02 Identification type AD01_C1_INSOLUBLE_03 Identification type AD01_C2_INSOLUBLE_01 Identification type AD02_C1_INSOLUBLE_01 Identification type AD02_C1_INSOLUBLE_02 Identification type AD02_C2_INSOLUBLE_01 Identification type AD03_C1_INSOLUBLE_01 Identification type AD03_C1_INSOLUBLE_02 Identification type AD03_C1_INSOLUBLE_03 Identification type AD03_C2_INSOLUBLE_01 Identification type AD04_C1_INSOLUBLE_01 Identification type AD04_C2_INSOLUBLE_01 Identification type AD05_C2_INSOLUBLE_01 Identification type AD06_C1_INSOLUBLE_01 Identification type AD07_C1_INSOLUBLE_01 Identification type AD07_C1_INSOLUBLE_02 Identification type AD07_C1_INSOLUBLE_03 Identification type AD07_C2_INSOLUBLE_01 Identification type AD08_C2_INSOLUBLE_01 Identification type AD09_C1_INSOLUBLE_01 Identification type AD10_C1_INSOLUBLE_01 Identification type AD10_C2_INSOLUBLE_01 Identification type AD11_C2_INSOLUBLE_01 Identification type AD12_C2_INSOLUBLE_01 Identification type AD13_C2_INSOLUBLE_01 Identification type AD14_C2_INSOLUBLE_01 Identification type AD15_C2_INSOLUBLE_01 Identification type AD16_C2_INSOLUBLE_01 Identification type AD17_C2_INSOLUBLE_01 Identification type AD18_C2_INSOLUBLE_01 Identification type AD19_C2_INSOLUBLE_01 Identification type AD20_C1_INSOLUBLE_01 Identification type AD21_C1_INSOLUBLE_01 Identification type AD21_C2_INSOLUBLE_01 Identification type AD22_C1_INSOLUBLE_01 Identification type AD23_C1_INSOLUBLE_01 Identification type AD23_C1_INSOLUBLE_02 Identification type AD23_C2_INSOLUBLE_01 Identification type AD24_C1_INSOLUBLE_01 Identification type AD24_C1_INSOLUBLE_02 Identification type AD25_C1_INSOLUBLE_01 Identification type AD26_C1_INSOLUBLE_01 Identification type AD27_C1_INSOLUBLE_01 Identification type AD27_C1_INSOLUBLE_02 Identification type AD28_C2_INSOLUBLE_01 Identification type AD29_C1_INSOLUBLE_01 Identification type AD30_C1_INSOLUBLE_01 Identification type AD30_C1_INSOLUBLE_02 Identification type AD30_C2_INSOLUBLE_01 Identification type AD31_C2_INSOLUBLE_01 Identification type AD32_C2_INSOLUBLE_01 Identification type AD33_C2_INSOLUBLE_01 Identification type AD34_C1_INSOLUBLE_01 Identification type AD34_C1_INSOLUBLE_02 Identification type AD35_C1_INSOLUBLE_01 Identification type AD35_C1_INSOLUBLE_02 Identification type AD36_C1_INSOLUBLE_01 Identification type AD37_C1_INSOLUBLE_01 Identification type AD37_C2_INSOLUBLE_01 Identification type AD38_C1_INSOLUBLE_01 Identification type AD38_C1_INSOLUBLE_02 Identification type AD38_C1_INSOLUBLE_03 Identification type AD39_C2_INSOLUBLE_01 Identification type AD40_C2_INSOLUBLE_01 Identification type AD41_C2_INSOLUBLE_01 Identification type AD42_C2_INSOLUBLE_01 Identification type AD43_C1_INSOLUBLE_01 Identification type AD44_C1_INSOLUBLE_01 Identification type AD44_C1_INSOLUBLE_02 Identification type AD44_C1_INSOLUBLE_03 Identification type AD44_C1_INSOLUBLE_04 Identification type AD45_C1_INSOLUBLE_01 Identification type AD45_C1_INSOLUBLE_02 Identification type AD46_C1_INSOLUBLE_01 Identification type AD46_C1_INSOLUBLE_02 Identification type AD46_C1_INSOLUBLE_03 Identification type AD46_C2_INSOLUBLE_01 Identification type AD47_C1_INSOLUBLE_01 Identification type AD48_C2_INSOLUBLE_01 Identification type AD49_C2_INSOLUBLE_01 Identification type CTR01_C1_INSOLUBLE_01 Identification type CTR02_C1_INSOLUBLE_01 Identification type CTR03_C1_INSOLUBLE_01 Identification type CTR04_C1_INSOLUBLE_01 Identification type CTR05_C2_INSOLUBLE_01 Identification type CTR06_C2_INSOLUBLE_01 Identification type CTR07_C1_INSOLUBLE_01 Identification type CTR08_C1_INSOLUBLE_01 Identification type CTR08_C2_INSOLUBLE_01 Identification type CTR09_C2_INSOLUBLE_01 Identification type CTR10_C1_INSOLUBLE_01 Identification type CTR10_C2_INSOLUBLE_01 Identification type CTR11_C2_INSOLUBLE_01 Identification type CTR12_C2_INSOLUBLE_01 Identification type CTR13_C2_INSOLUBLE_01 Identification type CTR14_C2_INSOLUBLE_01 Identification type CTR15_C2_INSOLUBLE_01 Identification type CTR16_C2_INSOLUBLE_01 Identification type CTR17_C2_INSOLUBLE_01 Identification type CTR18_C2_INSOLUBLE_01 Identification type CTR19_C1_INSOLUBLE_01 Identification type CTR20_C1_INSOLUBLE_01 Identification type CTR21_C2_INSOLUBLE_01 Identification type CTR22_C2_INSOLUBLE_01 Identification type CTR23_C2_INSOLUBLE_01 Identification type CTR24_C1_INSOLUBLE_01 Identification type CTR25_C1_INSOLUBLE_01 Identification type CTR26_C2_INSOLUBLE_01 Identification type CTR27_C1_INSOLUBLE_01 Identification type CTR28_C1_INSOLUBLE_01 Identification type CTR28_C1_INSOLUBLE_02 Identification type CTR28_C2_INSOLUBLE_01 Identification type CTR29_C1_INSOLUBLE_01 Identification type CTR29_C1_INSOLUBLE_02 Identification type CTR29_C1_INSOLUBLE_03 Identification type CTR30_C1_INSOLUBLE_01 Identification type CTR30_C1_INSOLUBLE_02 Identification type CTR30_C2_INSOLUBLE_01 Identification type CTR31_C1_INSOLUBLE_01 Identification type CTR31_C2_INSOLUBLE_01 Identification type CTR32_C1_INSOLUBLE_01 Identification type CTR32_C2_INSOLUBLE_01 Identification type CTR33_C1_INSOLUBLE_01 Identification type CTR34_C1_INSOLUBLE_01 Identification type CTR34_C2_INSOLUBLE_01 Identification type CTR35_C1_INSOLUBLE_01 Identification type CTR36_C1_INSOLUBLE_01 Identification type CTR36_C1_INSOLUBLE_02 Identification type CTR37_C1_INSOLUBLE_01 Identification type CTR38_C1_INSOLUBLE_01 Identification type CTR39_C1_INSOLUBLE_01 Identification type CTR40_C1_INSOLUBLE_01 Identification type CTR40_C1_INSOLUBLE_02 Identification type CTR40_C1_INSOLUBLE_03 Identification type CTR41_C1_INSOLUBLE_01 Identification type CTR41_C1_INSOLUBLE_02 Identification type CTR41_C1_INSOLUBLE_03 Identification type CTR42_C1_INSOLUBLE_01 Identification type CTR42_C1_INSOLUBLE_02 Identification type CTR42_C1_INSOLUBLE_03 Identification type CTR43_C2_INSOLUBLE_01 Identification type CTR44_C1_INSOLUBLE_01 Sequence coverage AD01_C1_INSOLUBLE_01 [%] Sequence coverage AD01_C1_INSOLUBLE_02 [%] Sequence coverage AD01_C1_INSOLUBLE_03 [%] Sequence coverage AD01_C2_INSOLUBLE_01 [%] Sequence coverage AD02_C1_INSOLUBLE_01 [%] Sequence coverage AD02_C1_INSOLUBLE_02 [%] Sequence coverage AD02_C2_INSOLUBLE_01 [%] Sequence coverage AD03_C1_INSOLUBLE_01 [%] Sequence coverage AD03_C1_INSOLUBLE_02 [%] Sequence coverage AD03_C1_INSOLUBLE_03 [%] Sequence coverage AD03_C2_INSOLUBLE_01 [%] Sequence coverage AD04_C1_INSOLUBLE_01 [%] Sequence coverage AD04_C2_INSOLUBLE_01 [%] Sequence coverage AD05_C2_INSOLUBLE_01 [%] Sequence coverage AD06_C1_INSOLUBLE_01 [%] Sequence coverage AD07_C1_INSOLUBLE_01 [%] Sequence coverage AD07_C1_INSOLUBLE_02 [%] Sequence coverage AD07_C1_INSOLUBLE_03 [%] Sequence coverage AD07_C2_INSOLUBLE_01 [%] Sequence coverage AD08_C2_INSOLUBLE_01 [%] Sequence coverage AD09_C1_INSOLUBLE_01 [%] Sequence coverage AD10_C1_INSOLUBLE_01 [%] Sequence coverage AD10_C2_INSOLUBLE_01 [%] Sequence coverage AD11_C2_INSOLUBLE_01 [%] Sequence coverage AD12_C2_INSOLUBLE_01 [%] Sequence coverage AD13_C2_INSOLUBLE_01 [%] Sequence coverage AD14_C2_INSOLUBLE_01 [%] Sequence coverage AD15_C2_INSOLUBLE_01 [%] Sequence coverage AD16_C2_INSOLUBLE_01 [%] Sequence coverage AD17_C2_INSOLUBLE_01 [%] Sequence coverage AD18_C2_INSOLUBLE_01 [%] Sequence coverage AD19_C2_INSOLUBLE_01 [%] Sequence coverage AD20_C1_INSOLUBLE_01 [%] Sequence coverage AD21_C1_INSOLUBLE_01 [%] Sequence coverage AD21_C2_INSOLUBLE_01 [%] Sequence coverage AD22_C1_INSOLUBLE_01 [%] Sequence coverage AD23_C1_INSOLUBLE_01 [%] Sequence coverage AD23_C1_INSOLUBLE_02 [%] Sequence coverage AD23_C2_INSOLUBLE_01 [%] Sequence coverage AD24_C1_INSOLUBLE_01 [%] Sequence coverage AD24_C1_INSOLUBLE_02 [%] Sequence coverage AD25_C1_INSOLUBLE_01 [%] Sequence coverage AD26_C1_INSOLUBLE_01 [%] Sequence coverage AD27_C1_INSOLUBLE_01 [%] Sequence coverage AD27_C1_INSOLUBLE_02 [%] Sequence coverage AD28_C2_INSOLUBLE_01 [%] Sequence coverage AD29_C1_INSOLUBLE_01 [%] Sequence coverage AD30_C1_INSOLUBLE_01 [%] Sequence coverage AD30_C1_INSOLUBLE_02 [%] Sequence coverage AD30_C2_INSOLUBLE_01 [%] Sequence coverage AD31_C2_INSOLUBLE_01 [%] Sequence coverage AD32_C2_INSOLUBLE_01 [%] Sequence coverage AD33_C2_INSOLUBLE_01 [%] Sequence coverage AD34_C1_INSOLUBLE_01 [%] Sequence coverage AD34_C1_INSOLUBLE_02 [%] Sequence coverage AD35_C1_INSOLUBLE_01 [%] Sequence coverage AD35_C1_INSOLUBLE_02 [%] Sequence coverage AD36_C1_INSOLUBLE_01 [%] Sequence coverage AD37_C1_INSOLUBLE_01 [%] Sequence coverage AD37_C2_INSOLUBLE_01 [%] Sequence coverage AD38_C1_INSOLUBLE_01 [%] Sequence coverage AD38_C1_INSOLUBLE_02 [%] Sequence coverage AD38_C1_INSOLUBLE_03 [%] Sequence coverage AD39_C2_INSOLUBLE_01 [%] Sequence coverage AD40_C2_INSOLUBLE_01 [%] Sequence coverage AD41_C2_INSOLUBLE_01 [%] Sequence coverage AD42_C2_INSOLUBLE_01 [%] Sequence coverage AD43_C1_INSOLUBLE_01 [%] Sequence coverage AD44_C1_INSOLUBLE_01 [%] Sequence coverage AD44_C1_INSOLUBLE_02 [%] Sequence coverage AD44_C1_INSOLUBLE_03 [%] Sequence coverage AD44_C1_INSOLUBLE_04 [%] Sequence coverage AD45_C1_INSOLUBLE_01 [%] Sequence coverage AD45_C1_INSOLUBLE_02 [%] Sequence coverage AD46_C1_INSOLUBLE_01 [%] Sequence coverage AD46_C1_INSOLUBLE_02 [%] Sequence coverage AD46_C1_INSOLUBLE_03 [%] Sequence coverage AD46_C2_INSOLUBLE_01 [%] Sequence coverage AD47_C1_INSOLUBLE_01 [%] Sequence coverage AD48_C2_INSOLUBLE_01 [%] Sequence coverage AD49_C2_INSOLUBLE_01 [%] Sequence coverage CTR01_C1_INSOLUBLE_01 [%] Sequence coverage CTR02_C1_INSOLUBLE_01 [%] Sequence coverage CTR03_C1_INSOLUBLE_01 [%] Sequence coverage CTR04_C1_INSOLUBLE_01 [%] Sequence coverage CTR05_C2_INSOLUBLE_01 [%] Sequence coverage CTR06_C2_INSOLUBLE_01 [%] Sequence coverage CTR07_C1_INSOLUBLE_01 [%] Sequence coverage CTR08_C1_INSOLUBLE_01 [%] Sequence coverage CTR08_C2_INSOLUBLE_01 [%] Sequence coverage CTR09_C2_INSOLUBLE_01 [%] Sequence coverage CTR10_C1_INSOLUBLE_01 [%] Sequence coverage CTR10_C2_INSOLUBLE_01 [%] Sequence coverage CTR11_C2_INSOLUBLE_01 [%] Sequence coverage CTR12_C2_INSOLUBLE_01 [%] Sequence coverage CTR13_C2_INSOLUBLE_01 [%] Sequence coverage CTR14_C2_INSOLUBLE_01 [%] Sequence coverage CTR15_C2_INSOLUBLE_01 [%] Sequence coverage CTR16_C2_INSOLUBLE_01 [%] Sequence coverage CTR17_C2_INSOLUBLE_01 [%] Sequence coverage CTR18_C2_INSOLUBLE_01 [%] Sequence coverage CTR19_C1_INSOLUBLE_01 [%] Sequence coverage CTR20_C1_INSOLUBLE_01 [%] Sequence coverage CTR21_C2_INSOLUBLE_01 [%] Sequence coverage CTR22_C2_INSOLUBLE_01 [%] Sequence coverage CTR23_C2_INSOLUBLE_01 [%] Sequence coverage CTR24_C1_INSOLUBLE_01 [%] Sequence coverage CTR25_C1_INSOLUBLE_01 [%] Sequence coverage CTR26_C2_INSOLUBLE_01 [%] Sequence coverage CTR27_C1_INSOLUBLE_01 [%] Sequence coverage CTR28_C1_INSOLUBLE_01 [%] Sequence coverage CTR28_C1_INSOLUBLE_02 [%] Sequence coverage CTR28_C2_INSOLUBLE_01 [%] Sequence coverage CTR29_C1_INSOLUBLE_01 [%] Sequence coverage CTR29_C1_INSOLUBLE_02 [%] Sequence coverage CTR29_C1_INSOLUBLE_03 [%] Sequence coverage CTR30_C1_INSOLUBLE_01 [%] Sequence coverage CTR30_C1_INSOLUBLE_02 [%] Sequence coverage CTR30_C2_INSOLUBLE_01 [%] Sequence coverage CTR31_C1_INSOLUBLE_01 [%] Sequence coverage CTR31_C2_INSOLUBLE_01 [%] Sequence coverage CTR32_C1_INSOLUBLE_01 [%] Sequence coverage CTR32_C2_INSOLUBLE_01 [%] Sequence coverage CTR33_C1_INSOLUBLE_01 [%] Sequence coverage CTR34_C1_INSOLUBLE_01 [%] Sequence coverage CTR34_C2_INSOLUBLE_01 [%] Sequence coverage CTR35_C1_INSOLUBLE_01 [%] Sequence coverage CTR36_C1_INSOLUBLE_01 [%] Sequence coverage CTR36_C1_INSOLUBLE_02 [%] Sequence coverage CTR37_C1_INSOLUBLE_01 [%] Sequence coverage CTR38_C1_INSOLUBLE_01 [%] Sequence coverage CTR39_C1_INSOLUBLE_01 [%] Sequence coverage CTR40_C1_INSOLUBLE_01 [%] Sequence coverage CTR40_C1_INSOLUBLE_02 [%] Sequence coverage CTR40_C1_INSOLUBLE_03 [%] Sequence coverage CTR41_C1_INSOLUBLE_01 [%] Sequence coverage CTR41_C1_INSOLUBLE_02 [%] Sequence coverage CTR41_C1_INSOLUBLE_03 [%] Sequence coverage CTR42_C1_INSOLUBLE_01 [%] Sequence coverage CTR42_C1_INSOLUBLE_02 [%] Sequence coverage CTR42_C1_INSOLUBLE_03 [%] Sequence coverage CTR43_C2_INSOLUBLE_01 [%] Sequence coverage CTR44_C1_INSOLUBLE_01 [%] Intensity Intensity AD01_C1_INSOLUBLE_01 Intensity AD01_C1_INSOLUBLE_02 Intensity AD01_C1_INSOLUBLE_03 Intensity AD01_C2_INSOLUBLE_01 Intensity AD02_C1_INSOLUBLE_01 Intensity AD02_C1_INSOLUBLE_02 Intensity AD02_C2_INSOLUBLE_01 Intensity AD03_C1_INSOLUBLE_01 Intensity AD03_C1_INSOLUBLE_02 Intensity AD03_C1_INSOLUBLE_03 Intensity AD03_C2_INSOLUBLE_01 Intensity AD04_C1_INSOLUBLE_01 Intensity AD04_C2_INSOLUBLE_01 Intensity AD05_C2_INSOLUBLE_01 Intensity AD06_C1_INSOLUBLE_01 Intensity AD07_C1_INSOLUBLE_01 Intensity AD07_C1_INSOLUBLE_02 Intensity AD07_C1_INSOLUBLE_03 Intensity AD07_C2_INSOLUBLE_01 Intensity AD08_C2_INSOLUBLE_01 Intensity AD09_C1_INSOLUBLE_01 Intensity AD10_C1_INSOLUBLE_01 Intensity AD10_C2_INSOLUBLE_01 Intensity AD11_C2_INSOLUBLE_01 Intensity AD12_C2_INSOLUBLE_01 Intensity AD13_C2_INSOLUBLE_01 Intensity AD14_C2_INSOLUBLE_01 Intensity AD15_C2_INSOLUBLE_01 Intensity AD16_C2_INSOLUBLE_01 Intensity AD17_C2_INSOLUBLE_01 Intensity AD18_C2_INSOLUBLE_01 Intensity AD19_C2_INSOLUBLE_01 Intensity AD20_C1_INSOLUBLE_01 Intensity AD21_C1_INSOLUBLE_01 Intensity AD21_C2_INSOLUBLE_01 Intensity AD22_C1_INSOLUBLE_01 Intensity AD23_C1_INSOLUBLE_01 Intensity AD23_C1_INSOLUBLE_02 Intensity AD23_C2_INSOLUBLE_01 Intensity AD24_C1_INSOLUBLE_01 Intensity AD24_C1_INSOLUBLE_02 Intensity AD25_C1_INSOLUBLE_01 Intensity AD26_C1_INSOLUBLE_01 Intensity AD27_C1_INSOLUBLE_01 Intensity AD27_C1_INSOLUBLE_02 Intensity AD28_C2_INSOLUBLE_01 Intensity AD29_C1_INSOLUBLE_01 Intensity AD30_C1_INSOLUBLE_01 Intensity AD30_C1_INSOLUBLE_02 Intensity AD30_C2_INSOLUBLE_01 Intensity AD31_C2_INSOLUBLE_01 Intensity AD32_C2_INSOLUBLE_01 Intensity AD33_C2_INSOLUBLE_01 Intensity AD34_C1_INSOLUBLE_01 Intensity AD34_C1_INSOLUBLE_02 Intensity AD35_C1_INSOLUBLE_01 Intensity AD35_C1_INSOLUBLE_02 Intensity AD36_C1_INSOLUBLE_01 Intensity AD37_C1_INSOLUBLE_01 Intensity AD37_C2_INSOLUBLE_01 Intensity AD38_C1_INSOLUBLE_01 Intensity AD38_C1_INSOLUBLE_02 Intensity AD38_C1_INSOLUBLE_03 Intensity AD39_C2_INSOLUBLE_01 Intensity AD40_C2_INSOLUBLE_01 Intensity AD41_C2_INSOLUBLE_01 Intensity AD42_C2_INSOLUBLE_01 Intensity AD43_C1_INSOLUBLE_01 Intensity AD44_C1_INSOLUBLE_01 Intensity AD44_C1_INSOLUBLE_02 Intensity AD44_C1_INSOLUBLE_03 Intensity AD44_C1_INSOLUBLE_04 Intensity AD45_C1_INSOLUBLE_01 Intensity AD45_C1_INSOLUBLE_02 Intensity AD46_C1_INSOLUBLE_01 Intensity AD46_C1_INSOLUBLE_02 Intensity AD46_C1_INSOLUBLE_03 Intensity AD46_C2_INSOLUBLE_01 Intensity AD47_C1_INSOLUBLE_01 Intensity AD48_C2_INSOLUBLE_01 Intensity AD49_C2_INSOLUBLE_01 Intensity CTR01_C1_INSOLUBLE_01 Intensity CTR02_C1_INSOLUBLE_01 Intensity CTR03_C1_INSOLUBLE_01 Intensity CTR04_C1_INSOLUBLE_01 Intensity CTR05_C2_INSOLUBLE_01 Intensity CTR06_C2_INSOLUBLE_01 Intensity CTR07_C1_INSOLUBLE_01 Intensity CTR08_C1_INSOLUBLE_01 Intensity CTR08_C2_INSOLUBLE_01 Intensity CTR09_C2_INSOLUBLE_01 Intensity CTR10_C1_INSOLUBLE_01 Intensity CTR10_C2_INSOLUBLE_01 Intensity CTR11_C2_INSOLUBLE_01 Intensity CTR12_C2_INSOLUBLE_01 Intensity CTR13_C2_INSOLUBLE_01 Intensity CTR14_C2_INSOLUBLE_01 Intensity CTR15_C2_INSOLUBLE_01 Intensity CTR16_C2_INSOLUBLE_01 Intensity CTR17_C2_INSOLUBLE_01 Intensity CTR18_C2_INSOLUBLE_01 Intensity CTR19_C1_INSOLUBLE_01 Intensity CTR20_C1_INSOLUBLE_01 Intensity CTR21_C2_INSOLUBLE_01 Intensity CTR22_C2_INSOLUBLE_01 Intensity CTR23_C2_INSOLUBLE_01 Intensity CTR24_C1_INSOLUBLE_01 Intensity CTR25_C1_INSOLUBLE_01 Intensity CTR26_C2_INSOLUBLE_01 Intensity CTR27_C1_INSOLUBLE_01 Intensity CTR28_C1_INSOLUBLE_01 Intensity CTR28_C1_INSOLUBLE_02 Intensity CTR28_C2_INSOLUBLE_01 Intensity CTR29_C1_INSOLUBLE_01 Intensity CTR29_C1_INSOLUBLE_02 Intensity CTR29_C1_INSOLUBLE_03 Intensity CTR30_C1_INSOLUBLE_01 Intensity CTR30_C1_INSOLUBLE_02 Intensity CTR30_C2_INSOLUBLE_01 Intensity CTR31_C1_INSOLUBLE_01 Intensity CTR31_C2_INSOLUBLE_01 Intensity CTR32_C1_INSOLUBLE_01 Intensity CTR32_C2_INSOLUBLE_01 Intensity CTR33_C1_INSOLUBLE_01 Intensity CTR34_C1_INSOLUBLE_01 Intensity CTR34_C2_INSOLUBLE_01 Intensity CTR35_C1_INSOLUBLE_01 Intensity CTR36_C1_INSOLUBLE_01 Intensity CTR36_C1_INSOLUBLE_02 Intensity CTR37_C1_INSOLUBLE_01 Intensity CTR38_C1_INSOLUBLE_01 Intensity CTR39_C1_INSOLUBLE_01 Intensity CTR40_C1_INSOLUBLE_01 Intensity CTR40_C1_INSOLUBLE_02 Intensity CTR40_C1_INSOLUBLE_03 Intensity CTR41_C1_INSOLUBLE_01 Intensity CTR41_C1_INSOLUBLE_02 Intensity CTR41_C1_INSOLUBLE_03 Intensity CTR42_C1_INSOLUBLE_01 Intensity CTR42_C1_INSOLUBLE_02 Intensity CTR42_C1_INSOLUBLE_03 Intensity CTR43_C2_INSOLUBLE_01 Intensity CTR44_C1_INSOLUBLE_01 iBAQ peptides iBAQ iBAQ AD01_C1_INSOLUBLE_01 iBAQ AD01_C1_INSOLUBLE_02 iBAQ AD01_C1_INSOLUBLE_03 iBAQ AD01_C2_INSOLUBLE_01 iBAQ AD02_C1_INSOLUBLE_01 iBAQ AD02_C1_INSOLUBLE_02 iBAQ AD02_C2_INSOLUBLE_01 iBAQ AD03_C1_INSOLUBLE_01 iBAQ AD03_C1_INSOLUBLE_02 iBAQ AD03_C1_INSOLUBLE_03 iBAQ AD03_C2_INSOLUBLE_01 iBAQ AD04_C1_INSOLUBLE_01 iBAQ AD04_C2_INSOLUBLE_01 iBAQ AD05_C2_INSOLUBLE_01 iBAQ AD06_C1_INSOLUBLE_01 iBAQ AD07_C1_INSOLUBLE_01 iBAQ AD07_C1_INSOLUBLE_02 iBAQ AD07_C1_INSOLUBLE_03 iBAQ AD07_C2_INSOLUBLE_01 iBAQ AD08_C2_INSOLUBLE_01 iBAQ AD09_C1_INSOLUBLE_01 iBAQ AD10_C1_INSOLUBLE_01 iBAQ AD10_C2_INSOLUBLE_01 iBAQ AD11_C2_INSOLUBLE_01 iBAQ AD12_C2_INSOLUBLE_01 iBAQ AD13_C2_INSOLUBLE_01 iBAQ AD14_C2_INSOLUBLE_01 iBAQ AD15_C2_INSOLUBLE_01 iBAQ AD16_C2_INSOLUBLE_01 iBAQ AD17_C2_INSOLUBLE_01 iBAQ AD18_C2_INSOLUBLE_01 iBAQ AD19_C2_INSOLUBLE_01 iBAQ AD20_C1_INSOLUBLE_01 iBAQ AD21_C1_INSOLUBLE_01 iBAQ AD21_C2_INSOLUBLE_01 iBAQ AD22_C1_INSOLUBLE_01 iBAQ AD23_C1_INSOLUBLE_01 iBAQ AD23_C1_INSOLUBLE_02 iBAQ AD23_C2_INSOLUBLE_01 iBAQ AD24_C1_INSOLUBLE_01 iBAQ AD24_C1_INSOLUBLE_02 iBAQ AD25_C1_INSOLUBLE_01 iBAQ AD26_C1_INSOLUBLE_01 iBAQ AD27_C1_INSOLUBLE_01 iBAQ AD27_C1_INSOLUBLE_02 iBAQ AD28_C2_INSOLUBLE_01 iBAQ AD29_C1_INSOLUBLE_01 iBAQ AD30_C1_INSOLUBLE_01 iBAQ AD30_C1_INSOLUBLE_02 iBAQ AD30_C2_INSOLUBLE_01 iBAQ AD31_C2_INSOLUBLE_01 iBAQ AD32_C2_INSOLUBLE_01 iBAQ AD33_C2_INSOLUBLE_01 iBAQ AD34_C1_INSOLUBLE_01 iBAQ AD34_C1_INSOLUBLE_02 iBAQ AD35_C1_INSOLUBLE_01 iBAQ AD35_C1_INSOLUBLE_02 iBAQ AD36_C1_INSOLUBLE_01 iBAQ AD37_C1_INSOLUBLE_01 iBAQ AD37_C2_INSOLUBLE_01 iBAQ AD38_C1_INSOLUBLE_01 iBAQ AD38_C1_INSOLUBLE_02 iBAQ AD38_C1_INSOLUBLE_03 iBAQ AD39_C2_INSOLUBLE_01 iBAQ AD40_C2_INSOLUBLE_01 iBAQ AD41_C2_INSOLUBLE_01 iBAQ AD42_C2_INSOLUBLE_01 iBAQ AD43_C1_INSOLUBLE_01 iBAQ AD44_C1_INSOLUBLE_01 iBAQ AD44_C1_INSOLUBLE_02 iBAQ AD44_C1_INSOLUBLE_03 iBAQ AD44_C1_INSOLUBLE_04 iBAQ AD45_C1_INSOLUBLE_01 iBAQ AD45_C1_INSOLUBLE_02 iBAQ AD46_C1_INSOLUBLE_01 iBAQ AD46_C1_INSOLUBLE_02 iBAQ AD46_C1_INSOLUBLE_03 iBAQ AD46_C2_INSOLUBLE_01 iBAQ AD47_C1_INSOLUBLE_01 iBAQ AD48_C2_INSOLUBLE_01 iBAQ AD49_C2_INSOLUBLE_01 iBAQ CTR01_C1_INSOLUBLE_01 iBAQ CTR02_C1_INSOLUBLE_01 iBAQ CTR03_C1_INSOLUBLE_01 iBAQ CTR04_C1_INSOLUBLE_01 iBAQ CTR05_C2_INSOLUBLE_01 iBAQ CTR06_C2_INSOLUBLE_01 iBAQ CTR07_C1_INSOLUBLE_01 iBAQ CTR08_C1_INSOLUBLE_01 iBAQ CTR08_C2_INSOLUBLE_01 iBAQ CTR09_C2_INSOLUBLE_01 iBAQ CTR10_C1_INSOLUBLE_01 iBAQ CTR10_C2_INSOLUBLE_01 iBAQ CTR11_C2_INSOLUBLE_01 iBAQ CTR12_C2_INSOLUBLE_01 iBAQ CTR13_C2_INSOLUBLE_01 iBAQ CTR14_C2_INSOLUBLE_01 iBAQ CTR15_C2_INSOLUBLE_01 iBAQ CTR16_C2_INSOLUBLE_01 iBAQ CTR17_C2_INSOLUBLE_01 iBAQ CTR18_C2_INSOLUBLE_01 iBAQ CTR19_C1_INSOLUBLE_01 iBAQ CTR20_C1_INSOLUBLE_01 iBAQ CTR21_C2_INSOLUBLE_01 iBAQ CTR22_C2_INSOLUBLE_01 iBAQ CTR23_C2_INSOLUBLE_01 iBAQ CTR24_C1_INSOLUBLE_01 iBAQ CTR25_C1_INSOLUBLE_01 iBAQ CTR26_C2_INSOLUBLE_01 iBAQ CTR27_C1_INSOLUBLE_01 iBAQ CTR28_C1_INSOLUBLE_01 iBAQ CTR28_C1_INSOLUBLE_02 iBAQ CTR28_C2_INSOLUBLE_01 iBAQ CTR29_C1_INSOLUBLE_01 iBAQ CTR29_C1_INSOLUBLE_02 iBAQ CTR29_C1_INSOLUBLE_03 iBAQ CTR30_C1_INSOLUBLE_01 iBAQ CTR30_C1_INSOLUBLE_02 iBAQ CTR30_C2_INSOLUBLE_01 iBAQ CTR31_C1_INSOLUBLE_01 iBAQ CTR31_C2_INSOLUBLE_01 iBAQ CTR32_C1_INSOLUBLE_01 iBAQ CTR32_C2_INSOLUBLE_01 iBAQ CTR33_C1_INSOLUBLE_01 iBAQ CTR34_C1_INSOLUBLE_01 iBAQ CTR34_C2_INSOLUBLE_01 iBAQ CTR35_C1_INSOLUBLE_01 iBAQ CTR36_C1_INSOLUBLE_01 iBAQ CTR36_C1_INSOLUBLE_02 iBAQ CTR37_C1_INSOLUBLE_01 iBAQ CTR38_C1_INSOLUBLE_01 iBAQ CTR39_C1_INSOLUBLE_01 iBAQ CTR40_C1_INSOLUBLE_01 iBAQ CTR40_C1_INSOLUBLE_02 iBAQ CTR40_C1_INSOLUBLE_03 iBAQ CTR41_C1_INSOLUBLE_01 iBAQ CTR41_C1_INSOLUBLE_02 iBAQ CTR41_C1_INSOLUBLE_03 iBAQ CTR42_C1_INSOLUBLE_01 iBAQ CTR42_C1_INSOLUBLE_02 iBAQ CTR42_C1_INSOLUBLE_03 iBAQ CTR43_C2_INSOLUBLE_01 iBAQ CTR44_C1_INSOLUBLE_01 LFQ intensity AD01_C1_INSOLUBLE_01 LFQ intensity AD01_C1_INSOLUBLE_02 LFQ intensity AD01_C1_INSOLUBLE_03 LFQ intensity AD01_C2_INSOLUBLE_01 LFQ intensity AD02_C1_INSOLUBLE_01 LFQ intensity AD02_C1_INSOLUBLE_02 LFQ intensity AD02_C2_INSOLUBLE_01 LFQ intensity AD03_C1_INSOLUBLE_01 LFQ intensity AD03_C1_INSOLUBLE_02 LFQ intensity AD03_C1_INSOLUBLE_03 LFQ intensity AD03_C2_INSOLUBLE_01 LFQ intensity AD04_C1_INSOLUBLE_01 LFQ intensity AD04_C2_INSOLUBLE_01 LFQ intensity AD05_C2_INSOLUBLE_01 LFQ intensity AD06_C1_INSOLUBLE_01 LFQ intensity AD07_C1_INSOLUBLE_01 LFQ intensity AD07_C1_INSOLUBLE_02 LFQ intensity AD07_C1_INSOLUBLE_03 LFQ intensity AD07_C2_INSOLUBLE_01 LFQ intensity AD08_C2_INSOLUBLE_01 LFQ intensity AD09_C1_INSOLUBLE_01 LFQ intensity AD10_C1_INSOLUBLE_01 LFQ intensity AD10_C2_INSOLUBLE_01 LFQ intensity AD11_C2_INSOLUBLE_01 LFQ intensity AD12_C2_INSOLUBLE_01 LFQ intensity AD13_C2_INSOLUBLE_01 LFQ intensity AD14_C2_INSOLUBLE_01 LFQ intensity AD15_C2_INSOLUBLE_01 LFQ intensity AD16_C2_INSOLUBLE_01 LFQ intensity AD17_C2_INSOLUBLE_01 LFQ intensity AD18_C2_INSOLUBLE_01 LFQ intensity AD19_C2_INSOLUBLE_01 LFQ intensity AD20_C1_INSOLUBLE_01 LFQ intensity AD21_C1_INSOLUBLE_01 LFQ intensity AD21_C2_INSOLUBLE_01 LFQ intensity AD22_C1_INSOLUBLE_01 LFQ intensity AD23_C1_INSOLUBLE_01 LFQ intensity AD23_C1_INSOLUBLE_02 LFQ intensity AD23_C2_INSOLUBLE_01 LFQ intensity AD24_C1_INSOLUBLE_01 LFQ intensity AD24_C1_INSOLUBLE_02 LFQ intensity AD25_C1_INSOLUBLE_01 LFQ intensity AD26_C1_INSOLUBLE_01 LFQ intensity AD27_C1_INSOLUBLE_01 LFQ intensity AD27_C1_INSOLUBLE_02 LFQ intensity AD28_C2_INSOLUBLE_01 LFQ intensity AD29_C1_INSOLUBLE_01 LFQ intensity AD30_C1_INSOLUBLE_01 LFQ intensity AD30_C1_INSOLUBLE_02 LFQ intensity AD30_C2_INSOLUBLE_01 LFQ intensity AD31_C2_INSOLUBLE_01 LFQ intensity AD32_C2_INSOLUBLE_01 LFQ intensity AD33_C2_INSOLUBLE_01 LFQ intensity AD34_C1_INSOLUBLE_01 LFQ intensity AD34_C1_INSOLUBLE_02 LFQ intensity AD35_C1_INSOLUBLE_01 LFQ intensity AD35_C1_INSOLUBLE_02 LFQ intensity AD36_C1_INSOLUBLE_01 LFQ intensity AD37_C1_INSOLUBLE_01 LFQ intensity AD37_C2_INSOLUBLE_01 LFQ intensity AD38_C1_INSOLUBLE_01 LFQ intensity AD38_C1_INSOLUBLE_02 LFQ intensity AD38_C1_INSOLUBLE_03 LFQ intensity AD39_C2_INSOLUBLE_01 LFQ intensity AD40_C2_INSOLUBLE_01 LFQ intensity AD41_C2_INSOLUBLE_01 LFQ intensity AD42_C2_INSOLUBLE_01 LFQ intensity AD43_C1_INSOLUBLE_01 LFQ intensity AD44_C1_INSOLUBLE_01 LFQ intensity AD44_C1_INSOLUBLE_02 LFQ intensity AD44_C1_INSOLUBLE_03 LFQ intensity AD44_C1_INSOLUBLE_04 LFQ intensity AD45_C1_INSOLUBLE_01 LFQ intensity AD45_C1_INSOLUBLE_02 LFQ intensity AD46_C1_INSOLUBLE_01 LFQ intensity AD46_C1_INSOLUBLE_02 LFQ intensity AD46_C1_INSOLUBLE_03 LFQ intensity AD46_C2_INSOLUBLE_01 LFQ intensity AD47_C1_INSOLUBLE_01 LFQ intensity AD48_C2_INSOLUBLE_01 LFQ intensity AD49_C2_INSOLUBLE_01 LFQ intensity CTR01_C1_INSOLUBLE_01 LFQ intensity CTR02_C1_INSOLUBLE_01 LFQ intensity CTR03_C1_INSOLUBLE_01 LFQ intensity CTR04_C1_INSOLUBLE_01 LFQ intensity CTR05_C2_INSOLUBLE_01 LFQ intensity CTR06_C2_INSOLUBLE_01 LFQ intensity CTR07_C1_INSOLUBLE_01 LFQ intensity CTR08_C1_INSOLUBLE_01 LFQ intensity CTR08_C2_INSOLUBLE_01 LFQ intensity CTR09_C2_INSOLUBLE_01 LFQ intensity CTR10_C1_INSOLUBLE_01 LFQ intensity CTR10_C2_INSOLUBLE_01 LFQ intensity CTR11_C2_INSOLUBLE_01 LFQ intensity CTR12_C2_INSOLUBLE_01 LFQ intensity CTR13_C2_INSOLUBLE_01 LFQ intensity CTR14_C2_INSOLUBLE_01 LFQ intensity CTR15_C2_INSOLUBLE_01 LFQ intensity CTR16_C2_INSOLUBLE_01 LFQ intensity CTR17_C2_INSOLUBLE_01 LFQ intensity CTR18_C2_INSOLUBLE_01 LFQ intensity CTR19_C1_INSOLUBLE_01 LFQ intensity CTR20_C1_INSOLUBLE_01 LFQ intensity CTR21_C2_INSOLUBLE_01 LFQ intensity CTR22_C2_INSOLUBLE_01 LFQ intensity CTR23_C2_INSOLUBLE_01 LFQ intensity CTR24_C1_INSOLUBLE_01 LFQ intensity CTR25_C1_INSOLUBLE_01 LFQ intensity CTR26_C2_INSOLUBLE_01 LFQ intensity CTR27_C1_INSOLUBLE_01 LFQ intensity CTR28_C1_INSOLUBLE_01 LFQ intensity CTR28_C1_INSOLUBLE_02 LFQ intensity CTR28_C2_INSOLUBLE_01 LFQ intensity CTR29_C1_INSOLUBLE_01 LFQ intensity CTR29_C1_INSOLUBLE_02 LFQ intensity CTR29_C1_INSOLUBLE_03 LFQ intensity CTR30_C1_INSOLUBLE_01 LFQ intensity CTR30_C1_INSOLUBLE_02 LFQ intensity CTR30_C2_INSOLUBLE_01 LFQ intensity CTR31_C1_INSOLUBLE_01 LFQ intensity CTR31_C2_INSOLUBLE_01 LFQ intensity CTR32_C1_INSOLUBLE_01 LFQ intensity CTR32_C2_INSOLUBLE_01 LFQ intensity CTR33_C1_INSOLUBLE_01 LFQ intensity CTR34_C1_INSOLUBLE_01 LFQ intensity CTR34_C2_INSOLUBLE_01 LFQ intensity CTR35_C1_INSOLUBLE_01 LFQ intensity CTR36_C1_INSOLUBLE_01 LFQ intensity CTR36_C1_INSOLUBLE_02 LFQ intensity CTR37_C1_INSOLUBLE_01 LFQ intensity CTR38_C1_INSOLUBLE_01 LFQ intensity CTR39_C1_INSOLUBLE_01 LFQ intensity CTR40_C1_INSOLUBLE_01 LFQ intensity CTR40_C1_INSOLUBLE_02 LFQ intensity CTR40_C1_INSOLUBLE_03 LFQ intensity CTR41_C1_INSOLUBLE_01 LFQ intensity CTR41_C1_INSOLUBLE_02 LFQ intensity CTR41_C1_INSOLUBLE_03 LFQ intensity CTR42_C1_INSOLUBLE_01 LFQ intensity CTR42_C1_INSOLUBLE_02 LFQ intensity CTR42_C1_INSOLUBLE_03 LFQ intensity CTR43_C2_INSOLUBLE_01 LFQ intensity CTR44_C1_INSOLUBLE_01 MS/MS count AD01_C1_INSOLUBLE_01 MS/MS count AD01_C1_INSOLUBLE_02 MS/MS count AD01_C1_INSOLUBLE_03 MS/MS count AD01_C2_INSOLUBLE_01 MS/MS count AD02_C1_INSOLUBLE_01 MS/MS count AD02_C1_INSOLUBLE_02 MS/MS count AD02_C2_INSOLUBLE_01 MS/MS count AD03_C1_INSOLUBLE_01 MS/MS count AD03_C1_INSOLUBLE_02 MS/MS count AD03_C1_INSOLUBLE_03 MS/MS count AD03_C2_INSOLUBLE_01 MS/MS count AD04_C1_INSOLUBLE_01 MS/MS count AD04_C2_INSOLUBLE_01 MS/MS count AD05_C2_INSOLUBLE_01 MS/MS count AD06_C1_INSOLUBLE_01 MS/MS count AD07_C1_INSOLUBLE_01 MS/MS count AD07_C1_INSOLUBLE_02 MS/MS count AD07_C1_INSOLUBLE_03 MS/MS count AD07_C2_INSOLUBLE_01 MS/MS count AD08_C2_INSOLUBLE_01 MS/MS count AD09_C1_INSOLUBLE_01 MS/MS count AD10_C1_INSOLUBLE_01 MS/MS count AD10_C2_INSOLUBLE_01 MS/MS count AD11_C2_INSOLUBLE_01 MS/MS count AD12_C2_INSOLUBLE_01 MS/MS count AD13_C2_INSOLUBLE_01 MS/MS count AD14_C2_INSOLUBLE_01 MS/MS count AD15_C2_INSOLUBLE_01 MS/MS count AD16_C2_INSOLUBLE_01 MS/MS count AD17_C2_INSOLUBLE_01 MS/MS count AD18_C2_INSOLUBLE_01 MS/MS count AD19_C2_INSOLUBLE_01 MS/MS count AD20_C1_INSOLUBLE_01 MS/MS count AD21_C1_INSOLUBLE_01 MS/MS count AD21_C2_INSOLUBLE_01 MS/MS count AD22_C1_INSOLUBLE_01 MS/MS count AD23_C1_INSOLUBLE_01 MS/MS count AD23_C1_INSOLUBLE_02 MS/MS count AD23_C2_INSOLUBLE_01 MS/MS count AD24_C1_INSOLUBLE_01 MS/MS count AD24_C1_INSOLUBLE_02 MS/MS count AD25_C1_INSOLUBLE_01 MS/MS count AD26_C1_INSOLUBLE_01 MS/MS count AD27_C1_INSOLUBLE_01 MS/MS count AD27_C1_INSOLUBLE_02 MS/MS count AD28_C2_INSOLUBLE_01 MS/MS count AD29_C1_INSOLUBLE_01 MS/MS count AD30_C1_INSOLUBLE_01 MS/MS count AD30_C1_INSOLUBLE_02 MS/MS count AD30_C2_INSOLUBLE_01 MS/MS count AD31_C2_INSOLUBLE_01 MS/MS count AD32_C2_INSOLUBLE_01 MS/MS count AD33_C2_INSOLUBLE_01 MS/MS count AD34_C1_INSOLUBLE_01 MS/MS count AD34_C1_INSOLUBLE_02 MS/MS count AD35_C1_INSOLUBLE_01 MS/MS count AD35_C1_INSOLUBLE_02 MS/MS count AD36_C1_INSOLUBLE_01 MS/MS count AD37_C1_INSOLUBLE_01 MS/MS count AD37_C2_INSOLUBLE_01 MS/MS count AD38_C1_INSOLUBLE_01 MS/MS count AD38_C1_INSOLUBLE_02 MS/MS count AD38_C1_INSOLUBLE_03 MS/MS count AD39_C2_INSOLUBLE_01 MS/MS count AD40_C2_INSOLUBLE_01 MS/MS count AD41_C2_INSOLUBLE_01 MS/MS count AD42_C2_INSOLUBLE_01 MS/MS count AD43_C1_INSOLUBLE_01 MS/MS count AD44_C1_INSOLUBLE_01 MS/MS count AD44_C1_INSOLUBLE_02 MS/MS count AD44_C1_INSOLUBLE_03 MS/MS count AD44_C1_INSOLUBLE_04 MS/MS count AD45_C1_INSOLUBLE_01 MS/MS count AD45_C1_INSOLUBLE_02 MS/MS count AD46_C1_INSOLUBLE_01 MS/MS count AD46_C1_INSOLUBLE_02 MS/MS count AD46_C1_INSOLUBLE_03 MS/MS count AD46_C2_INSOLUBLE_01 MS/MS count AD47_C1_INSOLUBLE_01 MS/MS count AD48_C2_INSOLUBLE_01 MS/MS count AD49_C2_INSOLUBLE_01 MS/MS count CTR01_C1_INSOLUBLE_01 MS/MS count CTR02_C1_INSOLUBLE_01 MS/MS count CTR03_C1_INSOLUBLE_01 MS/MS count CTR04_C1_INSOLUBLE_01 MS/MS count CTR05_C2_INSOLUBLE_01 MS/MS count CTR06_C2_INSOLUBLE_01 MS/MS count CTR07_C1_INSOLUBLE_01 MS/MS count CTR08_C1_INSOLUBLE_01 MS/MS count CTR08_C2_INSOLUBLE_01 MS/MS count CTR09_C2_INSOLUBLE_01 MS/MS count CTR10_C1_INSOLUBLE_01 MS/MS count CTR10_C2_INSOLUBLE_01 MS/MS count CTR11_C2_INSOLUBLE_01 MS/MS count CTR12_C2_INSOLUBLE_01 MS/MS count CTR13_C2_INSOLUBLE_01 MS/MS count CTR14_C2_INSOLUBLE_01 MS/MS count CTR15_C2_INSOLUBLE_01 MS/MS count CTR16_C2_INSOLUBLE_01 MS/MS count CTR17_C2_INSOLUBLE_01 MS/MS count CTR18_C2_INSOLUBLE_01 MS/MS count CTR19_C1_INSOLUBLE_01 MS/MS count CTR20_C1_INSOLUBLE_01 MS/MS count CTR21_C2_INSOLUBLE_01 MS/MS count CTR22_C2_INSOLUBLE_01 MS/MS count CTR23_C2_INSOLUBLE_01 MS/MS count CTR24_C1_INSOLUBLE_01 MS/MS count CTR25_C1_INSOLUBLE_01 MS/MS count CTR26_C2_INSOLUBLE_01 MS/MS count CTR27_C1_INSOLUBLE_01 MS/MS count CTR28_C1_INSOLUBLE_01 MS/MS count CTR28_C1_INSOLUBLE_02 MS/MS count CTR28_C2_INSOLUBLE_01 MS/MS count CTR29_C1_INSOLUBLE_01 MS/MS count CTR29_C1_INSOLUBLE_02 MS/MS count CTR29_C1_INSOLUBLE_03 MS/MS count CTR30_C1_INSOLUBLE_01 MS/MS count CTR30_C1_INSOLUBLE_02 MS/MS count CTR30_C2_INSOLUBLE_01 MS/MS count CTR31_C1_INSOLUBLE_01 MS/MS count CTR31_C2_INSOLUBLE_01 MS/MS count CTR32_C1_INSOLUBLE_01 MS/MS count CTR32_C2_INSOLUBLE_01 MS/MS count CTR33_C1_INSOLUBLE_01 MS/MS count CTR34_C1_INSOLUBLE_01 MS/MS count CTR34_C2_INSOLUBLE_01 MS/MS count CTR35_C1_INSOLUBLE_01 MS/MS count CTR36_C1_INSOLUBLE_01 MS/MS count CTR36_C1_INSOLUBLE_02 MS/MS count CTR37_C1_INSOLUBLE_01 MS/MS count CTR38_C1_INSOLUBLE_01 MS/MS count CTR39_C1_INSOLUBLE_01 MS/MS count CTR40_C1_INSOLUBLE_01 MS/MS count CTR40_C1_INSOLUBLE_02 MS/MS count CTR40_C1_INSOLUBLE_03 MS/MS count CTR41_C1_INSOLUBLE_01 MS/MS count CTR41_C1_INSOLUBLE_02 MS/MS count CTR41_C1_INSOLUBLE_03 MS/MS count CTR42_C1_INSOLUBLE_01 MS/MS count CTR42_C1_INSOLUBLE_02 MS/MS count CTR42_C1_INSOLUBLE_03 MS/MS count CTR43_C2_INSOLUBLE_01 MS/MS count CTR44_C1_INSOLUBLE_01 MS/MS count Only identified by site Reverse Potential contaminant id Peptide IDs Peptide is razor Mod. peptide IDs Evidence IDs MS/MS IDs Best MS/MS Acetyl (K) site IDs GlyGly (K) site IDs Oxidation (M) site IDs Phospho (STY) site IDs Acetyl (K) site positions GlyGly (K) site positions Oxidation (M) site positions Phospho (STY) site positions Taxonomy IDs -A0A075B6I0 2 2 2 IGLV8-61 LV861_HUMAN Immunoglobulin lambda variable 8-61 OS=Homo sapiens GN=IGLV8-61 PE=3 SV=7 1 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 2 0 0 0 0 0 1 0 0 1 1 2 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 2 0 0 0 0 0 1 0 0 1 1 2 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 2 0 0 0 0 0 1 0 0 1 1 2 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 14.8 14.8 14.8 12.814 122 122 0.0026537 3.4118 By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By MS/MS By MS/MS By matching 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7.4 0 0 0 0 0 0 0 0 0 7.4 0 0 7.4 7.4 0 0 0 14.8 0 0 0 0 0 7.4 0 0 7.4 7.4 14.8 0 0 0 7.4 0 0 0 7.4 0 0 0 7.4 0 0 7.4 0 0 0 0 0 0 0 0 0 0 0 0 0 7.4 0 7.4 0 0 0 0 0 7.4 0 0 0 7.4 7.4 7.4 0 7.4 0 0 0 0 0 0 7.4 7.4 0 0 7.4 0 0 7.4 0 7.4 7.4 7.4 7.4 7.4 7.4 7.4 0 7.4 7.4 7.4 7.4 7.4 7.4 0 7.4 7.4 0 7.4 0 7.4 0 0 0 0 0 7.4 7.4 7.4 7.4 0 0 0 0 1078800000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10544000 0 0 0 0 0 0 0 0 0 51433000 0 0 1201800 4708800 0 0 0 18292000 0 0 0 0 0 10671000 0 0 68315000 23689000 35386000 0 0 0 31720000 0 0 0 3843200 0 0 0 32011000 0 0 3585300 0 0 0 0 0 0 0 0 0 0 0 0 0 5269200 0 5220500 0 0 0 0 0 4125300 0 0 0 5130500 511940 1225500 0 2597800 0 0 0 0 0 0 550180 8679700 0 0 11430000 0 0 2551000 0 3444800 11409000 6653700 102450000 6602900 4239100 11507000 0 18876000 87506000 3313300 103810000 17227000 122910000 0 10760000 150530000 0 5686500 0 10178000 0 0 0 0 0 29254000 15357000 10771000 3667500 0 0 0 0 4 269710000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2635900 0 0 0 0 0 0 0 0 0 12858000 0 0 300440 1177200 0 0 0 4573100 0 0 0 0 0 2667800 0 0 17079000 5922200 8846500 0 0 0 7930000 0 0 0 960790 0 0 0 8002700 0 0 896330 0 0 0 0 0 0 0 0 0 0 0 0 0 1317300 0 1305100 0 0 0 0 0 1031300 0 0 0 1282600 127990 306370 0 649460 0 0 0 0 0 0 137550 2169900 0 0 2857400 0 0 637760 0 861210 2852200 1663400 25612000 1650700 1059800 2876800 0 4718900 21877000 828320 25952000 4306800 30728000 0 2690100 37632000 0 1421600 0 2544500 0 0 0 0 0 7313400 3839200 2692800 916880 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17393000 0 0 0 0 0 0 0 0 0 0 36285000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 11 0 8748;27013 True;True 9824;30671 547584;547585;547586;547587;547588;547589;547590;547591;547592;547593;547594;547595;547596;547597;547598;547599;547600;547601;547602;547603;547604;547605;547606;547607;547608;547609;547610;547611;547612;547613;547614;547615;547616;547617;547618;547619;547620;547621;547622;547623;547624;547625;547626;547627;547628;547629;547630;547631;1760392;1760393 497771;497772;497773;497774;497775;497776;497777;497778;497779;497780;1621639;1621640 497774;1621640 -1 -A0A075B6I9;P04211 3;2 3;2 3;2 Ig lambda chain V region 4A IGLV7-46 LV746_HUMAN Immunoglobulin lambda variable 7-46 OS=Homo sapiens GN=IGLV7-46 PE=3 SV=4;LV743_HUMAN Immunoglobulin lambda variable 7-43 OS=Homo sapiens GN=IGLV7-43 PE=3 SV=2 2 3 3 3 0 1 1 1 0 0 1 1 0 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 1 0 1 0 1 1 2 1 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 2 0 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 1 0 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 1 0 1 0 1 1 2 1 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 2 0 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 0 0 1 1 0 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0 1 1 0 1 0 0 1 0 1 0 1 1 2 1 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 2 0 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 21.4 21.4 21.4 12.468 117 117;117 0.0022614 3.7132 By matching By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By matching By matching By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching By matching By matching By matching By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching 0 7.7 6 7.7 0 0 7.7 7.7 0 0 7.7 0 7.7 0 7.7 7.7 0 0 0 0 7.7 7.7 0 0 0 7.7 7.7 0 7.7 0 0 7.7 0 7.7 0 7.7 7.7 15.4 7.7 7.7 0 7.7 0 0 0 0 7.7 7.7 0 0 0 7.7 7.7 0 0 0 7.7 0 0 0 0 0 0 7.7 7.7 0 0 0 7.7 7.7 7.7 7.7 0 0 0 0 0 13.7 0 6 7.7 7.7 0 7.7 0 6 0 0 0 7.7 0 0 0 0 0 0 0 0 0 0 7.7 7.7 7.7 0 0 0 7.7 0 0 7.7 7.7 7.7 7.7 7.7 0 0 7.7 0 0 7.7 0 0 7.7 7.7 7.7 7.7 7.7 0 0 7.7 0 0 0 0 0 0 0 0 0 0 0 0 7.7 750780000 0 670520 14278000 19252000 0 0 11737000 647510 0 0 11884000 0 0 0 18263000 589200 0 0 0 0 1509900 13961000 0 0 0 20661000 1842000 0 11795000 0 0 7442400 0 10453000 0 8616900 222500 33750000 10718000 11050000 0 27094000 0 0 0 0 38027000 21644000 0 0 0 3084200 24772000 0 0 0 11338000 0 0 0 0 0 0 50737000 1458500 0 0 0 8521500 5386800 3945800 4124000 0 0 0 0 0 10904000 0 6535300 11468000 7564600 0 4429900 0 3560500 0 0 0 10312000 0 0 0 0 0 0 0 0 0 0 258680 17668000 4264000 0 0 0 6622900 0 0 18147000 42779000 42975000 14863000 13154000 0 0 510330 0 0 18912000 0 0 6793800 25532000 23434000 7436800 18015000 0 0 9899100 0 0 0 0 0 0 0 0 0 0 0 0 15269000 4 187700000 0 167630 3569400 4813000 0 0 2934100 161880 0 0 2970900 0 0 0 4565800 147300 0 0 0 0 377480 3490100 0 0 0 5165200 460500 0 2948800 0 0 1860600 0 2613300 0 2154200 55625 8437500 2679500 2762500 0 6773500 0 0 0 0 9506700 5411100 0 0 0 771060 6193000 0 0 0 2834500 0 0 0 0 0 0 12684000 364620 0 0 0 2130400 1346700 986440 1031000 0 0 0 0 0 2726000 0 1633800 2867100 1891200 0 1107500 0 890120 0 0 0 2577900 0 0 0 0 0 0 0 0 0 0 64669 4417000 1066000 0 0 0 1655700 0 0 4536700 10695000 10744000 3715900 3288500 0 0 127580 0 0 4727900 0 0 1698400 6383100 5858400 1859200 4503800 0 0 2474800 0 0 0 0 0 0 0 0 0 0 0 0 3817300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33750000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22 1 8749;11978;27012 True;True;True 9825;13443;30670 547632;547633;547634;547635;547636;547637;547638;547639;547640;547641;547642;547643;547644;547645;547646;547647;547648;547649;547650;547651;547652;547653;547654;547655;547656;547657;547658;547659;547660;547661;547662;547663;547664;547665;547666;547667;547668;547669;547670;547671;547672;547673;547674;547675;547676;547677;547678;547679;547680;547681;547682;547683;547684;547685;547686;547687;774173;774174;774175;774176;1760389;1760390;1760391 497781;497782;497783;497784;497785;497786;497787;497788;497789;497790;497791;497792;497793;497794;497795;497796;497797;497798;497799;497800;713524;1621638 497800;713524;1621638 -1;-1 -A0A075B6K4 3;1 3;1 2;1 IGLV3-10 LV310_HUMAN Immunoglobulin lambda variable 3-10 OS=Homo sapiens GN=IGLV3-10 PE=3 SV=2 2 3 3 2 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 0 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 0 1 2 1 1 1 1 1 1 1 1 0 1 2 1 0 1 0 0 2 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 2 0 0 1 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 2 2 0 0 0 1 1 0 1 1 1 0 2 1 1 0 2 0 1 0 1 0 1 2 0 1 2 2 1 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 0 1 1 0 1 1 0 1 1 0 1 1 1 1 0 1 1 0 1 0 1 2 1 1 1 1 1 1 1 1 0 1 2 1 0 1 0 0 2 1 0 0 0 1 1 0 1 1 0 0 1 0 0 0 0 0 0 0 0 1 2 0 0 1 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 2 2 0 0 0 1 1 0 1 1 1 0 2 1 1 0 2 0 1 0 1 0 1 2 0 1 2 2 1 2 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 0 1 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 1 0 1 1 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 27.8 27.8 19.1 12.441 115 115;115 0.0049772 2.6663 By matching By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By MS/MS By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching 0 0 12.2 7 0 0 7 0 0 0 7 0 7 7 0 0 0 0 7 8.7 0 7 7 0 8.7 7 0 8.7 8.7 8.7 8.7 0 7 7 0 7 0 7 15.7 7 7 7 7 7 7 7 7 0 7 15.7 7 0 7 0 0 15.7 8.7 0 0 0 7 7 0 8.7 8.7 0 0 7 0 0 0 0 0 0 0 0 8.7 20.9 0 0 8.7 0 0 0 15.7 0 0 8.7 0 0 0 0 0 0 0 0 0 8.7 0 7 0 15.7 15.7 0 0 0 7 7 0 7 7 8.7 0 15.7 7 7 0 15.7 0 7 0 7 0 7 15.7 0 7 15.7 15.7 8.7 15.7 7 0 0 0 0 7 0 0 0 0 0 0 1188700000 0 0 1975200 21800000 0 0 63259000 0 0 0 22366000 0 29156000 6739000 0 0 0 0 75727000 9632200 0 5206200 30362000 0 11237000 18659000 0 17086000 7158200 12440000 4820000 0 2285900 3952300 0 4657200 0 5656100 89823000 1367200 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Immunoglobulin lambda variable 3-9 OS=Homo sapiens GN=IGLV3-9 PE=3 SV=1 2 5 3 3 0 1 1 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 2 2 1 0 1 2 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 0 1 0 0 0 1 0 0 1 1 1 1 0 1 0 1 1 0 1 0 1 3 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 2 1 0 1 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 49.6 26.1 26.1 12.332 115 115;115 0.0026942 3.5738 By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching 0 13.9 13.9 0 13.9 13.9 13.9 13.9 13.9 13.9 0 0 0 13.9 13.9 13.9 13.9 13.9 13.9 23.5 23.5 13.9 0 13.9 23.5 13.9 0 0 13.9 13.9 13.9 13.9 13.9 13.9 0 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 0 13.9 13.9 13.9 13.9 0 0 0 0 13.9 0 13.9 13.9 13.9 13.9 0 13.9 13.9 13.9 13.9 13.9 0 13.9 13.9 9.6 9.6 9.6 9.6 9.6 9.6 13.9 13.9 13.9 23.5 13.9 13.9 13.9 13.9 13.9 13.9 13.9 13.9 0 13.9 0 0 0 13.9 0 0 13.9 13.9 13.9 13.9 0 9.6 0 13.9 13.9 0 13.9 0 13.9 30.4 13.9 13.9 13.9 13.9 0 13.9 13.9 13.9 13.9 13.9 13.9 13.9 0 13.9 0 30.4 13.9 0 13.9 13.9 13.9 13.9 13.9 13.9 0 0 0 0 13.9 0 0 9.6 13.9 0 13.9 193940000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12235000 11589000 25127000 25600000 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-1;-1 -A0A075B6R2 3 2 2 IGHV4-4 HV404_HUMAN Immunoglobulin heavy variable 4-4 OS=Homo sapiens GN=IGHV4-4 PE=3 SV=2 1 3 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 37.6 23.9 23.9 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3-49 OS=Homo sapiens GN=IGHV3-49 PE=3 SV=1 1 4 4 3 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 2 0 0 0 0 0 0 0 2 0 1 2 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 2 0 0 0 0 0 0 0 2 0 1 2 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 2 0 0 0 0 0 0 0 2 0 1 1 1 1 0 1 0 0 0 37 37 27.7 13.056 119 119 0.0014259 4.5935 By matching By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS 0 0 0 7.6 0 0 7.6 0 0 0 0 0 0 0 7.6 0 0 0 7.6 0 0 7.6 0 0 0 7.6 0 0 0 0 0 0 0 0 0 7.6 0 0 0 7.6 0 0 7.6 7.6 0 0 10.9 0 0 0 7.6 0 0 0 0 0 0 0 0 0 0 0 0 7.6 0 0 0 0 0 0 0 0 7.6 0 0 0 0 16.8 0 7.6 7.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7.6 0 0 0 0 0 16.8 0 0 0 0 9.2 0 0 9.2 0 0 0 0 0 9.2 0 7.6 0 9.2 0 7.6 9.2 0 16.8 0 0 0 0 0 0 0 16.8 0 7.6 16.8 7.6 7.6 0 7.6 0 0 0 335470000 0 0 0 5105300 0 0 11050000 0 0 0 0 0 0 0 9907400 0 0 0 2244700 0 0 8187000 0 0 0 6430300 0 0 0 0 0 0 0 0 0 6492900 0 0 0 3518300 0 0 6611500 2916200 0 0 0 0 0 0 2940000 0 0 0 0 0 0 0 0 0 0 0 0 9040800 0 0 0 0 0 0 0 0 0 0 0 0 0 43650000 0 13625000 3170300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6168500 0 0 0 0 0 20645000 0 0 0 0 6049700 0 0 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matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching 5.9 0 15.1 16.8 0 0 16.8 10.9 10.9 10.9 5.9 10.9 5.9 5.9 16.8 0 10.9 10.9 5.9 16.8 0 26.1 5.9 10.9 5.9 5.9 0 0 0 16.8 0 0 0 0 16.8 0 0 20.2 15.1 0 5.9 10.9 20.2 0 0 0 20.2 20.2 26.1 5.9 0 5.9 0 0 0 10.9 20.2 0 5.9 5.9 0 0 0 0 9.2 0 5.9 0 0 0 0 5.9 0 0 0 5.9 5.9 5.9 0 5.9 15.1 9.2 5.9 0 5.9 15.1 5.9 0 5.9 5.9 0 5.9 5.9 5.9 0 5.9 15.1 0 0 0 0 15.1 15.1 5.9 5.9 0 5.9 5.9 5.9 26.1 16.8 5.9 5.9 9.2 5.9 5.9 0 15.1 5.9 9.2 5.9 26.1 5.9 16.8 15.1 5.9 20.2 26.1 16.8 16.8 16.8 5.9 0 0 16.8 20.2 20.2 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1449108;1859827 -1;-1 -A0A0C4DH34 1 1 1 IGHV4-28 HV428_HUMAN Immunoglobulin heavy variable 4-28 OS=Homo sapiens GN=IGHV4-28 PE=3 SV=1 1 1 1 1 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 7.7 7.7 7.7 13.124 117 117 1 -2 By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By matching By MS/MS By MS/MS By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By matching By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching 0 0 7.7 7.7 0 0 7.7 0 0 0 7.7 0 0 7.7 0 0 0 0 7.7 7.7 0 7.7 0 0 7.7 7.7 0 0 7.7 7.7 7.7 7.7 0 7.7 7.7 0 0 0 0 0 0 0 0 0 0 0 7.7 0 7.7 7.7 7.7 7.7 0 0 7.7 7.7 7.7 0 0 0 7.7 0 7.7 7.7 0 0 7.7 0 0 0 0 0 0 0 7.7 7.7 7.7 7.7 0 7.7 7.7 0 0 0 7.7 7.7 0 0 7.7 0 7.7 7.7 0 7.7 0 7.7 7.7 7.7 0 0 0 7.7 7.7 7.7 0 0 0 7.7 0 0 7.7 7.7 0 0 7.7 0 0 7.7 7.7 7.7 0 0 0 0 7.7 0 7.7 7.7 7.7 7.7 0 0 7.7 7.7 7.7 7.7 7.7 0 0 0 0 0 7.7 743170000 0 0 6927700 29891000 0 0 26111000 0 0 0 13041000 0 0 6975000 0 0 0 0 21107000 25326000 0 1310700 0 0 34345000 5925100 0 0 1637700 8238900 10548000 4549300 0 4152600 4775300 0 0 0 0 0 0 0 0 0 0 0 20733000 0 10619000 6378400 7370400 8451500 0 0 478710 20674000 18299000 0 0 0 1846000 0 3385600 2260600 0 0 9826900 0 0 0 0 0 0 0 7803100 12743000 18907000 16786000 0 6200300 1635100 0 0 0 9099000 1309600 0 0 594330 0 566630 4688600 0 2246700 0 11455000 11437000 2762000 0 0 0 3430600 2179600 4495900 0 0 0 17321000 0 0 56142000 12417000 0 0 16704000 0 0 11838000 20227000 29801000 0 0 0 0 15521000 0 10347000 37586000 2329900 8064800 0 0 10615000 8889800 19064000 7087300 12218000 0 0 0 0 0 13477000 6 123860000 0 0 1154600 4981800 0 0 4351800 0 0 0 2173400 0 0 1162500 0 0 0 0 3517900 4220900 0 218440 0 0 5724200 987510 0 0 272950 1373100 1758100 758210 0 692100 795890 0 0 0 0 0 0 0 0 0 0 0 3455500 0 1769800 1063100 1228400 1408600 0 0 79786 3445700 3049900 0 0 0 307670 0 564270 376770 0 0 1637800 0 0 0 0 0 0 0 1300500 2123900 3151100 2797700 0 1033400 272510 0 0 0 1516500 218260 0 0 99055 0 94438 781440 0 374440 0 1909200 1906200 460340 0 0 0 571770 363260 749320 0 0 0 2886900 0 0 9357000 2069500 0 0 2784000 0 0 1973000 3371100 4966800 0 0 0 0 2586800 0 1724400 6264300 388320 1344100 0 0 1769200 1481600 3177300 1181200 2036300 0 0 0 0 0 2246200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 12218000 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 0 2 1 0 0 1 0 0 0 0 0 0 15 + 18 30490 True 34638;34639 2003723;2003724;2003725;2003726;2003727;2003728;2003729;2003730;2003731;2003732;2003733;2003734;2003735;2003736;2003737;2003738;2003739;2003740;2003741;2003742;2003743;2003744;2003745;2003746;2003747;2003748;2003749;2003750;2003751;2003752;2003753;2003754;2003755;2003756;2003757;2003758;2003759;2003760;2003761;2003762;2003763;2003764;2003765;2003766;2003767;2003768;2003769;2003770;2003771;2003772;2003773;2003774;2003775;2003776;2003777;2003778;2003779;2003780;2003781;2003782;2003783;2003784;2003785;2003786;2003787;2003788;2003789;2003790;2003791;2003792;2003793;2003794;2003795;2003796;2003797;2003798;2003799;2003800;2003801;2003802;2003803 1859807;1859808;1859809;1859810;1859811;1859812;1859813;1859814;1859815;1859816;1859817;1859818;1859819;1859820;1859821;1859822;1859823;1859824;1859825;1859826 1859818 3 89 -1 -A0A0C4DH38 4 4 2 IGHV5-51 HV551_HUMAN Immunoglobulin heavy variable 5-51 OS=Homo sapiens GN=IGHV5-51 PE=3 SV=1 1 4 4 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 1 2 1 0 0 0 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 1 0 0 0 0 3 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 2 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 1 2 1 0 0 0 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 1 0 0 0 0 3 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 2 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 47 47 26.5 12.674 117 117 0.0010504 6.4794 By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS 0 0 0 0 0 0 13.7 0 0 0 0 0 0 0 0 0 0 0 0 33.3 0 12.8 0 12.8 22.2 12.8 0 0 0 33.3 12.8 12.8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33.3 0 11.1 0 13.7 0 0 0 0 33.3 12.8 0 0 0 0 0 0 12.8 12.8 0 12.8 0 0 0 0 0 0 0 0 0 12.8 22.2 11.1 12.8 12.8 0 0 0 0 12.8 0 0 0 0 0 0 0 0 12.8 12.8 0 0 0 0 0 0 13.7 0 0 0 0 0 0 0 9.4 0 0 0 0 0 0 0 0 0 0 0 0 0 26.5 0 0 12.8 12.8 0 0 0 0 13.7 0 0 0 11.1 0 11.1 0 0 0 358030000 0 0 0 0 0 0 4931200 0 0 0 0 0 0 0 0 0 0 0 0 33568000 0 8416000 0 573060 14921000 0 0 0 0 28681000 6081100 5336100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22264000 0 9129400 0 3639300 0 0 0 0 57423000 0 0 0 0 0 0 0 8034300 2896600 0 14957000 0 0 0 0 0 0 0 0 0 3535500 16624000 4759300 8027600 0 0 0 0 0 5744800 0 0 0 0 0 0 0 0 6597800 3638200 0 0 0 0 0 0 8328600 0 0 0 0 0 0 0 5112100 0 0 0 0 0 0 0 0 0 0 0 0 0 16092000 0 0 6817800 29558000 0 0 0 0 0 0 0 0 8377800 0 13968000 0 0 0 5 71607000 0 0 0 0 0 0 986240 0 0 0 0 0 0 0 0 0 0 0 0 6713600 0 1683200 0 114610 2984300 0 0 0 0 5736200 1216200 1067200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4452700 0 1825900 0 727850 0 0 0 0 11485000 0 0 0 0 0 0 0 1606900 579320 0 2991500 0 0 0 0 0 0 0 0 0 707100 3324800 951860 1605500 0 0 0 0 0 1149000 0 0 0 0 0 0 0 0 1319600 727640 0 0 0 0 0 0 1665700 0 0 0 0 0 0 0 1022400 0 0 0 0 0 0 0 0 0 0 0 0 0 3218400 0 0 1363600 5911600 0 0 0 0 0 0 0 0 1675600 0 2793600 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31907000 0 0 0 0 24753000 0 0 0 0 24896000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 38779000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30882000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 4 0 0 1 1 0 0 0 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 3 1 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 4 0 0 0 0 1 0 0 0 0 0 1 0 0 0 35 19 2338;10132;24657;32123 True;True;True;True 2643;2644;11350;28000;36454 158864;158865;158866;158867;158868;158869;158870;652565;652566;652567;652568;652569;1611516;1611517;1611518;1611519;1611520;1611521;1611522;1611523;2122617;2122618;2122619;2122620;2122621;2122622;2122623;2122624;2122625;2122626;2122627;2122628;2122629;2122630;2122631;2122632;2122633;2122634;2122635;2122636;2122637;2122638;2122639;2122640;2122641;2122642;2122643 144904;144905;144906;144907;599840;599841;599842;1482719;1482720;1482721;1482722;1973008;1973009;1973010;1973011;1973012;1973013;1973014;1973015;1973016;1973017;1973018;1973019;1973020;1973021;1973022;1973023;1973024;1973025;1973026;1973027;1973028;1973029;1973030;1973031;1973032;1973033;1973034;1973035 144904;599841;1482720;1973033 4 112 -1 -A0A0C4DH68 3 2 2 IGKV2-24 KV224_HUMAN Immunoglobulin kappa variable 2-24 OS=Homo sapiens GN=IGKV2-24 PE=3 SV=1 1 3 2 2 1 2 2 1 1 1 2 1 0 0 1 1 1 1 1 0 0 0 1 2 1 1 0 0 2 2 2 1 1 2 2 0 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 3 2 2 0 2 1 0 2 2 2 1 1 0 0 0 2 1 0 1 1 0 1 0 0 0 0 1 1 1 1 0 2 2 2 2 1 2 1 0 2 2 0 1 0 0 0 2 1 2 0 0 1 0 2 1 1 2 1 1 1 1 2 2 1 0 2 2 2 0 1 2 2 1 2 1 2 2 2 2 2 1 2 2 2 1 1 1 1 1 2 2 1 2 1 1 0 1 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 2 1 1 0 1 1 0 2 1 2 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 1 0 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 2 1 1 0 1 1 0 2 1 2 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 1 0 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 38.3 32.5 32.5 13.079 120 120 0 18.278 By matching By matching By MS/MS By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By matching By MS/MS By matching By matching By matching By MS/MS By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching 5.8 16.7 16.7 10.8 5.8 5.8 16.7 5.8 0 0 10.8 5.8 10.8 5.8 5.8 0 0 0 10.8 16.7 10.8 10.8 0 0 16.7 16.7 16.7 5.8 5.8 16.7 16.7 0 16.7 16.7 16.7 16.7 16.7 16.7 16.7 5.8 10.8 16.7 16.7 16.7 16.7 5.8 16.7 16.7 38.3 16.7 16.7 0 16.7 10.8 0 32.5 16.7 32.5 5.8 10.8 0 0 0 16.7 10.8 0 5.8 10.8 0 5.8 0 0 0 0 5.8 5.8 5.8 10.8 0 16.7 16.7 16.7 16.7 5.8 16.7 5.8 0 16.7 16.7 0 10.8 0 0 0 16.7 10.8 16.7 0 0 10.8 0 16.7 5.8 5.8 16.7 5.8 5.8 5.8 10.8 16.7 16.7 5.8 0 16.7 16.7 16.7 0 5.8 16.7 16.7 10.8 16.7 5.8 16.7 16.7 16.7 16.7 16.7 10.8 16.7 16.7 16.7 10.8 5.8 10.8 10.8 10.8 16.7 16.7 10.8 16.7 5.8 10.8 1465800000 0 2494700 14423000 12149000 0 0 21507000 0 0 0 18778000 0 6016100 0 0 0 0 0 7679600 15359000 3816400 13533000 0 0 16178000 19351000 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37668;1316971;1316972;1316973;1316974;1316975;1316976;1316977;1316978;1316979;1316980 37668;1316971 -1 -A0A0J9YXX1 3 1 1 HV5X1_HUMAN Immunoglobulin heavy variable 5-10-1 OS=Homo sapiens GN=IGHV5-10-1 PE=3 SV=1 1 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 0 2 0 0 0 0 3 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 2 0 1 0 0 0 0 0 0 3 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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modifier-activating enzyme 6 OS=Homo 4 4 4 4 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 2 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 2 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 2 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 4.8 4.8 4.8 117.97 1052 1052;340;389;578 0 8.6245 By matching By matching By matching By MS/MS By 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MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By matching By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS 2.4 0 0 0 0 0 0 0 0 0 2 0 2.2 0 0 0 0 0 2.2 0 0 0 0 0 0 0 0 0 0 0 2 2.4 0 0 3.3 0 0 3.3 3.3 3.3 9.4 0 2 0 0 2 3.3 0 0 0 0 2.2 3.3 5.3 0 0 0 3.3 5.3 7.5 0 2 0 0 1.8 0 3.9 6.6 1.8 3.9 3.9 1.8 2.2 2.2 0 3.3 2 0 0 0 2.2 2 5.3 5.3 7.2 0 2.2 4.6 5.7 4.6 0 6.4 0 0 0 6.6 4.6 6.8 0 11 0 0 0 0 2.2 0 5.5 3.3 6.8 3.3 3.3 0 0 0 0 0 0 0 0 0 5.5 0 0 0 0 2.2 2.2 0 0 2.2 0 0 3.3 0 5.5 3.3 5.5 0 0 0 0 4.2 0 620900000 790690 0 0 0 0 0 0 0 0 0 3242300 0 7179200 0 0 0 0 0 7623200 0 0 0 0 0 0 0 0 0 0 0 3970500 9286000 0 0 20384000 0 0 5156000 6547900 7782900 23743000 0 4993000 0 0 1727500 5699400 0 0 0 0 45495000 13396000 6398700 0 0 0 7059400 10902000 45574000 0 1280500 0 0 2477900 0 13836000 10777000 20341000 3915400 6049600 1798500 1516300 2197200 0 4152500 5796900 0 0 0 2311500 2009700 21153000 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183202;183203;183204;183205;183206;183207;183208;183209;183210;183211;183212;183213;183214;183215;183216;183217;183218;183219;183220;183221;183222;183223;183224;183225;183226;310825;310826;310827;310828;310829;310830;310831;310832;310833;310834;310835;310836;310837;773924;773925;773926;773927;773928;773929;773930;773931;773932;857333;857334;857335;857336;857337;857338;857339;857340;857341;857342;857343;857344;857345;1243627;1243628;1243629;1243630;1243631;1243632;1243633;1243634;1243635;1243636;1243637;1243638;1243639;1243640;1243641;1243642;1252839;1252840;1252841;1252842;1252843;1252844;1252845;1252846;1252847;1252848;1252849;1488170;1816897;1816898;1816899;1816900;1816901;1816902;1816903;1816904;1816905;1816906;1816907;1816908 165985;165986;165987;165988;165989;165990;165991;165992;165993;165994;282261;713362;713363;713364;792134;792135;792136;792137;792138;792139;1149676;1149677;1149678;1149679;1149680;1157733;1157734;1157735;1157736;1157737;1375695;1678743;1678744;1678745;1678746;1678747;1678748;1678749 165990;282261;713362;792139;1149677;1157733;1375695;1678746 -1;-1;-1;-1;-1;-1;-1 -A2RTY3-4;A2RTY3;A2RTY3-2;A2RTY3-5;A2RTY3-3 2;2;1;1;1 2;2;1;1;1 2;2;1;1;1 Protein HEATR9 HEATR9 HEAT9_HUMAN Isoform 4 of Protein HEATR9 OS=Homo sapiens GN=HEATR9;HEAT9_HUMAN Protein HEATR9 OS=Homo sapiens GN=HEATR9 PE=1 SV=2;HEAT9_HUMAN Isoform 2 of Protein HEATR9 OS=Homo sapiens GN=HEATR9;HEAT9_HUMAN Isoform 5 of Protein HEATR9 OS=Homo sapiens GN=HE 5 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 2 0 0 2 1 1 2 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 3.9 0 3.9 0 3.9 3.9 0 0 0 3.9 0 0 3.9 0 0 0 0 0 0 0 0 0 3.9 3.9 0 3.9 3.9 0 0 0 0 3.9 0 0 0 0 6.6 0 0 6.6 2.6 3.9 6.6 6.6 0 3.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.9 0 0 0 0 0 0 3.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.6 0 402010000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11134000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 629890 0 14080000 0 7356200 5487300 0 0 0 35934000 0 0 38002000 0 0 0 0 0 0 0 0 0 4998300 36146000 0 40036000 13204000 0 0 0 0 39922000 0 0 0 0 2241000 0 0 21549000 0 14960000 12679000 24674000 0 47435000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 16407000 0 0 0 0 0 0 15132000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 23 17479000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 484090 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 27387 0 612180 0 319840 238580 0 0 0 1562300 0 0 1652300 0 0 0 0 0 0 0 0 0 217320 1571600 0 1740700 574070 0 0 0 0 1735700 0 0 0 0 97435 0 0 936930 0 650430 551270 1072800 0 2062400 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 713350 0 0 0 0 0 0 657920 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15132000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 + 25 1276;18699 True;True 1438;20941 86205;86206;86207;86208;86209;86210;86211;86212;86213;86214;86215;86216;86217;86218;86219;86220;86221;86222;86223;86224;1238033;1238034;1238035;1238036;1238037;1238038;1238039;1238040;1238041;1238042 79296;1144429;1144430;1144431;1144432;1144433;1144434;1144435 79296;1144432 5 0;1 99 97;101 -1;-1;-1;-1;-1 -A2VEC9-2 2 2 2 SCO-spondin SSPO SSPO_HUMAN Isoform 2 of SCO-spondin OS=Homo sapiens GN=SSPO 1 2 2 2 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 1 1 2 1 1 1 0 0 0 1 0 1 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 1 1 2 1 1 1 0 0 0 1 0 1 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 0 0 1 0 1 0 0 0 0 1 1 1 0 1 1 2 1 1 1 0 0 0 1 0 1 1 0 0 0 1 1 1 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.7 2.7 2.7 138.77 1314 1314 1 -2 By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By matching By matching By MS/MS By MS/MS By MS/MS 0 0 0 1.3 0 0 0 0 0 0 0 0 1.3 0 0 0 0 0 1.3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1.3 0 0 0 1.3 1.3 0 1.3 1.3 0 0 0 1.3 0 1.3 0 0 0 0 1.3 1.3 1.3 0 1.4 1.4 2.7 1.4 1.4 1.4 0 0 0 1.3 0 1.3 1.3 0 0 0 1.3 1.3 1.3 0 0 1.3 0 1.3 1.3 0 0 0 0 0 0 0 0 0 0 1.3 0 0 0 0 1.3 0 0 0 1.3 0 1.3 0 0 0 1.3 0 1.3 0 0 0 0 1.3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 784350000 0 0 0 9949300 0 0 0 0 0 0 0 0 34074000 0 0 0 0 0 15793000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 38775000 0 0 0 14489000 15174000 0 15149000 3551600 0 0 0 7822700 0 110450000 0 0 0 0 9363300 2453100 24381000 0 33689000 43579000 42944000 23233000 88863000 44421000 0 0 0 5614800 0 30985000 21752000 0 0 0 6323800 5385100 9889700 0 0 33144000 0 3876200 13180000 0 0 0 0 0 0 0 0 0 0 8000200 0 0 0 0 10165000 0 0 0 5817900 0 1989000 0 0 0 17554000 0 16275000 0 0 0 0 16239000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33 23768000 0 0 0 301490 0 0 0 0 0 0 0 0 1032600 0 0 0 0 0 478570 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1175000 0 0 0 439070 459800 0 459070 107630 0 0 0 237050 0 3347000 0 0 0 0 283740 74335 738820 0 1020900 1320600 1301300 704030 2692800 1346100 0 0 0 170150 0 938950 659160 0 0 0 191630 163190 299690 0 0 1004400 0 117460 399390 0 0 0 0 0 0 0 0 0 0 242430 0 0 0 0 308030 0 0 0 176300 0 60272 0 0 0 531930 0 493180 0 0 0 0 492080 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 42944000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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sapiens GN=GSAP;GSAP_HUMAN Gamma-secretase-activating protein OS=Homo sapiens GN=GSAP PE=1 SV=2 3 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 2.9 2.9 2.9 63.917 559 559;577;854 1 -2 By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.9 0 0 0 0 0 0 0 0 2.9 0 2.9 2.9 2.9 2.9 2.9 0 0 0 0 2.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2.9 0 2.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 24 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 5.1 5.1 5.1 24.4 217 217 1 -2 By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5.1 0 5.1 5.1 0 0 0 0 0 0 0 0 5.1 0 0 0 5.1 5.1 5.1 5.1 5.1 0 5.1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5.1 0 76949000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9125800 0 5927800 8546500 0 0 0 0 0 0 0 0 7014300 0 0 0 6480200 4045700 4467200 10772000 5066400 0 4329700 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11174000 0 15 5129900 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 608390 0 395180 569770 0 0 0 0 0 0 0 0 467620 0 0 0 432020 269710 297810 718110 337760 0 288650 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 744900 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11174000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 4 + 28 19972 True 22682 1334867;1334868;1334869;1334870;1334871;1334872;1334873;1334874;1334875;1334876;1334877 1234001;1234002;1234003;1234004 1234002 7 68 -1 +Peptide counts (all) Peptide counts (razor+unique) Peptide counts (unique) Protein names Gene names Fasta headers Number of proteins Peptides Razor + unique peptides Unique peptides Peptides AD01_C1_INSOLUBLE_01 Peptides AD01_C1_INSOLUBLE_02 Peptides AD01_C1_INSOLUBLE_03 Peptides AD01_C2_INSOLUBLE_01 Peptides AD02_C1_INSOLUBLE_01 Peptides AD02_C1_INSOLUBLE_02 Peptides AD02_C2_INSOLUBLE_01 Peptides AD03_C1_INSOLUBLE_01 Peptides AD03_C1_INSOLUBLE_02 Peptides AD03_C1_INSOLUBLE_03 Peptides AD03_C2_INSOLUBLE_01 Peptides AD04_C1_INSOLUBLE_01 Peptides AD04_C2_INSOLUBLE_01 Peptides AD05_C2_INSOLUBLE_01 Peptides AD06_C1_INSOLUBLE_01 Peptides AD07_C1_INSOLUBLE_01 Peptides AD07_C1_INSOLUBLE_02 Peptides AD07_C1_INSOLUBLE_03 Peptides AD07_C2_INSOLUBLE_01 Peptides AD08_C2_INSOLUBLE_01 Peptides AD09_C1_INSOLUBLE_01 Peptides AD10_C1_INSOLUBLE_01 Peptides AD10_C2_INSOLUBLE_01 Peptides AD11_C2_INSOLUBLE_01 Peptides AD12_C2_INSOLUBLE_01 Peptides AD13_C2_INSOLUBLE_01 Peptides AD14_C2_INSOLUBLE_01 Peptides AD15_C2_INSOLUBLE_01 Peptides AD16_C2_INSOLUBLE_01 Peptides AD17_C2_INSOLUBLE_01 Peptides AD18_C2_INSOLUBLE_01 Peptides AD19_C2_INSOLUBLE_01 Peptides AD20_C1_INSOLUBLE_01 Peptides AD21_C1_INSOLUBLE_01 Peptides AD21_C2_INSOLUBLE_01 Peptides AD22_C1_INSOLUBLE_01 Peptides AD23_C1_INSOLUBLE_01 Peptides AD23_C1_INSOLUBLE_02 Peptides AD23_C2_INSOLUBLE_01 Peptides AD24_C1_INSOLUBLE_01 Peptides AD24_C1_INSOLUBLE_02 Peptides AD25_C1_INSOLUBLE_01 Peptides AD26_C1_INSOLUBLE_01 Peptides AD27_C1_INSOLUBLE_01 Peptides AD27_C1_INSOLUBLE_02 Peptides AD28_C2_INSOLUBLE_01 Peptides AD29_C1_INSOLUBLE_01 Peptides AD30_C1_INSOLUBLE_01 Peptides AD30_C1_INSOLUBLE_02 Peptides AD30_C2_INSOLUBLE_01 Peptides AD31_C2_INSOLUBLE_01 Peptides AD32_C2_INSOLUBLE_01 Peptides AD33_C2_INSOLUBLE_01 Peptides AD34_C1_INSOLUBLE_01 Peptides AD34_C1_INSOLUBLE_02 Peptides AD35_C1_INSOLUBLE_01 Peptides AD35_C1_INSOLUBLE_02 Peptides AD36_C1_INSOLUBLE_01 Peptides AD37_C1_INSOLUBLE_01 Peptides AD37_C2_INSOLUBLE_01 Peptides AD38_C1_INSOLUBLE_01 Peptides AD38_C1_INSOLUBLE_02 Peptides AD38_C1_INSOLUBLE_03 Peptides AD39_C2_INSOLUBLE_01 Peptides AD40_C2_INSOLUBLE_01 Peptides AD41_C2_INSOLUBLE_01 Peptides AD42_C2_INSOLUBLE_01 Peptides AD43_C1_INSOLUBLE_01 Peptides AD44_C1_INSOLUBLE_01 Peptides AD44_C1_INSOLUBLE_02 Peptides AD44_C1_INSOLUBLE_03 Peptides AD44_C1_INSOLUBLE_04 Peptides AD45_C1_INSOLUBLE_01 Peptides AD45_C1_INSOLUBLE_02 Peptides AD46_C1_INSOLUBLE_01 Peptides AD46_C1_INSOLUBLE_02 Peptides AD46_C1_INSOLUBLE_03 Peptides AD46_C2_INSOLUBLE_01 Peptides AD47_C1_INSOLUBLE_01 Peptides AD48_C2_INSOLUBLE_01 Peptides AD49_C2_INSOLUBLE_01 Peptides CTR01_C1_INSOLUBLE_01 Peptides CTR02_C1_INSOLUBLE_01 Peptides CTR03_C1_INSOLUBLE_01 Peptides CTR04_C1_INSOLUBLE_01 Peptides CTR05_C2_INSOLUBLE_01 Peptides CTR06_C2_INSOLUBLE_01 Peptides CTR07_C1_INSOLUBLE_01 Peptides CTR08_C1_INSOLUBLE_01 Peptides CTR08_C2_INSOLUBLE_01 Peptides CTR09_C2_INSOLUBLE_01 Peptides CTR10_C1_INSOLUBLE_01 Peptides CTR10_C2_INSOLUBLE_01 Peptides CTR11_C2_INSOLUBLE_01 Peptides CTR12_C2_INSOLUBLE_01 Peptides CTR13_C2_INSOLUBLE_01 Peptides CTR14_C2_INSOLUBLE_01 Peptides CTR15_C2_INSOLUBLE_01 Peptides CTR16_C2_INSOLUBLE_01 Peptides CTR17_C2_INSOLUBLE_01 Peptides CTR18_C2_INSOLUBLE_01 Peptides CTR19_C1_INSOLUBLE_01 Peptides CTR20_C1_INSOLUBLE_01 Peptides CTR21_C2_INSOLUBLE_01 Peptides CTR22_C2_INSOLUBLE_01 Peptides CTR23_C2_INSOLUBLE_01 Peptides CTR24_C1_INSOLUBLE_01 Peptides CTR25_C1_INSOLUBLE_01 Peptides CTR26_C2_INSOLUBLE_01 Peptides CTR27_C1_INSOLUBLE_01 Peptides CTR28_C1_INSOLUBLE_01 Peptides CTR28_C1_INSOLUBLE_02 Peptides CTR28_C2_INSOLUBLE_01 Peptides CTR29_C1_INSOLUBLE_01 Peptides CTR29_C1_INSOLUBLE_02 Peptides CTR29_C1_INSOLUBLE_03 Peptides CTR30_C1_INSOLUBLE_01 Peptides CTR30_C1_INSOLUBLE_02 Peptides CTR30_C2_INSOLUBLE_01 Peptides CTR31_C1_INSOLUBLE_01 Peptides CTR31_C2_INSOLUBLE_01 Peptides CTR32_C1_INSOLUBLE_01 Peptides CTR32_C2_INSOLUBLE_01 Peptides CTR33_C1_INSOLUBLE_01 Peptides CTR34_C1_INSOLUBLE_01 Peptides CTR34_C2_INSOLUBLE_01 Peptides CTR35_C1_INSOLUBLE_01 Peptides CTR36_C1_INSOLUBLE_01 Peptides CTR36_C1_INSOLUBLE_02 Peptides CTR37_C1_INSOLUBLE_01 Peptides CTR38_C1_INSOLUBLE_01 Peptides CTR39_C1_INSOLUBLE_01 Peptides CTR40_C1_INSOLUBLE_01 Peptides CTR40_C1_INSOLUBLE_02 Peptides CTR40_C1_INSOLUBLE_03 Peptides CTR41_C1_INSOLUBLE_01 Peptides CTR41_C1_INSOLUBLE_02 Peptides CTR41_C1_INSOLUBLE_03 Peptides CTR42_C1_INSOLUBLE_01 Peptides CTR42_C1_INSOLUBLE_02 Peptides CTR42_C1_INSOLUBLE_03 Peptides CTR43_C2_INSOLUBLE_01 Peptides CTR44_C1_INSOLUBLE_01 Razor + unique peptides AD01_C1_INSOLUBLE_01 Razor + unique peptides AD01_C1_INSOLUBLE_02 Razor + unique peptides AD01_C1_INSOLUBLE_03 Razor + unique peptides AD01_C2_INSOLUBLE_01 Razor + unique peptides AD02_C1_INSOLUBLE_01 Razor + unique peptides AD02_C1_INSOLUBLE_02 Razor + unique peptides AD02_C2_INSOLUBLE_01 Razor + unique peptides AD03_C1_INSOLUBLE_01 Razor + unique peptides AD03_C1_INSOLUBLE_02 Razor + unique peptides AD03_C1_INSOLUBLE_03 Razor + unique peptides AD03_C2_INSOLUBLE_01 Razor + unique peptides AD04_C1_INSOLUBLE_01 Razor + unique peptides AD04_C2_INSOLUBLE_01 Razor + unique peptides AD05_C2_INSOLUBLE_01 Razor + unique peptides AD06_C1_INSOLUBLE_01 Razor + unique peptides AD07_C1_INSOLUBLE_01 Razor + unique peptides AD07_C1_INSOLUBLE_02 Razor + unique peptides AD07_C1_INSOLUBLE_03 Razor + unique peptides AD07_C2_INSOLUBLE_01 Razor + unique peptides AD08_C2_INSOLUBLE_01 Razor + unique peptides AD09_C1_INSOLUBLE_01 Razor + unique peptides AD10_C1_INSOLUBLE_01 Razor + unique peptides AD10_C2_INSOLUBLE_01 Razor + unique peptides AD11_C2_INSOLUBLE_01 Razor + unique peptides AD12_C2_INSOLUBLE_01 Razor + unique peptides AD13_C2_INSOLUBLE_01 Razor + unique peptides AD14_C2_INSOLUBLE_01 Razor + unique peptides AD15_C2_INSOLUBLE_01 Razor + unique peptides AD16_C2_INSOLUBLE_01 Razor + unique peptides AD17_C2_INSOLUBLE_01 Razor + unique peptides AD18_C2_INSOLUBLE_01 Razor + unique peptides AD19_C2_INSOLUBLE_01 Razor + unique peptides AD20_C1_INSOLUBLE_01 Razor + unique peptides AD21_C1_INSOLUBLE_01 Razor + unique peptides AD21_C2_INSOLUBLE_01 Razor + unique peptides AD22_C1_INSOLUBLE_01 Razor + unique peptides AD23_C1_INSOLUBLE_01 Razor + unique peptides AD23_C1_INSOLUBLE_02 Razor + unique peptides AD23_C2_INSOLUBLE_01 Razor + unique peptides AD24_C1_INSOLUBLE_01 Razor + unique peptides AD24_C1_INSOLUBLE_02 Razor + unique peptides AD25_C1_INSOLUBLE_01 Razor + unique peptides AD26_C1_INSOLUBLE_01 Razor + unique peptides AD27_C1_INSOLUBLE_01 Razor + unique peptides AD27_C1_INSOLUBLE_02 Razor + unique peptides AD28_C2_INSOLUBLE_01 Razor + unique peptides AD29_C1_INSOLUBLE_01 Razor + unique peptides AD30_C1_INSOLUBLE_01 Razor + unique peptides AD30_C1_INSOLUBLE_02 Razor + unique peptides AD30_C2_INSOLUBLE_01 Razor + unique peptides AD31_C2_INSOLUBLE_01 Razor + unique peptides AD32_C2_INSOLUBLE_01 Razor + unique peptides AD33_C2_INSOLUBLE_01 Razor + unique peptides AD34_C1_INSOLUBLE_01 Razor + unique peptides AD34_C1_INSOLUBLE_02 Razor + unique peptides AD35_C1_INSOLUBLE_01 Razor + unique peptides AD35_C1_INSOLUBLE_02 Razor + unique peptides AD36_C1_INSOLUBLE_01 Razor + unique peptides AD37_C1_INSOLUBLE_01 Razor + unique peptides AD37_C2_INSOLUBLE_01 Razor + unique peptides AD38_C1_INSOLUBLE_01 Razor + unique peptides AD38_C1_INSOLUBLE_02 Razor + unique peptides AD38_C1_INSOLUBLE_03 Razor + unique peptides AD39_C2_INSOLUBLE_01 Razor + unique peptides AD40_C2_INSOLUBLE_01 Razor + unique peptides AD41_C2_INSOLUBLE_01 Razor + unique peptides AD42_C2_INSOLUBLE_01 Razor + unique peptides AD43_C1_INSOLUBLE_01 Razor + unique peptides AD44_C1_INSOLUBLE_01 Razor + unique peptides AD44_C1_INSOLUBLE_02 Razor + unique peptides AD44_C1_INSOLUBLE_03 Razor + unique peptides AD44_C1_INSOLUBLE_04 Razor + unique peptides AD45_C1_INSOLUBLE_01 Razor + unique peptides AD45_C1_INSOLUBLE_02 Razor + unique peptides AD46_C1_INSOLUBLE_01 Razor + unique peptides AD46_C1_INSOLUBLE_02 Razor + unique peptides AD46_C1_INSOLUBLE_03 Razor + unique peptides AD46_C2_INSOLUBLE_01 Razor + unique peptides AD47_C1_INSOLUBLE_01 Razor + unique peptides AD48_C2_INSOLUBLE_01 Razor + unique peptides AD49_C2_INSOLUBLE_01 Razor + unique peptides CTR01_C1_INSOLUBLE_01 Razor + unique peptides CTR02_C1_INSOLUBLE_01 Razor + unique peptides CTR03_C1_INSOLUBLE_01 Razor + unique peptides CTR04_C1_INSOLUBLE_01 Razor + unique peptides CTR05_C2_INSOLUBLE_01 Razor + unique peptides CTR06_C2_INSOLUBLE_01 Razor + unique peptides CTR07_C1_INSOLUBLE_01 Razor + unique peptides CTR08_C1_INSOLUBLE_01 Razor + unique peptides CTR08_C2_INSOLUBLE_01 Razor + unique peptides CTR09_C2_INSOLUBLE_01 Razor + unique peptides CTR10_C1_INSOLUBLE_01 Razor + unique peptides CTR10_C2_INSOLUBLE_01 Razor + unique peptides CTR11_C2_INSOLUBLE_01 Razor + unique peptides CTR12_C2_INSOLUBLE_01 Razor + unique peptides CTR13_C2_INSOLUBLE_01 Razor + unique peptides CTR14_C2_INSOLUBLE_01 Razor + unique peptides CTR15_C2_INSOLUBLE_01 Razor + unique peptides CTR16_C2_INSOLUBLE_01 Razor + unique peptides CTR17_C2_INSOLUBLE_01 Razor + unique peptides CTR18_C2_INSOLUBLE_01 Razor + unique peptides CTR19_C1_INSOLUBLE_01 Razor + unique peptides CTR20_C1_INSOLUBLE_01 Razor + unique peptides CTR21_C2_INSOLUBLE_01 Razor + unique peptides CTR22_C2_INSOLUBLE_01 Razor + unique peptides CTR23_C2_INSOLUBLE_01 Razor + unique peptides CTR24_C1_INSOLUBLE_01 Razor + unique peptides CTR25_C1_INSOLUBLE_01 Razor + unique peptides CTR26_C2_INSOLUBLE_01 Razor + unique peptides CTR27_C1_INSOLUBLE_01 Razor + unique peptides CTR28_C1_INSOLUBLE_01 Razor + unique peptides CTR28_C1_INSOLUBLE_02 Razor + unique peptides CTR28_C2_INSOLUBLE_01 Razor + unique peptides CTR29_C1_INSOLUBLE_01 Razor + unique peptides CTR29_C1_INSOLUBLE_02 Razor + unique peptides CTR29_C1_INSOLUBLE_03 Razor + unique peptides CTR30_C1_INSOLUBLE_01 Razor + unique peptides CTR30_C1_INSOLUBLE_02 Razor + unique peptides CTR30_C2_INSOLUBLE_01 Razor + unique peptides CTR31_C1_INSOLUBLE_01 Razor + unique peptides CTR31_C2_INSOLUBLE_01 Razor + unique peptides CTR32_C1_INSOLUBLE_01 Razor + unique peptides CTR32_C2_INSOLUBLE_01 Razor + unique peptides CTR33_C1_INSOLUBLE_01 Razor + unique peptides CTR34_C1_INSOLUBLE_01 Razor + unique peptides CTR34_C2_INSOLUBLE_01 Razor + unique peptides CTR35_C1_INSOLUBLE_01 Razor + unique peptides CTR36_C1_INSOLUBLE_01 Razor + unique peptides CTR36_C1_INSOLUBLE_02 Razor + unique peptides CTR37_C1_INSOLUBLE_01 Razor + unique peptides CTR38_C1_INSOLUBLE_01 Razor + unique peptides CTR39_C1_INSOLUBLE_01 Razor + unique peptides CTR40_C1_INSOLUBLE_01 Razor + unique peptides CTR40_C1_INSOLUBLE_02 Razor + unique peptides CTR40_C1_INSOLUBLE_03 Razor + unique peptides CTR41_C1_INSOLUBLE_01 Razor + unique peptides CTR41_C1_INSOLUBLE_02 Razor + unique peptides CTR41_C1_INSOLUBLE_03 Razor + unique peptides CTR42_C1_INSOLUBLE_01 Razor + unique peptides CTR42_C1_INSOLUBLE_02 Razor + unique peptides CTR42_C1_INSOLUBLE_03 Razor + unique peptides CTR43_C2_INSOLUBLE_01 Razor + unique peptides CTR44_C1_INSOLUBLE_01 Unique peptides AD01_C1_INSOLUBLE_01 Unique peptides AD01_C1_INSOLUBLE_02 Unique peptides AD01_C1_INSOLUBLE_03 Unique peptides AD01_C2_INSOLUBLE_01 Unique peptides AD02_C1_INSOLUBLE_01 Unique peptides AD02_C1_INSOLUBLE_02 Unique peptides AD02_C2_INSOLUBLE_01 Unique peptides AD03_C1_INSOLUBLE_01 Unique peptides AD03_C1_INSOLUBLE_02 Unique peptides AD03_C1_INSOLUBLE_03 Unique peptides AD03_C2_INSOLUBLE_01 Unique peptides AD04_C1_INSOLUBLE_01 Unique peptides AD04_C2_INSOLUBLE_01 Unique peptides AD05_C2_INSOLUBLE_01 Unique peptides AD06_C1_INSOLUBLE_01 Unique peptides AD07_C1_INSOLUBLE_01 Unique peptides AD07_C1_INSOLUBLE_02 Unique peptides AD07_C1_INSOLUBLE_03 Unique peptides AD07_C2_INSOLUBLE_01 Unique peptides AD08_C2_INSOLUBLE_01 Unique peptides AD09_C1_INSOLUBLE_01 Unique peptides AD10_C1_INSOLUBLE_01 Unique peptides AD10_C2_INSOLUBLE_01 Unique peptides AD11_C2_INSOLUBLE_01 Unique peptides AD12_C2_INSOLUBLE_01 Unique peptides AD13_C2_INSOLUBLE_01 Unique peptides AD14_C2_INSOLUBLE_01 Unique peptides AD15_C2_INSOLUBLE_01 Unique peptides AD16_C2_INSOLUBLE_01 Unique peptides AD17_C2_INSOLUBLE_01 Unique peptides AD18_C2_INSOLUBLE_01 Unique peptides AD19_C2_INSOLUBLE_01 Unique peptides AD20_C1_INSOLUBLE_01 Unique peptides AD21_C1_INSOLUBLE_01 Unique peptides AD21_C2_INSOLUBLE_01 Unique peptides AD22_C1_INSOLUBLE_01 Unique peptides AD23_C1_INSOLUBLE_01 Unique peptides AD23_C1_INSOLUBLE_02 Unique peptides AD23_C2_INSOLUBLE_01 Unique peptides AD24_C1_INSOLUBLE_01 Unique peptides AD24_C1_INSOLUBLE_02 Unique peptides AD25_C1_INSOLUBLE_01 Unique peptides AD26_C1_INSOLUBLE_01 Unique peptides AD27_C1_INSOLUBLE_01 Unique peptides AD27_C1_INSOLUBLE_02 Unique peptides AD28_C2_INSOLUBLE_01 Unique peptides AD29_C1_INSOLUBLE_01 Unique peptides AD30_C1_INSOLUBLE_01 Unique peptides AD30_C1_INSOLUBLE_02 Unique peptides AD30_C2_INSOLUBLE_01 Unique peptides AD31_C2_INSOLUBLE_01 Unique peptides AD32_C2_INSOLUBLE_01 Unique peptides AD33_C2_INSOLUBLE_01 Unique peptides AD34_C1_INSOLUBLE_01 Unique peptides AD34_C1_INSOLUBLE_02 Unique peptides AD35_C1_INSOLUBLE_01 Unique peptides AD35_C1_INSOLUBLE_02 Unique peptides AD36_C1_INSOLUBLE_01 Unique peptides AD37_C1_INSOLUBLE_01 Unique peptides AD37_C2_INSOLUBLE_01 Unique peptides AD38_C1_INSOLUBLE_01 Unique peptides AD38_C1_INSOLUBLE_02 Unique peptides AD38_C1_INSOLUBLE_03 Unique peptides AD39_C2_INSOLUBLE_01 Unique peptides AD40_C2_INSOLUBLE_01 Unique peptides AD41_C2_INSOLUBLE_01 Unique peptides AD42_C2_INSOLUBLE_01 Unique peptides AD43_C1_INSOLUBLE_01 Unique peptides AD44_C1_INSOLUBLE_01 Unique peptides AD44_C1_INSOLUBLE_02 Unique peptides AD44_C1_INSOLUBLE_03 Unique peptides AD44_C1_INSOLUBLE_04 Unique peptides AD45_C1_INSOLUBLE_01 Unique peptides AD45_C1_INSOLUBLE_02 Unique peptides AD46_C1_INSOLUBLE_01 Unique peptides AD46_C1_INSOLUBLE_02 Unique peptides AD46_C1_INSOLUBLE_03 Unique peptides AD46_C2_INSOLUBLE_01 Unique peptides AD47_C1_INSOLUBLE_01 Unique peptides AD48_C2_INSOLUBLE_01 Unique peptides AD49_C2_INSOLUBLE_01 Unique peptides CTR01_C1_INSOLUBLE_01 Unique peptides CTR02_C1_INSOLUBLE_01 Unique peptides CTR03_C1_INSOLUBLE_01 Unique peptides CTR04_C1_INSOLUBLE_01 Unique peptides CTR05_C2_INSOLUBLE_01 Unique peptides CTR06_C2_INSOLUBLE_01 Unique peptides CTR07_C1_INSOLUBLE_01 Unique peptides CTR08_C1_INSOLUBLE_01 Unique peptides CTR08_C2_INSOLUBLE_01 Unique peptides CTR09_C2_INSOLUBLE_01 Unique peptides CTR10_C1_INSOLUBLE_01 Unique peptides CTR10_C2_INSOLUBLE_01 Unique peptides CTR11_C2_INSOLUBLE_01 Unique peptides CTR12_C2_INSOLUBLE_01 Unique peptides CTR13_C2_INSOLUBLE_01 Unique peptides CTR14_C2_INSOLUBLE_01 Unique peptides CTR15_C2_INSOLUBLE_01 Unique peptides CTR16_C2_INSOLUBLE_01 Unique peptides CTR17_C2_INSOLUBLE_01 Unique peptides CTR18_C2_INSOLUBLE_01 Unique peptides CTR19_C1_INSOLUBLE_01 Unique peptides CTR20_C1_INSOLUBLE_01 Unique peptides CTR21_C2_INSOLUBLE_01 Unique peptides CTR22_C2_INSOLUBLE_01 Unique peptides CTR23_C2_INSOLUBLE_01 Unique peptides CTR24_C1_INSOLUBLE_01 Unique peptides CTR25_C1_INSOLUBLE_01 Unique peptides CTR26_C2_INSOLUBLE_01 Unique peptides CTR27_C1_INSOLUBLE_01 Unique peptides CTR28_C1_INSOLUBLE_01 Unique peptides CTR28_C1_INSOLUBLE_02 Unique peptides CTR28_C2_INSOLUBLE_01 Unique peptides CTR29_C1_INSOLUBLE_01 Unique peptides CTR29_C1_INSOLUBLE_02 Unique peptides CTR29_C1_INSOLUBLE_03 Unique peptides CTR30_C1_INSOLUBLE_01 Unique peptides CTR30_C1_INSOLUBLE_02 Unique peptides CTR30_C2_INSOLUBLE_01 Unique peptides CTR31_C1_INSOLUBLE_01 Unique peptides CTR31_C2_INSOLUBLE_01 Unique peptides CTR32_C1_INSOLUBLE_01 Unique peptides CTR32_C2_INSOLUBLE_01 Unique peptides CTR33_C1_INSOLUBLE_01 Unique peptides CTR34_C1_INSOLUBLE_01 Unique peptides CTR34_C2_INSOLUBLE_01 Unique peptides CTR35_C1_INSOLUBLE_01 Unique peptides CTR36_C1_INSOLUBLE_01 Unique peptides CTR36_C1_INSOLUBLE_02 Unique peptides CTR37_C1_INSOLUBLE_01 Unique peptides CTR38_C1_INSOLUBLE_01 Unique peptides CTR39_C1_INSOLUBLE_01 Unique peptides CTR40_C1_INSOLUBLE_01 Unique peptides CTR40_C1_INSOLUBLE_02 Unique peptides CTR40_C1_INSOLUBLE_03 Unique peptides CTR41_C1_INSOLUBLE_01 Unique peptides CTR41_C1_INSOLUBLE_02 Unique peptides CTR41_C1_INSOLUBLE_03 Unique peptides CTR42_C1_INSOLUBLE_01 Unique peptides CTR42_C1_INSOLUBLE_02 Unique peptides CTR42_C1_INSOLUBLE_03 Unique peptides CTR43_C2_INSOLUBLE_01 Unique peptides CTR44_C1_INSOLUBLE_01 Sequence coverage [%] Unique + razor sequence coverage [%] Unique sequence coverage [%] Mol. weight [kDa] Sequence length Sequence lengths Q-value Score Identification type AD01_C1_INSOLUBLE_01 Identification type AD01_C1_INSOLUBLE_02 Identification type AD01_C1_INSOLUBLE_03 Identification type AD01_C2_INSOLUBLE_01 Identification type AD02_C1_INSOLUBLE_01 Identification type AD02_C1_INSOLUBLE_02 Identification type AD02_C2_INSOLUBLE_01 Identification type AD03_C1_INSOLUBLE_01 Identification type AD03_C1_INSOLUBLE_02 Identification type AD03_C1_INSOLUBLE_03 Identification type AD03_C2_INSOLUBLE_01 Identification type AD04_C1_INSOLUBLE_01 Identification type AD04_C2_INSOLUBLE_01 Identification type AD05_C2_INSOLUBLE_01 Identification type AD06_C1_INSOLUBLE_01 Identification type AD07_C1_INSOLUBLE_01 Identification type AD07_C1_INSOLUBLE_02 Identification type AD07_C1_INSOLUBLE_03 Identification type AD07_C2_INSOLUBLE_01 Identification type AD08_C2_INSOLUBLE_01 Identification type AD09_C1_INSOLUBLE_01 Identification type AD10_C1_INSOLUBLE_01 Identification type AD10_C2_INSOLUBLE_01 Identification type AD11_C2_INSOLUBLE_01 Identification type AD12_C2_INSOLUBLE_01 Identification type AD13_C2_INSOLUBLE_01 Identification type AD14_C2_INSOLUBLE_01 Identification type AD15_C2_INSOLUBLE_01 Identification type AD16_C2_INSOLUBLE_01 Identification type AD17_C2_INSOLUBLE_01 Identification type AD18_C2_INSOLUBLE_01 Identification type AD19_C2_INSOLUBLE_01 Identification type AD20_C1_INSOLUBLE_01 Identification type AD21_C1_INSOLUBLE_01 Identification type AD21_C2_INSOLUBLE_01 Identification type AD22_C1_INSOLUBLE_01 Identification type AD23_C1_INSOLUBLE_01 Identification type AD23_C1_INSOLUBLE_02 Identification type AD23_C2_INSOLUBLE_01 Identification type AD24_C1_INSOLUBLE_01 Identification type AD24_C1_INSOLUBLE_02 Identification type AD25_C1_INSOLUBLE_01 Identification type AD26_C1_INSOLUBLE_01 Identification type AD27_C1_INSOLUBLE_01 Identification type AD27_C1_INSOLUBLE_02 Identification type AD28_C2_INSOLUBLE_01 Identification type AD29_C1_INSOLUBLE_01 Identification type AD30_C1_INSOLUBLE_01 Identification type AD30_C1_INSOLUBLE_02 Identification type AD30_C2_INSOLUBLE_01 Identification type AD31_C2_INSOLUBLE_01 Identification type AD32_C2_INSOLUBLE_01 Identification type AD33_C2_INSOLUBLE_01 Identification type AD34_C1_INSOLUBLE_01 Identification type AD34_C1_INSOLUBLE_02 Identification type AD35_C1_INSOLUBLE_01 Identification type AD35_C1_INSOLUBLE_02 Identification type AD36_C1_INSOLUBLE_01 Identification type AD37_C1_INSOLUBLE_01 Identification type AD37_C2_INSOLUBLE_01 Identification type AD38_C1_INSOLUBLE_01 Identification type AD38_C1_INSOLUBLE_02 Identification type AD38_C1_INSOLUBLE_03 Identification type AD39_C2_INSOLUBLE_01 Identification type AD40_C2_INSOLUBLE_01 Identification type AD41_C2_INSOLUBLE_01 Identification type AD42_C2_INSOLUBLE_01 Identification type AD43_C1_INSOLUBLE_01 Identification type AD44_C1_INSOLUBLE_01 Identification type AD44_C1_INSOLUBLE_02 Identification type AD44_C1_INSOLUBLE_03 Identification type AD44_C1_INSOLUBLE_04 Identification type AD45_C1_INSOLUBLE_01 Identification type AD45_C1_INSOLUBLE_02 Identification type AD46_C1_INSOLUBLE_01 Identification type AD46_C1_INSOLUBLE_02 Identification type AD46_C1_INSOLUBLE_03 Identification type AD46_C2_INSOLUBLE_01 Identification type AD47_C1_INSOLUBLE_01 Identification type AD48_C2_INSOLUBLE_01 Identification type AD49_C2_INSOLUBLE_01 Identification type CTR01_C1_INSOLUBLE_01 Identification type CTR02_C1_INSOLUBLE_01 Identification type CTR03_C1_INSOLUBLE_01 Identification type CTR04_C1_INSOLUBLE_01 Identification type CTR05_C2_INSOLUBLE_01 Identification type CTR06_C2_INSOLUBLE_01 Identification type CTR07_C1_INSOLUBLE_01 Identification type CTR08_C1_INSOLUBLE_01 Identification type CTR08_C2_INSOLUBLE_01 Identification type CTR09_C2_INSOLUBLE_01 Identification type CTR10_C1_INSOLUBLE_01 Identification type CTR10_C2_INSOLUBLE_01 Identification type CTR11_C2_INSOLUBLE_01 Identification type CTR12_C2_INSOLUBLE_01 Identification type CTR13_C2_INSOLUBLE_01 Identification type CTR14_C2_INSOLUBLE_01 Identification type CTR15_C2_INSOLUBLE_01 Identification type CTR16_C2_INSOLUBLE_01 Identification type CTR17_C2_INSOLUBLE_01 Identification type CTR18_C2_INSOLUBLE_01 Identification type CTR19_C1_INSOLUBLE_01 Identification type CTR20_C1_INSOLUBLE_01 Identification type CTR21_C2_INSOLUBLE_01 Identification type CTR22_C2_INSOLUBLE_01 Identification type CTR23_C2_INSOLUBLE_01 Identification type CTR24_C1_INSOLUBLE_01 Identification type CTR25_C1_INSOLUBLE_01 Identification type CTR26_C2_INSOLUBLE_01 Identification type CTR27_C1_INSOLUBLE_01 Identification type CTR28_C1_INSOLUBLE_01 Identification type CTR28_C1_INSOLUBLE_02 Identification type CTR28_C2_INSOLUBLE_01 Identification type CTR29_C1_INSOLUBLE_01 Identification type CTR29_C1_INSOLUBLE_02 Identification type CTR29_C1_INSOLUBLE_03 Identification type CTR30_C1_INSOLUBLE_01 Identification type CTR30_C1_INSOLUBLE_02 Identification type CTR30_C2_INSOLUBLE_01 Identification type CTR31_C1_INSOLUBLE_01 Identification type CTR31_C2_INSOLUBLE_01 Identification type CTR32_C1_INSOLUBLE_01 Identification type CTR32_C2_INSOLUBLE_01 Identification type CTR33_C1_INSOLUBLE_01 Identification type CTR34_C1_INSOLUBLE_01 Identification type CTR34_C2_INSOLUBLE_01 Identification type CTR35_C1_INSOLUBLE_01 Identification type CTR36_C1_INSOLUBLE_01 Identification type CTR36_C1_INSOLUBLE_02 Identification type CTR37_C1_INSOLUBLE_01 Identification type CTR38_C1_INSOLUBLE_01 Identification type CTR39_C1_INSOLUBLE_01 Identification type CTR40_C1_INSOLUBLE_01 Identification type CTR40_C1_INSOLUBLE_02 Identification type CTR40_C1_INSOLUBLE_03 Identification type CTR41_C1_INSOLUBLE_01 Identification type CTR41_C1_INSOLUBLE_02 Identification type CTR41_C1_INSOLUBLE_03 Identification type CTR42_C1_INSOLUBLE_01 Identification type CTR42_C1_INSOLUBLE_02 Identification type CTR42_C1_INSOLUBLE_03 Identification type CTR43_C2_INSOLUBLE_01 Identification type CTR44_C1_INSOLUBLE_01 Sequence coverage AD01_C1_INSOLUBLE_01 [%] Sequence coverage AD01_C1_INSOLUBLE_02 [%] Sequence coverage AD01_C1_INSOLUBLE_03 [%] Sequence coverage AD01_C2_INSOLUBLE_01 [%] Sequence coverage AD02_C1_INSOLUBLE_01 [%] Sequence coverage AD02_C1_INSOLUBLE_02 [%] Sequence coverage AD02_C2_INSOLUBLE_01 [%] Sequence coverage AD03_C1_INSOLUBLE_01 [%] Sequence coverage AD03_C1_INSOLUBLE_02 [%] Sequence coverage AD03_C1_INSOLUBLE_03 [%] Sequence coverage AD03_C2_INSOLUBLE_01 [%] Sequence coverage AD04_C1_INSOLUBLE_01 [%] Sequence coverage AD04_C2_INSOLUBLE_01 [%] Sequence coverage AD05_C2_INSOLUBLE_01 [%] Sequence coverage AD06_C1_INSOLUBLE_01 [%] Sequence coverage AD07_C1_INSOLUBLE_01 [%] Sequence coverage AD07_C1_INSOLUBLE_02 [%] Sequence coverage AD07_C1_INSOLUBLE_03 [%] Sequence coverage AD07_C2_INSOLUBLE_01 [%] Sequence coverage AD08_C2_INSOLUBLE_01 [%] Sequence coverage AD09_C1_INSOLUBLE_01 [%] Sequence coverage AD10_C1_INSOLUBLE_01 [%] Sequence coverage AD10_C2_INSOLUBLE_01 [%] Sequence coverage AD11_C2_INSOLUBLE_01 [%] Sequence coverage AD12_C2_INSOLUBLE_01 [%] Sequence coverage AD13_C2_INSOLUBLE_01 [%] Sequence coverage AD14_C2_INSOLUBLE_01 [%] Sequence coverage AD15_C2_INSOLUBLE_01 [%] Sequence coverage AD16_C2_INSOLUBLE_01 [%] Sequence coverage AD17_C2_INSOLUBLE_01 [%] Sequence coverage AD18_C2_INSOLUBLE_01 [%] Sequence coverage AD19_C2_INSOLUBLE_01 [%] Sequence coverage AD20_C1_INSOLUBLE_01 [%] Sequence coverage AD21_C1_INSOLUBLE_01 [%] Sequence coverage AD21_C2_INSOLUBLE_01 [%] Sequence coverage AD22_C1_INSOLUBLE_01 [%] Sequence coverage AD23_C1_INSOLUBLE_01 [%] Sequence coverage AD23_C1_INSOLUBLE_02 [%] Sequence coverage AD23_C2_INSOLUBLE_01 [%] Sequence coverage AD24_C1_INSOLUBLE_01 [%] Sequence coverage AD24_C1_INSOLUBLE_02 [%] Sequence coverage AD25_C1_INSOLUBLE_01 [%] Sequence coverage AD26_C1_INSOLUBLE_01 [%] Sequence coverage AD27_C1_INSOLUBLE_01 [%] Sequence coverage AD27_C1_INSOLUBLE_02 [%] Sequence coverage AD28_C2_INSOLUBLE_01 [%] Sequence coverage AD29_C1_INSOLUBLE_01 [%] Sequence coverage AD30_C1_INSOLUBLE_01 [%] Sequence coverage AD30_C1_INSOLUBLE_02 [%] Sequence coverage AD30_C2_INSOLUBLE_01 [%] Sequence coverage AD31_C2_INSOLUBLE_01 [%] Sequence coverage AD32_C2_INSOLUBLE_01 [%] Sequence coverage AD33_C2_INSOLUBLE_01 [%] Sequence coverage AD34_C1_INSOLUBLE_01 [%] Sequence coverage AD34_C1_INSOLUBLE_02 [%] Sequence coverage AD35_C1_INSOLUBLE_01 [%] Sequence coverage AD35_C1_INSOLUBLE_02 [%] Sequence coverage AD36_C1_INSOLUBLE_01 [%] Sequence coverage AD37_C1_INSOLUBLE_01 [%] Sequence coverage AD37_C2_INSOLUBLE_01 [%] Sequence coverage AD38_C1_INSOLUBLE_01 [%] Sequence coverage AD38_C1_INSOLUBLE_02 [%] Sequence coverage AD38_C1_INSOLUBLE_03 [%] Sequence coverage AD39_C2_INSOLUBLE_01 [%] Sequence coverage AD40_C2_INSOLUBLE_01 [%] Sequence coverage AD41_C2_INSOLUBLE_01 [%] Sequence coverage AD42_C2_INSOLUBLE_01 [%] Sequence coverage AD43_C1_INSOLUBLE_01 [%] Sequence coverage AD44_C1_INSOLUBLE_01 [%] Sequence coverage AD44_C1_INSOLUBLE_02 [%] Sequence coverage AD44_C1_INSOLUBLE_03 [%] Sequence coverage AD44_C1_INSOLUBLE_04 [%] Sequence coverage AD45_C1_INSOLUBLE_01 [%] Sequence coverage AD45_C1_INSOLUBLE_02 [%] Sequence coverage AD46_C1_INSOLUBLE_01 [%] Sequence coverage AD46_C1_INSOLUBLE_02 [%] Sequence coverage AD46_C1_INSOLUBLE_03 [%] Sequence coverage AD46_C2_INSOLUBLE_01 [%] Sequence coverage AD47_C1_INSOLUBLE_01 [%] Sequence coverage AD48_C2_INSOLUBLE_01 [%] Sequence coverage AD49_C2_INSOLUBLE_01 [%] Sequence coverage CTR01_C1_INSOLUBLE_01 [%] Sequence coverage CTR02_C1_INSOLUBLE_01 [%] Sequence coverage CTR03_C1_INSOLUBLE_01 [%] Sequence coverage CTR04_C1_INSOLUBLE_01 [%] Sequence coverage CTR05_C2_INSOLUBLE_01 [%] Sequence coverage CTR06_C2_INSOLUBLE_01 [%] Sequence coverage CTR07_C1_INSOLUBLE_01 [%] Sequence coverage CTR08_C1_INSOLUBLE_01 [%] Sequence coverage CTR08_C2_INSOLUBLE_01 [%] Sequence coverage CTR09_C2_INSOLUBLE_01 [%] Sequence coverage CTR10_C1_INSOLUBLE_01 [%] Sequence coverage CTR10_C2_INSOLUBLE_01 [%] Sequence coverage CTR11_C2_INSOLUBLE_01 [%] Sequence coverage CTR12_C2_INSOLUBLE_01 [%] Sequence coverage CTR13_C2_INSOLUBLE_01 [%] Sequence coverage CTR14_C2_INSOLUBLE_01 [%] Sequence coverage CTR15_C2_INSOLUBLE_01 [%] Sequence coverage CTR16_C2_INSOLUBLE_01 [%] Sequence coverage CTR17_C2_INSOLUBLE_01 [%] Sequence coverage CTR18_C2_INSOLUBLE_01 [%] Sequence coverage CTR19_C1_INSOLUBLE_01 [%] Sequence coverage CTR20_C1_INSOLUBLE_01 [%] Sequence coverage CTR21_C2_INSOLUBLE_01 [%] Sequence coverage CTR22_C2_INSOLUBLE_01 [%] Sequence coverage CTR23_C2_INSOLUBLE_01 [%] Sequence coverage CTR24_C1_INSOLUBLE_01 [%] Sequence coverage CTR25_C1_INSOLUBLE_01 [%] Sequence coverage CTR26_C2_INSOLUBLE_01 [%] Sequence coverage CTR27_C1_INSOLUBLE_01 [%] Sequence coverage CTR28_C1_INSOLUBLE_01 [%] Sequence coverage CTR28_C1_INSOLUBLE_02 [%] Sequence coverage CTR28_C2_INSOLUBLE_01 [%] Sequence coverage CTR29_C1_INSOLUBLE_01 [%] Sequence coverage CTR29_C1_INSOLUBLE_02 [%] Sequence coverage CTR29_C1_INSOLUBLE_03 [%] Sequence coverage CTR30_C1_INSOLUBLE_01 [%] Sequence coverage CTR30_C1_INSOLUBLE_02 [%] Sequence coverage CTR30_C2_INSOLUBLE_01 [%] Sequence coverage CTR31_C1_INSOLUBLE_01 [%] Sequence coverage CTR31_C2_INSOLUBLE_01 [%] Sequence coverage CTR32_C1_INSOLUBLE_01 [%] Sequence coverage CTR32_C2_INSOLUBLE_01 [%] Sequence coverage CTR33_C1_INSOLUBLE_01 [%] Sequence coverage CTR34_C1_INSOLUBLE_01 [%] Sequence coverage CTR34_C2_INSOLUBLE_01 [%] Sequence coverage CTR35_C1_INSOLUBLE_01 [%] Sequence coverage CTR36_C1_INSOLUBLE_01 [%] Sequence coverage CTR36_C1_INSOLUBLE_02 [%] Sequence coverage CTR37_C1_INSOLUBLE_01 [%] Sequence coverage CTR38_C1_INSOLUBLE_01 [%] Sequence coverage CTR39_C1_INSOLUBLE_01 [%] Sequence coverage CTR40_C1_INSOLUBLE_01 [%] Sequence coverage CTR40_C1_INSOLUBLE_02 [%] Sequence coverage CTR40_C1_INSOLUBLE_03 [%] Sequence coverage CTR41_C1_INSOLUBLE_01 [%] Sequence coverage CTR41_C1_INSOLUBLE_02 [%] Sequence coverage CTR41_C1_INSOLUBLE_03 [%] Sequence coverage CTR42_C1_INSOLUBLE_01 [%] Sequence coverage CTR42_C1_INSOLUBLE_02 [%] Sequence coverage CTR42_C1_INSOLUBLE_03 [%] Sequence coverage CTR43_C2_INSOLUBLE_01 [%] Sequence coverage CTR44_C1_INSOLUBLE_01 [%] Intensity Intensity AD01_C1_INSOLUBLE_01 Intensity AD01_C1_INSOLUBLE_02 Intensity AD01_C1_INSOLUBLE_03 Intensity AD01_C2_INSOLUBLE_01 Intensity AD02_C1_INSOLUBLE_01 Intensity AD02_C1_INSOLUBLE_02 Intensity AD02_C2_INSOLUBLE_01 Intensity AD03_C1_INSOLUBLE_01 Intensity AD03_C1_INSOLUBLE_02 Intensity AD03_C1_INSOLUBLE_03 Intensity AD03_C2_INSOLUBLE_01 Intensity AD04_C1_INSOLUBLE_01 Intensity AD04_C2_INSOLUBLE_01 Intensity AD05_C2_INSOLUBLE_01 Intensity AD06_C1_INSOLUBLE_01 Intensity AD07_C1_INSOLUBLE_01 Intensity AD07_C1_INSOLUBLE_02 Intensity AD07_C1_INSOLUBLE_03 Intensity AD07_C2_INSOLUBLE_01 Intensity AD08_C2_INSOLUBLE_01 Intensity AD09_C1_INSOLUBLE_01 Intensity AD10_C1_INSOLUBLE_01 Intensity AD10_C2_INSOLUBLE_01 Intensity AD11_C2_INSOLUBLE_01 Intensity AD12_C2_INSOLUBLE_01 Intensity AD13_C2_INSOLUBLE_01 Intensity AD14_C2_INSOLUBLE_01 Intensity AD15_C2_INSOLUBLE_01 Intensity AD16_C2_INSOLUBLE_01 Intensity AD17_C2_INSOLUBLE_01 Intensity AD18_C2_INSOLUBLE_01 Intensity AD19_C2_INSOLUBLE_01 Intensity AD20_C1_INSOLUBLE_01 Intensity AD21_C1_INSOLUBLE_01 Intensity AD21_C2_INSOLUBLE_01 Intensity AD22_C1_INSOLUBLE_01 Intensity AD23_C1_INSOLUBLE_01 Intensity AD23_C1_INSOLUBLE_02 Intensity AD23_C2_INSOLUBLE_01 Intensity AD24_C1_INSOLUBLE_01 Intensity AD24_C1_INSOLUBLE_02 Intensity AD25_C1_INSOLUBLE_01 Intensity AD26_C1_INSOLUBLE_01 Intensity AD27_C1_INSOLUBLE_01 Intensity AD27_C1_INSOLUBLE_02 Intensity AD28_C2_INSOLUBLE_01 Intensity AD29_C1_INSOLUBLE_01 Intensity AD30_C1_INSOLUBLE_01 Intensity AD30_C1_INSOLUBLE_02 Intensity AD30_C2_INSOLUBLE_01 Intensity AD31_C2_INSOLUBLE_01 Intensity AD32_C2_INSOLUBLE_01 Intensity AD33_C2_INSOLUBLE_01 Intensity AD34_C1_INSOLUBLE_01 Intensity AD34_C1_INSOLUBLE_02 Intensity AD35_C1_INSOLUBLE_01 Intensity AD35_C1_INSOLUBLE_02 Intensity AD36_C1_INSOLUBLE_01 Intensity AD37_C1_INSOLUBLE_01 Intensity AD37_C2_INSOLUBLE_01 Intensity AD38_C1_INSOLUBLE_01 Intensity AD38_C1_INSOLUBLE_02 Intensity AD38_C1_INSOLUBLE_03 Intensity AD39_C2_INSOLUBLE_01 Intensity AD40_C2_INSOLUBLE_01 Intensity AD41_C2_INSOLUBLE_01 Intensity AD42_C2_INSOLUBLE_01 Intensity AD43_C1_INSOLUBLE_01 Intensity AD44_C1_INSOLUBLE_01 Intensity AD44_C1_INSOLUBLE_02 Intensity AD44_C1_INSOLUBLE_03 Intensity AD44_C1_INSOLUBLE_04 Intensity AD45_C1_INSOLUBLE_01 Intensity AD45_C1_INSOLUBLE_02 Intensity AD46_C1_INSOLUBLE_01 Intensity AD46_C1_INSOLUBLE_02 Intensity AD46_C1_INSOLUBLE_03 Intensity AD46_C2_INSOLUBLE_01 Intensity AD47_C1_INSOLUBLE_01 Intensity AD48_C2_INSOLUBLE_01 Intensity AD49_C2_INSOLUBLE_01 Intensity CTR01_C1_INSOLUBLE_01 Intensity CTR02_C1_INSOLUBLE_01 Intensity CTR03_C1_INSOLUBLE_01 Intensity CTR04_C1_INSOLUBLE_01 Intensity CTR05_C2_INSOLUBLE_01 Intensity CTR06_C2_INSOLUBLE_01 Intensity CTR07_C1_INSOLUBLE_01 Intensity CTR08_C1_INSOLUBLE_01 Intensity CTR08_C2_INSOLUBLE_01 Intensity CTR09_C2_INSOLUBLE_01 Intensity CTR10_C1_INSOLUBLE_01 Intensity CTR10_C2_INSOLUBLE_01 Intensity CTR11_C2_INSOLUBLE_01 Intensity CTR12_C2_INSOLUBLE_01 Intensity CTR13_C2_INSOLUBLE_01 Intensity CTR14_C2_INSOLUBLE_01 Intensity CTR15_C2_INSOLUBLE_01 Intensity CTR16_C2_INSOLUBLE_01 Intensity CTR17_C2_INSOLUBLE_01 Intensity CTR18_C2_INSOLUBLE_01 Intensity CTR19_C1_INSOLUBLE_01 Intensity CTR20_C1_INSOLUBLE_01 Intensity CTR21_C2_INSOLUBLE_01 Intensity CTR22_C2_INSOLUBLE_01 Intensity CTR23_C2_INSOLUBLE_01 Intensity CTR24_C1_INSOLUBLE_01 Intensity CTR25_C1_INSOLUBLE_01 Intensity CTR26_C2_INSOLUBLE_01 Intensity CTR27_C1_INSOLUBLE_01 Intensity CTR28_C1_INSOLUBLE_01 Intensity CTR28_C1_INSOLUBLE_02 Intensity CTR28_C2_INSOLUBLE_01 Intensity CTR29_C1_INSOLUBLE_01 Intensity CTR29_C1_INSOLUBLE_02 Intensity CTR29_C1_INSOLUBLE_03 Intensity CTR30_C1_INSOLUBLE_01 Intensity CTR30_C1_INSOLUBLE_02 Intensity CTR30_C2_INSOLUBLE_01 Intensity CTR31_C1_INSOLUBLE_01 Intensity CTR31_C2_INSOLUBLE_01 Intensity CTR32_C1_INSOLUBLE_01 Intensity CTR32_C2_INSOLUBLE_01 Intensity CTR33_C1_INSOLUBLE_01 Intensity CTR34_C1_INSOLUBLE_01 Intensity CTR34_C2_INSOLUBLE_01 Intensity CTR35_C1_INSOLUBLE_01 Intensity CTR36_C1_INSOLUBLE_01 Intensity CTR36_C1_INSOLUBLE_02 Intensity CTR37_C1_INSOLUBLE_01 Intensity CTR38_C1_INSOLUBLE_01 Intensity CTR39_C1_INSOLUBLE_01 Intensity CTR40_C1_INSOLUBLE_01 Intensity CTR40_C1_INSOLUBLE_02 Intensity CTR40_C1_INSOLUBLE_03 Intensity CTR41_C1_INSOLUBLE_01 Intensity CTR41_C1_INSOLUBLE_02 Intensity CTR41_C1_INSOLUBLE_03 Intensity CTR42_C1_INSOLUBLE_01 Intensity CTR42_C1_INSOLUBLE_02 Intensity CTR42_C1_INSOLUBLE_03 Intensity CTR43_C2_INSOLUBLE_01 Intensity CTR44_C1_INSOLUBLE_01 iBAQ peptides iBAQ iBAQ AD01_C1_INSOLUBLE_01 iBAQ AD01_C1_INSOLUBLE_02 iBAQ AD01_C1_INSOLUBLE_03 iBAQ AD01_C2_INSOLUBLE_01 iBAQ AD02_C1_INSOLUBLE_01 iBAQ AD02_C1_INSOLUBLE_02 iBAQ AD02_C2_INSOLUBLE_01 iBAQ AD03_C1_INSOLUBLE_01 iBAQ AD03_C1_INSOLUBLE_02 iBAQ AD03_C1_INSOLUBLE_03 iBAQ AD03_C2_INSOLUBLE_01 iBAQ AD04_C1_INSOLUBLE_01 iBAQ AD04_C2_INSOLUBLE_01 iBAQ AD05_C2_INSOLUBLE_01 iBAQ AD06_C1_INSOLUBLE_01 iBAQ AD07_C1_INSOLUBLE_01 iBAQ AD07_C1_INSOLUBLE_02 iBAQ AD07_C1_INSOLUBLE_03 iBAQ AD07_C2_INSOLUBLE_01 iBAQ AD08_C2_INSOLUBLE_01 iBAQ AD09_C1_INSOLUBLE_01 iBAQ AD10_C1_INSOLUBLE_01 iBAQ AD10_C2_INSOLUBLE_01 iBAQ AD11_C2_INSOLUBLE_01 iBAQ AD12_C2_INSOLUBLE_01 iBAQ AD13_C2_INSOLUBLE_01 iBAQ AD14_C2_INSOLUBLE_01 iBAQ AD15_C2_INSOLUBLE_01 iBAQ AD16_C2_INSOLUBLE_01 iBAQ AD17_C2_INSOLUBLE_01 iBAQ AD18_C2_INSOLUBLE_01 iBAQ AD19_C2_INSOLUBLE_01 iBAQ AD20_C1_INSOLUBLE_01 iBAQ AD21_C1_INSOLUBLE_01 iBAQ AD21_C2_INSOLUBLE_01 iBAQ AD22_C1_INSOLUBLE_01 iBAQ AD23_C1_INSOLUBLE_01 iBAQ AD23_C1_INSOLUBLE_02 iBAQ AD23_C2_INSOLUBLE_01 iBAQ AD24_C1_INSOLUBLE_01 iBAQ AD24_C1_INSOLUBLE_02 iBAQ AD25_C1_INSOLUBLE_01 iBAQ AD26_C1_INSOLUBLE_01 iBAQ AD27_C1_INSOLUBLE_01 iBAQ AD27_C1_INSOLUBLE_02 iBAQ AD28_C2_INSOLUBLE_01 iBAQ AD29_C1_INSOLUBLE_01 iBAQ AD30_C1_INSOLUBLE_01 iBAQ AD30_C1_INSOLUBLE_02 iBAQ AD30_C2_INSOLUBLE_01 iBAQ AD31_C2_INSOLUBLE_01 iBAQ AD32_C2_INSOLUBLE_01 iBAQ AD33_C2_INSOLUBLE_01 iBAQ AD34_C1_INSOLUBLE_01 iBAQ AD34_C1_INSOLUBLE_02 iBAQ AD35_C1_INSOLUBLE_01 iBAQ AD35_C1_INSOLUBLE_02 iBAQ AD36_C1_INSOLUBLE_01 iBAQ AD37_C1_INSOLUBLE_01 iBAQ AD37_C2_INSOLUBLE_01 iBAQ AD38_C1_INSOLUBLE_01 iBAQ AD38_C1_INSOLUBLE_02 iBAQ AD38_C1_INSOLUBLE_03 iBAQ AD39_C2_INSOLUBLE_01 iBAQ AD40_C2_INSOLUBLE_01 iBAQ AD41_C2_INSOLUBLE_01 iBAQ AD42_C2_INSOLUBLE_01 iBAQ AD43_C1_INSOLUBLE_01 iBAQ AD44_C1_INSOLUBLE_01 iBAQ AD44_C1_INSOLUBLE_02 iBAQ AD44_C1_INSOLUBLE_03 iBAQ AD44_C1_INSOLUBLE_04 iBAQ AD45_C1_INSOLUBLE_01 iBAQ AD45_C1_INSOLUBLE_02 iBAQ AD46_C1_INSOLUBLE_01 iBAQ AD46_C1_INSOLUBLE_02 iBAQ AD46_C1_INSOLUBLE_03 iBAQ AD46_C2_INSOLUBLE_01 iBAQ AD47_C1_INSOLUBLE_01 iBAQ AD48_C2_INSOLUBLE_01 iBAQ AD49_C2_INSOLUBLE_01 iBAQ CTR01_C1_INSOLUBLE_01 iBAQ CTR02_C1_INSOLUBLE_01 iBAQ CTR03_C1_INSOLUBLE_01 iBAQ CTR04_C1_INSOLUBLE_01 iBAQ CTR05_C2_INSOLUBLE_01 iBAQ CTR06_C2_INSOLUBLE_01 iBAQ CTR07_C1_INSOLUBLE_01 iBAQ CTR08_C1_INSOLUBLE_01 iBAQ CTR08_C2_INSOLUBLE_01 iBAQ CTR09_C2_INSOLUBLE_01 iBAQ CTR10_C1_INSOLUBLE_01 iBAQ CTR10_C2_INSOLUBLE_01 iBAQ CTR11_C2_INSOLUBLE_01 iBAQ CTR12_C2_INSOLUBLE_01 iBAQ CTR13_C2_INSOLUBLE_01 iBAQ CTR14_C2_INSOLUBLE_01 iBAQ CTR15_C2_INSOLUBLE_01 iBAQ CTR16_C2_INSOLUBLE_01 iBAQ CTR17_C2_INSOLUBLE_01 iBAQ CTR18_C2_INSOLUBLE_01 iBAQ CTR19_C1_INSOLUBLE_01 iBAQ CTR20_C1_INSOLUBLE_01 iBAQ CTR21_C2_INSOLUBLE_01 iBAQ CTR22_C2_INSOLUBLE_01 iBAQ CTR23_C2_INSOLUBLE_01 iBAQ CTR24_C1_INSOLUBLE_01 iBAQ CTR25_C1_INSOLUBLE_01 iBAQ CTR26_C2_INSOLUBLE_01 iBAQ CTR27_C1_INSOLUBLE_01 iBAQ CTR28_C1_INSOLUBLE_01 iBAQ CTR28_C1_INSOLUBLE_02 iBAQ CTR28_C2_INSOLUBLE_01 iBAQ CTR29_C1_INSOLUBLE_01 iBAQ CTR29_C1_INSOLUBLE_02 iBAQ CTR29_C1_INSOLUBLE_03 iBAQ CTR30_C1_INSOLUBLE_01 iBAQ CTR30_C1_INSOLUBLE_02 iBAQ CTR30_C2_INSOLUBLE_01 iBAQ CTR31_C1_INSOLUBLE_01 iBAQ CTR31_C2_INSOLUBLE_01 iBAQ CTR32_C1_INSOLUBLE_01 iBAQ CTR32_C2_INSOLUBLE_01 iBAQ CTR33_C1_INSOLUBLE_01 iBAQ CTR34_C1_INSOLUBLE_01 iBAQ CTR34_C2_INSOLUBLE_01 iBAQ CTR35_C1_INSOLUBLE_01 iBAQ CTR36_C1_INSOLUBLE_01 iBAQ CTR36_C1_INSOLUBLE_02 iBAQ CTR37_C1_INSOLUBLE_01 iBAQ CTR38_C1_INSOLUBLE_01 iBAQ CTR39_C1_INSOLUBLE_01 iBAQ CTR40_C1_INSOLUBLE_01 iBAQ CTR40_C1_INSOLUBLE_02 iBAQ CTR40_C1_INSOLUBLE_03 iBAQ CTR41_C1_INSOLUBLE_01 iBAQ CTR41_C1_INSOLUBLE_02 iBAQ CTR41_C1_INSOLUBLE_03 iBAQ CTR42_C1_INSOLUBLE_01 iBAQ CTR42_C1_INSOLUBLE_02 iBAQ CTR42_C1_INSOLUBLE_03 iBAQ CTR43_C2_INSOLUBLE_01 iBAQ CTR44_C1_INSOLUBLE_01 LFQ intensity AD01_C1_INSOLUBLE_01 LFQ intensity AD01_C1_INSOLUBLE_02 LFQ intensity AD01_C1_INSOLUBLE_03 LFQ intensity AD01_C2_INSOLUBLE_01 LFQ intensity AD02_C1_INSOLUBLE_01 LFQ intensity AD02_C1_INSOLUBLE_02 LFQ intensity AD02_C2_INSOLUBLE_01 LFQ intensity AD03_C1_INSOLUBLE_01 LFQ intensity AD03_C1_INSOLUBLE_02 LFQ intensity AD03_C1_INSOLUBLE_03 LFQ intensity AD03_C2_INSOLUBLE_01 LFQ intensity AD04_C1_INSOLUBLE_01 LFQ intensity AD04_C2_INSOLUBLE_01 LFQ intensity AD05_C2_INSOLUBLE_01 LFQ intensity AD06_C1_INSOLUBLE_01 LFQ intensity AD07_C1_INSOLUBLE_01 LFQ intensity AD07_C1_INSOLUBLE_02 LFQ intensity AD07_C1_INSOLUBLE_03 LFQ intensity AD07_C2_INSOLUBLE_01 LFQ intensity AD08_C2_INSOLUBLE_01 LFQ intensity AD09_C1_INSOLUBLE_01 LFQ intensity AD10_C1_INSOLUBLE_01 LFQ intensity AD10_C2_INSOLUBLE_01 LFQ intensity AD11_C2_INSOLUBLE_01 LFQ intensity AD12_C2_INSOLUBLE_01 LFQ intensity AD13_C2_INSOLUBLE_01 LFQ intensity AD14_C2_INSOLUBLE_01 LFQ intensity AD15_C2_INSOLUBLE_01 LFQ intensity AD16_C2_INSOLUBLE_01 LFQ intensity AD17_C2_INSOLUBLE_01 LFQ intensity AD18_C2_INSOLUBLE_01 LFQ intensity AD19_C2_INSOLUBLE_01 LFQ intensity AD20_C1_INSOLUBLE_01 LFQ intensity AD21_C1_INSOLUBLE_01 LFQ intensity AD21_C2_INSOLUBLE_01 LFQ intensity AD22_C1_INSOLUBLE_01 LFQ intensity AD23_C1_INSOLUBLE_01 LFQ intensity AD23_C1_INSOLUBLE_02 LFQ intensity AD23_C2_INSOLUBLE_01 LFQ intensity AD24_C1_INSOLUBLE_01 LFQ intensity AD24_C1_INSOLUBLE_02 LFQ intensity AD25_C1_INSOLUBLE_01 LFQ intensity AD26_C1_INSOLUBLE_01 LFQ intensity AD27_C1_INSOLUBLE_01 LFQ intensity AD27_C1_INSOLUBLE_02 LFQ intensity AD28_C2_INSOLUBLE_01 LFQ intensity AD29_C1_INSOLUBLE_01 LFQ intensity AD30_C1_INSOLUBLE_01 LFQ intensity AD30_C1_INSOLUBLE_02 LFQ intensity AD30_C2_INSOLUBLE_01 LFQ intensity AD31_C2_INSOLUBLE_01 LFQ intensity AD32_C2_INSOLUBLE_01 LFQ intensity AD33_C2_INSOLUBLE_01 LFQ intensity AD34_C1_INSOLUBLE_01 LFQ intensity AD34_C1_INSOLUBLE_02 LFQ intensity AD35_C1_INSOLUBLE_01 LFQ intensity AD35_C1_INSOLUBLE_02 LFQ intensity AD36_C1_INSOLUBLE_01 LFQ intensity AD37_C1_INSOLUBLE_01 LFQ intensity AD37_C2_INSOLUBLE_01 LFQ intensity AD38_C1_INSOLUBLE_01 LFQ intensity AD38_C1_INSOLUBLE_02 LFQ intensity AD38_C1_INSOLUBLE_03 LFQ intensity AD39_C2_INSOLUBLE_01 LFQ intensity AD40_C2_INSOLUBLE_01 LFQ intensity AD41_C2_INSOLUBLE_01 LFQ intensity AD42_C2_INSOLUBLE_01 LFQ intensity AD43_C1_INSOLUBLE_01 LFQ intensity AD44_C1_INSOLUBLE_01 LFQ intensity AD44_C1_INSOLUBLE_02 LFQ intensity AD44_C1_INSOLUBLE_03 LFQ intensity AD44_C1_INSOLUBLE_04 LFQ intensity AD45_C1_INSOLUBLE_01 LFQ intensity AD45_C1_INSOLUBLE_02 LFQ intensity AD46_C1_INSOLUBLE_01 LFQ intensity AD46_C1_INSOLUBLE_02 LFQ intensity AD46_C1_INSOLUBLE_03 LFQ intensity AD46_C2_INSOLUBLE_01 LFQ intensity AD47_C1_INSOLUBLE_01 LFQ intensity AD48_C2_INSOLUBLE_01 LFQ intensity AD49_C2_INSOLUBLE_01 LFQ intensity CTR01_C1_INSOLUBLE_01 LFQ intensity CTR02_C1_INSOLUBLE_01 LFQ intensity CTR03_C1_INSOLUBLE_01 LFQ intensity CTR04_C1_INSOLUBLE_01 LFQ intensity CTR05_C2_INSOLUBLE_01 LFQ intensity CTR06_C2_INSOLUBLE_01 LFQ intensity CTR07_C1_INSOLUBLE_01 LFQ intensity CTR08_C1_INSOLUBLE_01 LFQ intensity CTR08_C2_INSOLUBLE_01 LFQ intensity CTR09_C2_INSOLUBLE_01 LFQ intensity CTR10_C1_INSOLUBLE_01 LFQ intensity CTR10_C2_INSOLUBLE_01 LFQ intensity CTR11_C2_INSOLUBLE_01 LFQ intensity CTR12_C2_INSOLUBLE_01 LFQ intensity CTR13_C2_INSOLUBLE_01 LFQ intensity CTR14_C2_INSOLUBLE_01 LFQ intensity CTR15_C2_INSOLUBLE_01 LFQ intensity CTR16_C2_INSOLUBLE_01 LFQ intensity CTR17_C2_INSOLUBLE_01 LFQ intensity CTR18_C2_INSOLUBLE_01 LFQ intensity CTR19_C1_INSOLUBLE_01 LFQ intensity CTR20_C1_INSOLUBLE_01 LFQ intensity CTR21_C2_INSOLUBLE_01 LFQ intensity CTR22_C2_INSOLUBLE_01 LFQ intensity CTR23_C2_INSOLUBLE_01 LFQ intensity CTR24_C1_INSOLUBLE_01 LFQ intensity CTR25_C1_INSOLUBLE_01 LFQ intensity CTR26_C2_INSOLUBLE_01 LFQ intensity CTR27_C1_INSOLUBLE_01 LFQ intensity CTR28_C1_INSOLUBLE_01 LFQ intensity CTR28_C1_INSOLUBLE_02 LFQ intensity CTR28_C2_INSOLUBLE_01 LFQ intensity CTR29_C1_INSOLUBLE_01 LFQ intensity CTR29_C1_INSOLUBLE_02 LFQ intensity CTR29_C1_INSOLUBLE_03 LFQ intensity CTR30_C1_INSOLUBLE_01 LFQ intensity CTR30_C1_INSOLUBLE_02 LFQ intensity CTR30_C2_INSOLUBLE_01 LFQ intensity CTR31_C1_INSOLUBLE_01 LFQ intensity CTR31_C2_INSOLUBLE_01 LFQ intensity CTR32_C1_INSOLUBLE_01 LFQ intensity CTR32_C2_INSOLUBLE_01 LFQ intensity CTR33_C1_INSOLUBLE_01 LFQ intensity CTR34_C1_INSOLUBLE_01 LFQ intensity CTR34_C2_INSOLUBLE_01 LFQ intensity CTR35_C1_INSOLUBLE_01 LFQ intensity CTR36_C1_INSOLUBLE_01 LFQ intensity CTR36_C1_INSOLUBLE_02 LFQ intensity CTR37_C1_INSOLUBLE_01 LFQ intensity CTR38_C1_INSOLUBLE_01 LFQ intensity CTR39_C1_INSOLUBLE_01 LFQ intensity CTR40_C1_INSOLUBLE_01 LFQ intensity CTR40_C1_INSOLUBLE_02 LFQ intensity CTR40_C1_INSOLUBLE_03 LFQ intensity CTR41_C1_INSOLUBLE_01 LFQ intensity CTR41_C1_INSOLUBLE_02 LFQ intensity CTR41_C1_INSOLUBLE_03 LFQ intensity CTR42_C1_INSOLUBLE_01 LFQ intensity CTR42_C1_INSOLUBLE_02 LFQ intensity CTR42_C1_INSOLUBLE_03 LFQ intensity CTR43_C2_INSOLUBLE_01 LFQ intensity CTR44_C1_INSOLUBLE_01 MS/MS count AD01_C1_INSOLUBLE_01 MS/MS count AD01_C1_INSOLUBLE_02 MS/MS count AD01_C1_INSOLUBLE_03 MS/MS count AD01_C2_INSOLUBLE_01 MS/MS count AD02_C1_INSOLUBLE_01 MS/MS count AD02_C1_INSOLUBLE_02 MS/MS count AD02_C2_INSOLUBLE_01 MS/MS count AD03_C1_INSOLUBLE_01 MS/MS count AD03_C1_INSOLUBLE_02 MS/MS count AD03_C1_INSOLUBLE_03 MS/MS count AD03_C2_INSOLUBLE_01 MS/MS count AD04_C1_INSOLUBLE_01 MS/MS count AD04_C2_INSOLUBLE_01 MS/MS count AD05_C2_INSOLUBLE_01 MS/MS count AD06_C1_INSOLUBLE_01 MS/MS count AD07_C1_INSOLUBLE_01 MS/MS count AD07_C1_INSOLUBLE_02 MS/MS count AD07_C1_INSOLUBLE_03 MS/MS count AD07_C2_INSOLUBLE_01 MS/MS count AD08_C2_INSOLUBLE_01 MS/MS count AD09_C1_INSOLUBLE_01 MS/MS count AD10_C1_INSOLUBLE_01 MS/MS count AD10_C2_INSOLUBLE_01 MS/MS count AD11_C2_INSOLUBLE_01 MS/MS count AD12_C2_INSOLUBLE_01 MS/MS count AD13_C2_INSOLUBLE_01 MS/MS count AD14_C2_INSOLUBLE_01 MS/MS count AD15_C2_INSOLUBLE_01 MS/MS count AD16_C2_INSOLUBLE_01 MS/MS count AD17_C2_INSOLUBLE_01 MS/MS count AD18_C2_INSOLUBLE_01 MS/MS count AD19_C2_INSOLUBLE_01 MS/MS count AD20_C1_INSOLUBLE_01 MS/MS count AD21_C1_INSOLUBLE_01 MS/MS count AD21_C2_INSOLUBLE_01 MS/MS count AD22_C1_INSOLUBLE_01 MS/MS count AD23_C1_INSOLUBLE_01 MS/MS count AD23_C1_INSOLUBLE_02 MS/MS count AD23_C2_INSOLUBLE_01 MS/MS count AD24_C1_INSOLUBLE_01 MS/MS count AD24_C1_INSOLUBLE_02 MS/MS count AD25_C1_INSOLUBLE_01 MS/MS count AD26_C1_INSOLUBLE_01 MS/MS count AD27_C1_INSOLUBLE_01 MS/MS count AD27_C1_INSOLUBLE_02 MS/MS count AD28_C2_INSOLUBLE_01 MS/MS count AD29_C1_INSOLUBLE_01 MS/MS count AD30_C1_INSOLUBLE_01 MS/MS count AD30_C1_INSOLUBLE_02 MS/MS count AD30_C2_INSOLUBLE_01 MS/MS count AD31_C2_INSOLUBLE_01 MS/MS count AD32_C2_INSOLUBLE_01 MS/MS count AD33_C2_INSOLUBLE_01 MS/MS count AD34_C1_INSOLUBLE_01 MS/MS count AD34_C1_INSOLUBLE_02 MS/MS count AD35_C1_INSOLUBLE_01 MS/MS count AD35_C1_INSOLUBLE_02 MS/MS count AD36_C1_INSOLUBLE_01 MS/MS count AD37_C1_INSOLUBLE_01 MS/MS count AD37_C2_INSOLUBLE_01 MS/MS count AD38_C1_INSOLUBLE_01 MS/MS count AD38_C1_INSOLUBLE_02 MS/MS count AD38_C1_INSOLUBLE_03 MS/MS count AD39_C2_INSOLUBLE_01 MS/MS count AD40_C2_INSOLUBLE_01 MS/MS count AD41_C2_INSOLUBLE_01 MS/MS count AD42_C2_INSOLUBLE_01 MS/MS count AD43_C1_INSOLUBLE_01 MS/MS count AD44_C1_INSOLUBLE_01 MS/MS count AD44_C1_INSOLUBLE_02 MS/MS count AD44_C1_INSOLUBLE_03 MS/MS count AD44_C1_INSOLUBLE_04 MS/MS count AD45_C1_INSOLUBLE_01 MS/MS count AD45_C1_INSOLUBLE_02 MS/MS count AD46_C1_INSOLUBLE_01 MS/MS count AD46_C1_INSOLUBLE_02 MS/MS count AD46_C1_INSOLUBLE_03 MS/MS count AD46_C2_INSOLUBLE_01 MS/MS count AD47_C1_INSOLUBLE_01 MS/MS count AD48_C2_INSOLUBLE_01 MS/MS count AD49_C2_INSOLUBLE_01 MS/MS count CTR01_C1_INSOLUBLE_01 MS/MS count CTR02_C1_INSOLUBLE_01 MS/MS count CTR03_C1_INSOLUBLE_01 MS/MS count CTR04_C1_INSOLUBLE_01 MS/MS count CTR05_C2_INSOLUBLE_01 MS/MS count CTR06_C2_INSOLUBLE_01 MS/MS count CTR07_C1_INSOLUBLE_01 MS/MS count CTR08_C1_INSOLUBLE_01 MS/MS count CTR08_C2_INSOLUBLE_01 MS/MS count CTR09_C2_INSOLUBLE_01 MS/MS count CTR10_C1_INSOLUBLE_01 MS/MS count CTR10_C2_INSOLUBLE_01 MS/MS count CTR11_C2_INSOLUBLE_01 MS/MS count CTR12_C2_INSOLUBLE_01 MS/MS count CTR13_C2_INSOLUBLE_01 MS/MS count CTR14_C2_INSOLUBLE_01 MS/MS count CTR15_C2_INSOLUBLE_01 MS/MS count CTR16_C2_INSOLUBLE_01 MS/MS count CTR17_C2_INSOLUBLE_01 MS/MS count CTR18_C2_INSOLUBLE_01 MS/MS count CTR19_C1_INSOLUBLE_01 MS/MS count CTR20_C1_INSOLUBLE_01 MS/MS count CTR21_C2_INSOLUBLE_01 MS/MS count CTR22_C2_INSOLUBLE_01 MS/MS count CTR23_C2_INSOLUBLE_01 MS/MS count CTR24_C1_INSOLUBLE_01 MS/MS count CTR25_C1_INSOLUBLE_01 MS/MS count CTR26_C2_INSOLUBLE_01 MS/MS count CTR27_C1_INSOLUBLE_01 MS/MS count CTR28_C1_INSOLUBLE_01 MS/MS count CTR28_C1_INSOLUBLE_02 MS/MS count CTR28_C2_INSOLUBLE_01 MS/MS count CTR29_C1_INSOLUBLE_01 MS/MS count CTR29_C1_INSOLUBLE_02 MS/MS count CTR29_C1_INSOLUBLE_03 MS/MS count CTR30_C1_INSOLUBLE_01 MS/MS count CTR30_C1_INSOLUBLE_02 MS/MS count CTR30_C2_INSOLUBLE_01 MS/MS count CTR31_C1_INSOLUBLE_01 MS/MS count CTR31_C2_INSOLUBLE_01 MS/MS count CTR32_C1_INSOLUBLE_01 MS/MS count CTR32_C2_INSOLUBLE_01 MS/MS count CTR33_C1_INSOLUBLE_01 MS/MS count CTR34_C1_INSOLUBLE_01 MS/MS count CTR34_C2_INSOLUBLE_01 MS/MS count CTR35_C1_INSOLUBLE_01 MS/MS count CTR36_C1_INSOLUBLE_01 MS/MS count CTR36_C1_INSOLUBLE_02 MS/MS count CTR37_C1_INSOLUBLE_01 MS/MS count CTR38_C1_INSOLUBLE_01 MS/MS count CTR39_C1_INSOLUBLE_01 MS/MS count CTR40_C1_INSOLUBLE_01 MS/MS count CTR40_C1_INSOLUBLE_02 MS/MS count CTR40_C1_INSOLUBLE_03 MS/MS count CTR41_C1_INSOLUBLE_01 MS/MS count CTR41_C1_INSOLUBLE_02 MS/MS count CTR41_C1_INSOLUBLE_03 MS/MS count CTR42_C1_INSOLUBLE_01 MS/MS count CTR42_C1_INSOLUBLE_02 MS/MS count CTR42_C1_INSOLUBLE_03 MS/MS count CTR43_C2_INSOLUBLE_01 MS/MS count CTR44_C1_INSOLUBLE_01 MS/MS count Only identified by site Reverse Potential contaminant id Peptide IDs Peptide is razor Mod. peptide IDs Evidence IDs MS/MS IDs Best MS/MS Acetyl (K) site IDs GlyGly (K) site IDs Oxidation (M) site IDs Phospho (STY) site IDs Acetyl (K) site positions GlyGly (K) site positions Oxidation (M) site positions Phospho (STY) site positions Taxonomy IDs +2 2 2 IGLV8-61 LV861_HUMAN Immunoglobulin lambda variable 8-61 OS=Homo sapiens GN=IGLV8-61 PE=3 SV=7 1 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 2 0 0 0 0 0 1 0 0 1 1 2 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 2 0 0 0 0 0 1 0 0 1 1 2 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 2 0 0 0 0 0 1 0 0 1 1 2 0 0 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 1 0 1 1 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 0 0 14.8 14.8 14.8 12.814 122 122 0.0026537 3.4118 By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By MS/MS By MS/MS By matching 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7.4 0 0 0 0 0 0 0 0 0 7.4 0 0 7.4 7.4 0 0 0 14.8 0 0 0 0 0 7.4 0 0 7.4 7.4 14.8 0 0 0 7.4 0 0 0 7.4 0 0 0 7.4 0 0 7.4 0 0 0 0 0 0 0 0 0 0 0 0 0 7.4 0 7.4 0 0 0 0 0 7.4 0 0 0 7.4 7.4 7.4 0 7.4 0 0 0 0 0 0 7.4 7.4 0 0 7.4 0 0 7.4 0 7.4 7.4 7.4 7.4 7.4 7.4 7.4 0 7.4 7.4 7.4 7.4 7.4 7.4 0 7.4 7.4 0 7.4 0 7.4 0 0 0 0 0 7.4 7.4 7.4 7.4 0 0 0 0 1078800000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10544000 0 0 0 0 0 0 0 0 0 51433000 0 0 1201800 4708800 0 0 0 18292000 0 0 0 0 0 10671000 0 0 68315000 23689000 35386000 0 0 0 31720000 0 0 0 3843200 0 0 0 32011000 0 0 3585300 0 0 0 0 0 0 0 0 0 0 0 0 0 5269200 0 5220500 0 0 0 0 0 4125300 0 0 0 5130500 511940 1225500 0 2597800 0 0 0 0 0 0 550180 8679700 0 0 11430000 0 0 2551000 0 3444800 11409000 6653700 102450000 6602900 4239100 11507000 0 18876000 87506000 3313300 103810000 17227000 122910000 0 10760000 150530000 0 5686500 0 10178000 0 0 0 0 0 29254000 15357000 10771000 3667500 0 0 0 0 4 269710000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2635900 0 0 0 0 0 0 0 0 0 12858000 0 0 300440 1177200 0 0 0 4573100 0 0 0 0 0 2667800 0 0 17079000 5922200 8846500 0 0 0 7930000 0 0 0 960790 0 0 0 8002700 0 0 896330 0 0 0 0 0 0 0 0 0 0 0 0 0 1317300 0 1305100 0 0 0 0 0 1031300 0 0 0 1282600 127990 306370 0 649460 0 0 0 0 0 0 137550 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lambda variable 3-9 OS=Homo sapiens GN=IGLV3-9 PE=3 SV=1 2 5 3 3 0 1 1 0 1 1 1 1 1 1 0 0 0 1 1 1 1 1 1 2 2 1 0 1 2 1 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 0 0 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 0 1 0 0 0 1 0 0 1 1 1 1 0 1 0 1 1 0 1 0 1 3 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 2 1 0 1 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 49.6 26.1 26.1 12.332 115 115;115 0.0026942 3.5738 By matching By matching By 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497440;497934;1532168 -1;-1;-1 +1;1;1 1;1;1 1;1;1 IGKV1-8;IGKV1-9;IGKV1-27 KV108_HUMAN Immunoglobulin kappa variable 1-8 OS=Homo sapiens GN=IGKV1-8 PE=3 SV=1;KV109_HUMAN Immunoglobulin kappa variable 1-9 OS=Homo sapiens GN=IGKV1-9 PE=3 SV=1;KV127_HUMAN Immunoglobulin kappa variable 1-27 OS=Homo sapiens GN=IGKV1-27 PE=3 SV=1 3 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 1 0 1 0 1 0 1 0 1 1 0 1 0 0 0 1 0 0 0 1 0 0 1 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 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18182000 0 0 0 0 0 0 14774000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14032000 0 8020000 4632800 0 0 4412700 4071900 6054500 0 5611900 0 8952000 0 8587400 0 11739000 12158000 0 12795000 0 0 0 16509000 0 0 0 11888000 0 0 22658000 0 0 8390800 0 0 4 63938000 0 0 703210 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3917500 0 0 0 0 0 0 0 438010 0 0 0 0 0 2166300 0 3996100 0 0 0 0 0 0 0 0 4349300 0 4545600 0 0 0 0 0 0 3693600 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3508000 0 2005000 1158200 0 0 1103200 1018000 1513600 0 1403000 0 2238000 0 2146900 0 2934700 3039500 0 3198800 0 0 0 4127300 0 0 0 2972000 0 0 5664400 0 0 2097700 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 8390800 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 2 0 0 0 1 0 0 0 1 0 2 0 0 16 6 17394 True 19471 1157959;1157960;1157961;1157962;1157963;1157964;1157965;1157966;1157967;1157968;1157969;1157970;1157971;1157972;1157973;1157974;1157975;1157976;1157977;1157978;1157979;1157980;1157981;1157982;1157983 1070261;1070262;1070263;1070264;1070265;1070266;1070267;1070268;1070269;1070270;1070271;1070272;1070273;1070274;1070275;1070276;1070277;1070278;1070279;1070280;1070281;1070282;1070283;1070284 1070262 -1;-1;-1 +3;3 2;2 2;2 Ig kappa chain V-III region VG IGKV3D-11 KVD11_HUMAN Immunoglobulin kappa variable 3D-11 OS=Homo sapiens GN=IGKV3D-11 PE=3 SV=6;KV311_HUMAN Immunoglobulin kappa variable 3-11 OS=Homo sapiens GN=IGKV3-11 PE=1 SV=1 2 3 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 2 1 1 1 1 0 1 0 1 1 1 1 2 1 2 2 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 0 1 1 2 0 2 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 0 1 0 0 2 2 0 1 1 1 0 0 0 2 1 1 1 1 2 1 1 1 2 1 1 0 1 3 1 2 1 2 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 0 1 1 1 0 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 2 1 1 1 1 1 1 1 1 1 1 1 0 1 2 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 0 1 1 1 0 1 1 1 1 1 1 0 0 1 1 1 1 1 0 0 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 0 1 0 0 1 1 0 1 1 1 0 0 0 2 1 1 1 1 1 1 1 1 1 1 1 0 1 2 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 32.2 26.1 26.1 12.625 115 115;115 0 8.9899 By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 13.9 7.8 7.8 13.9 7.8 7.8 7.8 13.9 7.8 7.8 7.8 7.8 0 7.8 0 7.8 7.8 7.8 7.8 13.9 7.8 13.9 13.9 13.9 7.8 7.8 7.8 13.9 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 26.1 0 7.8 7.8 13.9 0 13.9 7.8 7.8 7.8 7.8 7.8 0 0 7.8 7.8 7.8 7.8 7.8 0 0 0 7.8 7.8 6.1 7.8 7.8 7.8 0 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 0 0 7.8 0 0 7.8 7.8 0 7.8 0 0 13.9 13.9 0 7.8 7.8 7.8 0 0 0 26.1 7.8 7.8 7.8 7.8 13.9 7.8 7.8 7.8 13.9 7.8 7.8 0 7.8 32.2 7.8 24.3 7.8 13.9 7.8 0 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 7.8 0 4355000000 1634400 4471100 18005000 14015000 2952500 4877600 55207000 1975300 1026300 632840 21554000 1459600 19925000 36251000 49073000 2112900 1277500 838180 72162000 80830000 11417000 94764000 35221000 2651100 47124000 62508000 0 4202100 0 46561000 1745000 27766000 35539000 34933000 10508000 30733000 3160400 89230000 52056000 22124000 42309000 72407000 88222000 42845000 8122900 16277000 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165953;165954;165955;165956;180306;180307;180308;180309;180310;180311;180312;180313;180314;180315;180316;180317;180318;180319;180320;180321;180322;180323;1158241;1158242;1158243;1158244;1158245;1158246;1158247;1158248;1158249;1158250;1158251;1158252;1158253;1158254;1158255;1158256;1158257;1158258;1158259;1158260;1158261;1158262;1158263;1158264;1158265;1158266;1158267;1158268;1158269;1158270;1158271;1158272;1158273;1158274;1158275;1158276;1158277;1158278;1158279;1158280;1158281;1158282;1158283;1158284;1158285;1158286;1158287;1158288;1158289;1158290;1158291;1158292;1158293;1158294;1158295;1158296;1158297;1158298;1158299;1158300;1158301;1158302;1158303;1158304;1158305;1158306;1158307;1158308;1158309;1158310;1158311;1158312;1158313;1158314;1158315;1158316;1158317;1158318;1158319;1158320;1158321;1158322;1158323;1158324;1158325;1158326;1158327;1158328;1158329;1158330;1158331;1158332;1158333;1158334;1158335;1158336;1158337;1158338;1158339;1158340;1158341;1158342;1158343;1158344;1158345;1158346;1158347;1158348;1158349;1158350;1158351;1158352;1158353;1158354;1158355;1158356;1158357;1158358;1158359;1158360;1158361;1158362;1158363;1158364;1158365;1158366;1158367;1158368;1158369;1158370;1158371;1158372 150381;150382;150383;150384;150385;164243;164244;164245;164246;164247;164248;164249;164250;164251;164252;164253;164254;1070551;1070552;1070553;1070554;1070555;1070556;1070557;1070558;1070559;1070560;1070561;1070562;1070563;1070564;1070565;1070566;1070567;1070568;1070569;1070570;1070571;1070572;1070573;1070574;1070575;1070576;1070577;1070578;1070579;1070580;1070581;1070582;1070583;1070584;1070585;1070586;1070587;1070588;1070589;1070590;1070591;1070592;1070593;1070594;1070595;1070596;1070597;1070598;1070599;1070600;1070601;1070602;1070603;1070604;1070605;1070606;1070607;1070608;1070609;1070610;1070611;1070612;1070613;1070614;1070615;1070616;1070617;1070618;1070619;1070620;1070621;1070622;1070623;1070624;1070625;1070626;1070627;1070628;1070629;1070630;1070631;1070632;1070633;1070634;1070635;1070636;1070637;1070638;1070639;1070640;1070641;1070642;1070643;1070644;1070645;1070646;1070647;1070648 150382;164252;1070583 -1;-1 +4 4 3 IGHV3-49 HV349_HUMAN Immunoglobulin heavy variable 3-49 OS=Homo sapiens GN=IGHV3-49 PE=3 SV=1 1 4 4 3 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 2 0 0 0 0 0 0 0 2 0 1 2 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 2 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 2 0 0 0 0 0 0 0 2 0 1 2 1 1 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 1 0 1 0 1 1 0 2 0 0 0 0 0 0 0 2 0 1 1 1 1 0 1 0 0 0 37 37 27.7 13.056 119 119 0.0014259 4.5935 By matching By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS 0 0 0 7.6 0 0 7.6 0 0 0 0 0 0 0 7.6 0 0 0 7.6 0 0 7.6 0 0 0 7.6 0 0 0 0 0 0 0 0 0 7.6 0 0 0 7.6 0 0 7.6 7.6 0 0 10.9 0 0 0 7.6 0 0 0 0 0 0 0 0 0 0 0 0 7.6 0 0 0 0 0 0 0 0 7.6 0 0 0 0 16.8 0 7.6 7.6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7.6 0 0 0 0 0 16.8 0 0 0 0 9.2 0 0 9.2 0 0 0 0 0 9.2 0 7.6 0 9.2 0 7.6 9.2 0 16.8 0 0 0 0 0 0 0 16.8 0 7.6 16.8 7.6 7.6 0 7.6 0 0 0 335470000 0 0 0 5105300 0 0 11050000 0 0 0 0 0 0 0 9907400 0 0 0 2244700 0 0 8187000 0 0 0 6430300 0 0 0 0 0 0 0 0 0 6492900 0 0 0 3518300 0 0 6611500 2916200 0 0 0 0 0 0 2940000 0 0 0 0 0 0 0 0 0 0 0 0 9040800 0 0 0 0 0 0 0 0 0 0 0 0 0 43650000 0 13625000 3170300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6168500 0 0 0 0 0 20645000 0 0 0 0 6049700 0 0 13391000 0 0 0 0 0 5795300 0 4432900 0 11378000 0 5342800 4946400 0 29108000 0 0 0 0 0 0 0 13111000 0 28721000 17960000 14472000 13223000 0 5833100 0 0 0 7 47924000 0 0 0 729330 0 0 1578600 0 0 0 0 0 0 0 1415300 0 0 0 320670 0 0 1169600 0 0 0 918610 0 0 0 0 0 0 0 0 0 927550 0 0 0 502610 0 0 944500 416610 0 0 0 0 0 0 420000 0 0 0 0 0 0 0 0 0 0 0 0 1291500 0 0 0 0 0 0 0 0 0 0 0 0 0 6235600 0 1946500 452910 0 0 0 0 0 0 0 0 0 0 0 0 0 0 881210 0 0 0 0 0 2949300 0 0 0 0 864240 0 0 1913000 0 0 0 0 0 827900 0 633280 0 1625400 0 763260 706620 0 4158300 0 0 0 0 0 0 0 1873000 0 4103000 2565700 2067500 1888900 0 833300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 43650000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 11 8 3030;10135;24546;26339 True;True;True;True 3410;11354;27875;29916 205888;652675;652676;652677;652678;652679;652680;652681;652682;652683;652684;652685;652686;652687;652688;652689;652690;652691;652692;652693;652694;652695;652696;652697;652698;652699;652700;652701;1605490;1605491;1605492;1605493;1605494;1605495;1605496;1605497;1714396;1714397 186183;599905;599906;599907;599908;599909;599910;599911;599912;599913;599914;1478300;1478301;1575593;1575594 186183;599912;1478300;1575594 -1 +2 2 2 IGHV6-1 HV601_HUMAN Immunoglobulin heavy variable 6-1 OS=Homo sapiens GN=IGHV6-1 PE=3 SV=1 1 2 2 2 2 2 1 1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 1 2 1 1 1 0 2 1 0 1 0 0 0 1 0 1 1 1 0 2 1 0 1 1 1 0 0 0 1 2 1 1 0 1 2 0 0 1 1 0 0 1 0 0 0 1 0 1 2 0 1 1 0 1 0 0 0 0 0 1 1 1 0 0 0 0 1 0 0 0 2 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 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By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching 13.2 13.2 5.8 5.8 0 0 7.4 0 0 0 5.8 0 5.8 5.8 5.8 0 0 7.4 5.8 13.2 5.8 5.8 5.8 0 13.2 7.4 0 7.4 0 0 0 7.4 0 5.8 7.4 5.8 0 13.2 5.8 0 5.8 5.8 7.4 0 0 0 5.8 13.2 5.8 5.8 0 5.8 13.2 0 0 5.8 5.8 0 0 5.8 0 0 0 7.4 0 5.8 13.2 0 7.4 5.8 0 5.8 0 0 0 0 0 5.8 5.8 5.8 0 0 0 0 5.8 0 0 0 13.2 0 0 5.8 5.8 0 0 5.8 0 0 0 5.8 0 0 7.4 0 5.8 0 0 0 5.8 0 5.8 0 0 0 5.8 0 0 0 5.8 5.8 0 0 5.8 0 5.8 5.8 0 13.2 5.8 0 5.8 0 0 0 0 0 0 13.2 0 13.2 0 7.4 0 1426600000 7030400 2892800 15894000 41892000 0 0 5718000 0 0 0 23859000 0 20779000 9453200 24774000 0 0 68687 12988000 26610000 1239600 11223000 24569000 0 28765000 5763400 0 12393000 0 0 0 15440000 0 7041800 2545000 0 0 5291300 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652497;652498;652499;652500;652501;652502;652503;652504;652505;652506;652507;652508;652509;652510;652511;652512;652513;652514;652515;652516;652517;652518;652519;652520;652521;652522;652523;652524;652525;652526;652527;652528;652529;652530;652531;652532;652533;652534;652535;652536;652537;652538;652539;652540;652541;652542;652543;652544;652545;652546;652547;652548;652549;652550;652551;652552;652553;652554;652555;652556;652557;652558;652559;652560;652561;652562;652563;652564;919080;919081;919082;919083;919084;919085;919086;919087;919088;919089;919090;919091;919092;919093;919094;919095;919096;919097;919098;919099;919100;919101;919102;919103;919104;919105 599783;599784;599785;599786;599787;599788;599789;599790;599791;599792;599793;599794;599795;599796;599797;599798;599799;599800;599801;599802;599803;599804;599805;599806;599807;599808;599809;599810;599811;599812;599813;599814;599815;599816;599817;599818;599819;599820;599821;599822;599823;599824;599825;599826;599827;599828;599829;599830;599831;599832;599833;599834;599835;599836;599837;599838;599839;850039;850040;850041;850042 599828;850039 -1 +3 2 2 IGHV3-15 HV315_HUMAN Immunoglobulin heavy variable 3-15 OS=Homo sapiens GN=IGHV3-15 PE=3 SV=1 1 3 2 2 1 0 2 2 0 0 2 1 1 1 1 1 1 1 2 0 1 1 1 2 0 3 1 1 1 1 0 0 0 2 0 0 0 0 2 0 0 2 2 0 1 1 2 0 0 0 2 2 3 1 0 1 0 0 0 1 2 0 1 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 1 2 1 1 0 1 2 1 0 1 1 0 1 1 1 0 1 2 0 0 0 0 2 2 1 1 0 1 1 1 3 2 1 1 1 1 1 0 2 1 1 1 3 1 2 2 1 2 3 2 2 2 1 0 0 2 2 2 2 0 3 2 0 1 0 0 1 1 0 0 1 1 1 1 0 1 0 0 1 0 1 1 0 1 0 2 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 2 1 0 0 1 2 0 0 0 2 2 2 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 2 1 0 0 1 0 0 0 1 0 1 0 2 0 1 1 0 2 2 1 1 1 0 0 0 1 2 2 1 0 2 2 0 1 0 0 1 1 0 0 1 1 1 1 0 1 0 0 1 0 1 1 0 1 0 2 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 2 1 0 0 1 2 0 0 0 2 2 2 0 0 0 0 0 0 1 2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 2 1 0 0 1 0 0 0 1 0 1 0 2 0 1 1 0 2 2 1 1 1 0 0 0 1 2 2 1 0 2 2 0 1 26.1 20.2 20.2 12.926 119 119 0.0018631 4.2224 By matching By MS/MS By MS/MS By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By MS/MS By MS/MS By MS/MS By matching By matching By matching By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching 5.9 0 15.1 16.8 0 0 16.8 10.9 10.9 10.9 5.9 10.9 5.9 5.9 16.8 0 10.9 10.9 5.9 16.8 0 26.1 5.9 10.9 5.9 5.9 0 0 0 16.8 0 0 0 0 16.8 0 0 20.2 15.1 0 5.9 10.9 20.2 0 0 0 20.2 20.2 26.1 5.9 0 5.9 0 0 0 10.9 20.2 0 5.9 5.9 0 0 0 0 9.2 0 5.9 0 0 0 0 5.9 0 0 0 5.9 5.9 5.9 0 5.9 15.1 9.2 5.9 0 5.9 15.1 5.9 0 5.9 5.9 0 5.9 5.9 5.9 0 5.9 15.1 0 0 0 0 15.1 15.1 5.9 5.9 0 5.9 5.9 5.9 26.1 16.8 5.9 5.9 9.2 5.9 5.9 0 15.1 5.9 9.2 5.9 26.1 5.9 16.8 15.1 5.9 20.2 26.1 16.8 16.8 16.8 5.9 0 0 16.8 20.2 20.2 15.1 0 26.1 20.2 0 9.2 532960000 0 0 4433300 20007000 0 0 20141000 294530 587940 2427100 0 3575900 0 0 5520900 0 335930 2999400 0 7813600 0 19709000 0 387500 0 0 0 0 0 945820 0 0 0 0 24443000 0 0 20619000 20320000 0 0 7606200 10978000 0 0 0 30782000 10074000 22270000 0 0 0 0 0 0 12690000 23456000 0 0 0 0 0 0 0 11854000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10809000 6249900 0 0 0 994510 0 0 0 0 0 0 0 0 0 0 2221400 0 0 0 0 9390800 5386500 0 0 0 0 0 0 14349000 8141800 0 0 2509400 0 0 0 9278400 0 8967800 0 16712000 0 6260700 9288500 0 23084000 14647000 18243000 4325700 8441300 0 0 0 3526300 17427000 11025000 5220900 0 16683000 12049000 0 3458100 7 76138000 0 0 633330 2858200 0 0 2877400 42075 83991 346730 0 510840 0 0 788700 0 47990 428480 0 1116200 0 2815600 0 55357 0 0 0 0 0 135120 0 0 0 0 3491900 0 0 2945600 2902800 0 0 1086600 1568300 0 0 0 4397400 1439200 3181400 0 0 0 0 0 0 1812800 3350900 0 0 0 0 0 0 0 1693500 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1544200 892850 0 0 0 142070 0 0 0 0 0 0 0 0 0 0 317340 0 0 0 0 1341500 769500 0 0 0 0 0 0 2049800 1163100 0 0 358480 0 0 0 1325500 0 1281100 0 2387500 0 894390 1326900 0 3297700 2092500 2606200 617950 1205900 0 0 0 503760 2489600 1574900 745840 0 2383300 1721400 0 494010 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18086000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18867000 0 0 0 0 11591000 0 0 0 34543000 10880000 22875000 0 0 0 0 0 0 0 21108000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14032000 0 0 0 0 0 0 0 0 0 0 0 18772000 0 0 0 0 25161000 11856000 0 0 0 0 0 0 0 15575000 11163000 0 0 16216000 13141000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 2 0 1 1 0 4 1 2 0 2 0 0 0 0 2 1 0 0 0 2 0 0 28 10 10136;21298;26250 False;True;True 11355;24293;29810 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599915;599916;599917;599918;599919;599920;599921;599922;599923;599924;599925;599926;599927;599928;599929;599930;599931;599932;599933;599934;599935;599936;599937;599938;599939;599940;599941;599942;599943;599944;599945;599946;599947;599948;599949;599950;599951;599952;599953;599954;599955;599956;599957;599958;599959;599960;599961;599962;599963;599964;599965;599966;599967;599968;599969;599970;1316815;1316816;1316817;1316818;1316819;1316820;1316821;1316822;1316823;1316824;1316825;1316826;1316827;1316828;1571116;1571117;1571118;1571119;1571120;1571121;1571122;1571123;1571124;1571125;1571126;1571127;1571128;1571129;1571130;1571131;1571132;1571133;1571134;1571135;1571136 599968;1316817;1571127 -1 +4;2;2;1 4;2;2;1 2;0;0;0 Ig heavy chain V-III region BUT IGHV3-74;IGHV3-66 HV374_HUMAN Immunoglobulin heavy variable 3-74 OS=Homo sapiens GN=IGHV3-74 PE=3 SV=1;HV366_HUMAN Immunoglobulin heavy variable 3-66 OS=Homo sapiens GN=IGHV3-66 PE=3 SV=1;HV353_HUMAN Immunoglobulin heavy variable 3-53 OS=Homo sapiens GN=IGHV3-53 PE=1 SV=2 4 4 4 2 2 3 3 0 1 1 2 0 0 0 2 0 1 3 3 0 0 0 1 2 3 3 2 2 2 1 2 3 2 3 2 2 2 3 2 2 2 4 2 3 2 2 3 3 1 0 4 4 3 2 0 1 0 2 0 4 4 2 2 0 2 2 2 2 2 2 3 3 2 1 2 2 1 1 2 3 3 3 3 4 2 2 2 2 2 3 1 2 3 1 3 2 0 2 0 3 2 3 0 2 0 2 4 0 1 1 2 3 2 3 4 3 2 2 3 1 1 4 2 4 1 4 1 3 4 2 3 4 4 3 4 2 2 3 2 2 2 3 2 3 2 1 2 2 3 3 0 1 1 2 0 0 0 2 0 1 3 3 0 0 0 1 2 3 3 2 2 2 1 2 3 2 3 2 2 2 3 2 2 2 4 2 3 2 2 3 3 1 0 4 4 3 2 0 1 0 2 0 4 4 2 2 0 2 2 2 2 2 2 3 3 2 1 2 2 1 1 2 3 3 3 3 4 2 2 2 2 2 3 1 2 3 1 3 2 0 2 0 3 2 3 0 2 0 2 4 0 1 1 2 3 2 3 4 3 2 2 3 1 1 4 2 4 1 4 1 3 4 2 3 4 4 3 4 2 2 3 2 2 2 3 2 3 2 1 2 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1 0 1 2 1 1 0 0 1 1 0 0 2 2 1 1 0 1 0 1 0 2 2 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 1 1 1 1 1 2 0 0 0 0 0 1 0 1 1 1 1 0 0 0 0 1 0 1 0 1 0 0 2 0 1 1 0 1 0 1 2 1 1 0 1 0 0 2 1 2 1 2 1 1 2 1 1 2 2 1 2 0 0 1 0 0 0 2 0 2 0 0 0 37.6 37.6 18.8 12.839 117 117;116;116;117 0 234.09 By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By 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IGHV4-28 HV428_HUMAN Immunoglobulin heavy variable 4-28 OS=Homo sapiens GN=IGHV4-28 PE=3 SV=1 1 1 1 1 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 1 0 1 0 0 1 1 0 0 1 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 0 0 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 0 1 1 0 0 1 0 1 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1 7.7 7.7 7.7 13.124 117 117 1 -2 By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By matching By MS/MS By MS/MS By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By matching By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching 0 0 7.7 7.7 0 0 7.7 0 0 0 7.7 0 0 7.7 0 0 0 0 7.7 7.7 0 7.7 0 0 7.7 7.7 0 0 7.7 7.7 7.7 7.7 0 7.7 7.7 0 0 0 0 0 0 0 0 0 0 0 7.7 0 7.7 7.7 7.7 7.7 0 0 7.7 7.7 7.7 0 0 0 7.7 0 7.7 7.7 0 0 7.7 0 0 0 0 0 0 0 7.7 7.7 7.7 7.7 0 7.7 7.7 0 0 0 7.7 7.7 0 0 7.7 0 7.7 7.7 0 7.7 0 7.7 7.7 7.7 0 0 0 7.7 7.7 7.7 0 0 0 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1 0 0 0 0 0 1 0 0 1 0 0 0 0 2 1 0 0 1 0 0 0 0 0 0 15 + 18 30490 True 34638;34639 2003723;2003724;2003725;2003726;2003727;2003728;2003729;2003730;2003731;2003732;2003733;2003734;2003735;2003736;2003737;2003738;2003739;2003740;2003741;2003742;2003743;2003744;2003745;2003746;2003747;2003748;2003749;2003750;2003751;2003752;2003753;2003754;2003755;2003756;2003757;2003758;2003759;2003760;2003761;2003762;2003763;2003764;2003765;2003766;2003767;2003768;2003769;2003770;2003771;2003772;2003773;2003774;2003775;2003776;2003777;2003778;2003779;2003780;2003781;2003782;2003783;2003784;2003785;2003786;2003787;2003788;2003789;2003790;2003791;2003792;2003793;2003794;2003795;2003796;2003797;2003798;2003799;2003800;2003801;2003802;2003803 1859807;1859808;1859809;1859810;1859811;1859812;1859813;1859814;1859815;1859816;1859817;1859818;1859819;1859820;1859821;1859822;1859823;1859824;1859825;1859826 1859818 3 89 -1 +4 4 2 IGHV5-51 HV551_HUMAN Immunoglobulin heavy variable 5-51 OS=Homo sapiens GN=IGHV5-51 PE=3 SV=1 1 4 4 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 1 2 1 0 0 0 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 1 0 0 0 0 3 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 2 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 1 2 1 0 0 0 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 1 0 0 0 0 3 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 2 1 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 47 47 26.5 12.674 117 117 0.0010504 6.4794 By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS 0 0 0 0 0 0 13.7 0 0 0 0 0 0 0 0 0 0 0 0 33.3 0 12.8 0 12.8 22.2 12.8 0 0 0 33.3 12.8 12.8 0 0 0 0 0 0 0 0 0 0 0 0 0 0 33.3 0 11.1 0 13.7 0 0 0 0 33.3 12.8 0 0 0 0 0 0 12.8 12.8 0 12.8 0 0 0 0 0 0 0 0 0 12.8 22.2 11.1 12.8 12.8 0 0 0 0 12.8 0 0 0 0 0 0 0 0 12.8 12.8 0 0 0 0 0 0 13.7 0 0 0 0 0 0 0 9.4 0 0 0 0 0 0 0 0 0 0 0 0 0 26.5 0 0 12.8 12.8 0 0 0 0 13.7 0 0 0 11.1 0 11.1 0 0 0 358030000 0 0 0 0 0 0 4931200 0 0 0 0 0 0 0 0 0 0 0 0 33568000 0 8416000 0 573060 14921000 0 0 0 0 28681000 6081100 5336100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22264000 0 9129400 0 3639300 0 0 0 0 57423000 0 0 0 0 0 0 0 8034300 2896600 0 14957000 0 0 0 0 0 0 0 0 0 3535500 16624000 4759300 8027600 0 0 0 0 0 5744800 0 0 0 0 0 0 0 0 6597800 3638200 0 0 0 0 0 0 8328600 0 0 0 0 0 0 0 5112100 0 0 0 0 0 0 0 0 0 0 0 0 0 16092000 0 0 6817800 29558000 0 0 0 0 0 0 0 0 8377800 0 13968000 0 0 0 5 71607000 0 0 0 0 0 0 986240 0 0 0 0 0 0 0 0 0 0 0 0 6713600 0 1683200 0 114610 2984300 0 0 0 0 5736200 1216200 1067200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4452700 0 1825900 0 727850 0 0 0 0 11485000 0 0 0 0 0 0 0 1606900 579320 0 2991500 0 0 0 0 0 0 0 0 0 707100 3324800 951860 1605500 0 0 0 0 0 1149000 0 0 0 0 0 0 0 0 1319600 727640 0 0 0 0 0 0 1665700 0 0 0 0 0 0 0 1022400 0 0 0 0 0 0 0 0 0 0 0 0 0 3218400 0 0 1363600 5911600 0 0 0 0 0 0 0 0 1675600 0 2793600 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 31907000 0 0 0 0 24753000 0 0 0 0 24896000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 38779000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 30882000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 4 0 0 1 1 0 0 0 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 3 1 0 0 0 0 0 0 1 0 0 2 0 0 0 0 0 0 0 0 0 0 2 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 4 0 0 0 0 1 0 0 0 0 0 1 0 0 0 35 19 2338;10132;24657;32123 True;True;True;True 2643;2644;11350;28000;36454 158864;158865;158866;158867;158868;158869;158870;652565;652566;652567;652568;652569;1611516;1611517;1611518;1611519;1611520;1611521;1611522;1611523;2122617;2122618;2122619;2122620;2122621;2122622;2122623;2122624;2122625;2122626;2122627;2122628;2122629;2122630;2122631;2122632;2122633;2122634;2122635;2122636;2122637;2122638;2122639;2122640;2122641;2122642;2122643 144904;144905;144906;144907;599840;599841;599842;1482719;1482720;1482721;1482722;1973008;1973009;1973010;1973011;1973012;1973013;1973014;1973015;1973016;1973017;1973018;1973019;1973020;1973021;1973022;1973023;1973024;1973025;1973026;1973027;1973028;1973029;1973030;1973031;1973032;1973033;1973034;1973035 144904;599841;1482720;1973033 4 112 -1 +3 2 2 IGKV2-24 KV224_HUMAN Immunoglobulin kappa variable 2-24 OS=Homo sapiens GN=IGKV2-24 PE=3 SV=1 1 3 2 2 1 2 2 1 1 1 2 1 0 0 1 1 1 1 1 0 0 0 1 2 1 1 0 0 2 2 2 1 1 2 2 0 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 3 2 2 0 2 1 0 2 2 2 1 1 0 0 0 2 1 0 1 1 0 1 0 0 0 0 1 1 1 1 0 2 2 2 2 1 2 1 0 2 2 0 1 0 0 0 2 1 2 0 0 1 0 2 1 1 2 1 1 1 1 2 2 1 0 2 2 2 0 1 2 2 1 2 1 2 2 2 2 2 1 2 2 2 1 1 1 1 1 2 2 1 2 1 1 0 1 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 2 1 1 0 1 1 0 2 1 2 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 1 0 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 0 1 0 0 0 1 0 1 0 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 1 0 1 1 2 1 1 0 1 1 0 2 1 2 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 0 1 0 0 1 1 0 1 0 0 0 1 1 1 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 38.3 32.5 32.5 13.079 120 120 0 18.278 By matching By matching By MS/MS By matching By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By matching By MS/MS By matching By MS/MS By matching By MS/MS By matching By matching By matching By MS/MS By matching By matching By MS/MS By matching By matching By matching By matching By matching By matching By matching By matching By MS/MS By matching By matching By MS/MS By MS/MS By matching By matching By MS/MS By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS By matching By matching 5.8 16.7 16.7 10.8 5.8 5.8 16.7 5.8 0 0 10.8 5.8 10.8 5.8 5.8 0 0 0 10.8 16.7 10.8 10.8 0 0 16.7 16.7 16.7 5.8 5.8 16.7 16.7 0 16.7 16.7 16.7 16.7 16.7 16.7 16.7 5.8 10.8 16.7 16.7 16.7 16.7 5.8 16.7 16.7 38.3 16.7 16.7 0 16.7 10.8 0 32.5 16.7 32.5 5.8 10.8 0 0 0 16.7 10.8 0 5.8 10.8 0 5.8 0 0 0 0 5.8 5.8 5.8 10.8 0 16.7 16.7 16.7 16.7 5.8 16.7 5.8 0 16.7 16.7 0 10.8 0 0 0 16.7 10.8 16.7 0 0 10.8 0 16.7 5.8 5.8 16.7 5.8 5.8 5.8 10.8 16.7 16.7 5.8 0 16.7 16.7 16.7 0 5.8 16.7 16.7 10.8 16.7 5.8 16.7 16.7 16.7 16.7 16.7 10.8 16.7 16.7 16.7 10.8 5.8 10.8 10.8 10.8 16.7 16.7 10.8 16.7 5.8 10.8 1465800000 0 2494700 14423000 12149000 0 0 21507000 0 0 0 18778000 0 6016100 0 0 0 0 0 7679600 15359000 3816400 13533000 0 0 16178000 19351000 2692800 0 0 15760000 4199700 0 9121700 12183000 3051000 22268000 819440 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0 1 0 0 73 20 8744;8751;25609 True;False;True 9820;9827;29084 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497181;497182;497183;497184;497185;497186;497187;497188;497189;497190;497191;497192;497193;497194;497195;497196;497197;497198;497199;497200;497201;497202;497203;497204;497205;497206;497207;497208;497209;497210;497211;497212;497213;497214;497215;497216;497217;497218;497219;497220;497221;497222;497223;497224;497225;497226;497227;497228;497229;497230;497231;497232;497233;497234;497235;497236;497237;497238;497239;497240;497241;497242;497243;497244;497245;497246;497247;497248;497249;497250;497932;497933;497934;497935;497936;497937;497938;497939;497940;497941;497942;497943;497944;497945;497946;497947;497948;497949;497950;497951;497952;497953;497954;497955;497956;497957;497958;497959;497960;497961;497962;497963;497964;1532169;1532170;1532171 497208;497934;1532170 -1 +2 2 2 HV64D_HUMAN Immunoglobulin heavy variable 3-64D OS=Homo sapiens GN=IGHV3-64D PE=3 SV=1 1 2 2 2 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9.4 0 0 9.4 0 0 0 0 9.4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9.4 0 0 0 0 0 0 9.4 0 0 0 0 0 0 0 0 0 0 0 0 0 9.4 0 9.4 0 0 0 0 0 0 9.4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9.4 0 9.4 0 9.4 0 0 0 9.4 0 0 9.4 0 9.4 9.4 9.4 0 0 0 0 0 9.4 9.4 9.4 0 0 0 0 0 0 161640000 246150 0 3452400 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11006000 0 0 4091500 0 0 0 0 3157300 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18829000 0 0 0 0 0 0 13420000 0 0 0 0 0 0 0 0 0 0 0 0 0 3474300 0 2918300 0 0 0 0 0 0 944670 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4348000 0 12768000 0 7000100 0 0 0 9089100 0 0 15651000 0 8500400 8990600 13280000 0 0 0 0 0 5603000 11129000 3736100 0 0 0 0 0 0 6 26939000 41025 0 575390 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1834400 0 0 681920 0 0 0 0 526220 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3138200 0 0 0 0 0 0 2236700 0 0 0 0 0 0 0 0 0 0 0 0 0 579050 0 486380 0 0 0 0 0 0 157440 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 724660 0 2128000 0 1166700 0 0 0 1514900 0 0 2608500 0 1416700 1498400 2213300 0 0 0 0 0 933830 1854800 622690 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3736100 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9 21 575;21300 True;True 645;24296 40701;40702;40703;40704;1425031;1425032;1425033;1425034;1425035;1425036;1425037;1425038;1425039;1425040;1425041;1425042;1425043;1425044;1425045;1425046;1425047 37668;1316971;1316972;1316973;1316974;1316975;1316976;1316977;1316978;1316979;1316980 37668;1316971 -1 +3 1 1 HV5X1_HUMAN Immunoglobulin heavy variable 5-10-1 OS=Homo sapiens GN=IGHV5-10-1 PE=3 SV=1 1 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 1 0 0 2 0 0 0 0 3 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 2 0 1 0 0 0 0 0 0 3 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 2 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 26.5 6 6 12.772 117 117 1 -2 By MS/MS By MS/MS By matching By matching By matching By MS/MS By matching By matching By MS/MS By matching By matching By MS/MS By matching By matching By matching By MS/MS By MS/MS By matching By MS/MS By MS/MS By matching By MS/MS By matching By MS/MS By MS/MS By matching By matching 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 26.5 0 6 0 0 15.4 0 0 0 0 26.5 6 0 0 6 0 6 0 0 0 0 0 6 6 0 0 0 20.5 0 11.1 0 0 0 0 0 0 26.5 6 0 0 0 6 6 6 6 0 0 0 0 0 0 0 0 0 0 0 0 0 9.4 17.1 0 0 0 0 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 0 0 15.4 0 0 6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 6 0 6 0 0 0 0 0 0 11.1 0 11.1 0 0 0 170090000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10223000 0 7042300 0 0 8712700 0 0 0 0 6014400 2668500 0 0 7883700 0 4226800 0 0 0 0 0 7191600 8898200 0 0 0 0 0 0 0 0 0 0 0 0 15915000 9931400 0 0 0 1347400 1266400 2773200 7348000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 7510300 0 0 0 0 0 0 6800200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3135400 0 0 0 15909000 0 0 2232200 0 0 0 0 0 0 0 0 0 0 0 0 0 0 22002000 0 11062000 0 0 0 0 0 0 0 0 0 0 0 0 5 34019000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2044500 0 1408500 0 0 1742500 0 0 0 0 1202900 533700 0 0 1576700 0 845360 0 0 0 0 0 1438300 1779600 0 0 0 0 0 0 0 0 0 0 0 0 3183000 1986300 0 0 0 269470 253270 554650 1469600 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1502100 0 0 0 0 0 0 1360000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 627080 0 0 0 3181700 0 0 446450 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4400300 0 2212400 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11062000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 12 + 22 2338;10133;24657 False;True;False 2643;2644;11351;11352;28000 158864;158865;158866;158867;158868;158869;158870;652570;652571;652572;652573;652574;652575;652576;652577;652578;652579;652580;652581;652582;652583;652584;652585;652586;652587;652588;652589;652590;652591;1611516;1611517;1611518;1611519;1611520;1611521;1611522;1611523 144904;144905;144906;144907;599843;599844;599845;599846;599847;599848;599849;599850;599851;599852;599853;599854;1482719;1482720;1482721;1482722 144904;599849;1482720 4 112 -1 +4;2;2;2 4;2;2;2 4;2;2;2 Ubiquitin-like modifier-activating enzyme 6 UBA6 UBA6_HUMAN Ubiquitin-like modifier-activating enzyme 6 OS=Homo sapiens GN=UBA6 PE=1 SV=1;UBA6_HUMAN Isoform 4 of Ubiquitin-like modifier-activating enzyme 6 OS=Homo sapiens GN=UBA6;UBA6_HUMAN Isoform 3 of Ubiquitin-like modifier-activating enzyme 6 OS=Homo 4 4 4 4 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 2 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 2 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 2 0 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 4.8 4.8 4.8 117.97 1052 1052;340;389;578 0 8.6245 By matching By matching By matching By MS/MS By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By MS/MS By matching By matching By matching By MS/MS By MS/MS By MS/MS 0 0 0 0 0 0 1.2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 0 0 0 2.9 0 2.9 1.2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 163510000 0 0 0 0 0 0 20730000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3424400 0 0 0 0 0 0 0 0 0 0 0 0 0 3645100 6988500 0 1003200 706390 991980 0 0 0 0 0 9478700 0 5893100 1770800 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 15817000 0 0 0 0 0 0 8449000 0 0 28690000 0 3384600 0 0 0 0 0 2794000 0 0 0 0 0 10034000 12634000 7570000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 19503000 56 2919800 0 0 0 0 0 0 370170 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 61149 0 0 0 0 0 0 0 0 0 0 0 0 0 65092 124790 0 17914 12614 17714 0 0 0 0 0 169260 0 105230 31621 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 282450 0 0 0 0 0 0 150880 0 0 512330 0 60439 0 0 0 0 0 49893 0 0 0 0 0 179190 225610 135180 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 348260 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9453100 0 5918700 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 6 23 5685;7993;16347;32226 True;True;True;True 6390;8981;18316;36572 362727;362728;362729;362730;491867;491868;1087250;1087251;1087252;1087253;1087254;2131833;2131834;2131835;2131836;2131837;2131838;2131839;2131840;2131841;2131842 328903;439972;1003166;1982591;1982592;1982593 328903;439972;1003166;1982592 -1;-1;-1;-1 +8;8;8;8;7;7;7 8;8;8;8;7;7;7 8;8;8;8;7;7;7 Shootin-1 KIAA1598 SHOT1_HUMAN Isoform 2 of Shootin-1 OS=Homo sapiens GN=SHTN1;SHOT1_HUMAN Isoform 4 of Shootin-1 OS=Homo sapiens GN=SHTN1;SHOT1_HUMAN Shootin-1 OS=Homo sapiens GN=SHTN1 PE=1 SV=4;SHOT1_HUMAN Isoform 3 of Shootin-1 OS=Homo sapiens GN=SHTN1;SHOT1_HUMAN Isoform 7 8 8 8 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 1 1 4 0 1 0 0 1 1 0 0 0 0 1 1 2 0 0 0 1 2 3 0 1 0 0 1 0 2 3 1 2 2 1 1 1 0 1 1 0 0 0 1 1 2 2 3 0 1 2 2 2 0 3 0 0 0 3 2 3 0 5 0 0 0 0 1 0 2 1 3 1 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 1 0 0 1 0 0 1 0 2 1 2 0 0 0 0 2 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 1 1 4 0 1 0 0 1 1 0 0 0 0 1 1 2 0 0 0 1 2 3 0 1 0 0 1 0 2 3 1 2 2 1 1 1 0 1 1 0 0 0 1 1 2 2 3 0 1 2 2 2 0 3 0 0 0 3 2 3 0 5 0 0 0 0 1 0 2 1 3 1 1 0 0 0 0 0 0 0 0 0 2 0 0 0 0 1 1 0 0 1 0 0 1 0 2 1 2 0 0 0 0 2 0 1 0 0 0 0 0 0 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0 2 0 2.2 0 0 0 0 0 2.2 0 0 0 0 0 0 0 0 0 0 0 2 2.4 0 0 3.3 0 0 3.3 3.3 3.3 9.4 0 2 0 0 2 3.3 0 0 0 0 2.2 3.3 5.3 0 0 0 3.3 5.3 7.5 0 2 0 0 1.8 0 3.9 6.6 1.8 3.9 3.9 1.8 2.2 2.2 0 3.3 2 0 0 0 2.2 2 5.3 5.3 7.2 0 2.2 4.6 5.7 4.6 0 6.4 0 0 0 6.6 4.6 6.8 0 11 0 0 0 0 2.2 0 5.5 3.3 6.8 3.3 3.3 0 0 0 0 0 0 0 0 0 5.5 0 0 0 0 2.2 2.2 0 0 2.2 0 0 3.3 0 5.5 3.3 5.5 0 0 0 0 4.2 0 620900000 790690 0 0 0 0 0 0 0 0 0 3242300 0 7179200 0 0 0 0 0 7623200 0 0 0 0 0 0 0 0 0 0 0 3970500 9286000 0 0 20384000 0 0 5156000 6547900 7782900 23743000 0 4993000 0 0 1727500 5699400 0 0 0 0 45495000 13396000 6398700 0 0 0 7059400 10902000 45574000 0 1280500 0 0 2477900 0 13836000 10777000 20341000 3915400 6049600 1798500 1516300 2197200 0 4152500 5796900 0 0 0 2311500 2009700 21153000 9991000 10441000 0 4075200 4762700 2004300 19218000 0 8572900 0 0 0 9037800 5174300 9604600 0 64960000 0 0 0 0 742820 0 13255000 6266800 28494000 4892400 14100000 0 0 0 0 0 0 0 0 0 21129000 0 0 0 0 7792800 3083200 0 0 5029200 0 0 3676000 0 10508000 0 10255000 0 0 0 0 7272500 0 26 23881000 30411 0 0 0 0 0 0 0 0 0 124700 0 276120 0 0 0 0 0 293200 0 0 0 0 0 0 0 0 0 0 0 152710 357160 0 0 784000 0 0 198310 251840 299340 913190 0 192040 0 0 66440 219210 0 0 0 0 1749800 515250 246100 0 0 0 271510 419320 1752900 0 49249 0 0 95304 0 532140 414490 782360 150590 232680 69173 58319 84506 0 159710 222960 0 0 0 88906 77295 813580 384270 401590 0 156740 183180 77088 739140 0 329730 0 0 0 347610 199010 369410 0 2498500 0 0 0 0 28570 0 509820 241030 1095900 188170 542310 0 0 0 0 0 0 0 0 0 812640 0 0 0 0 299720 118580 0 0 193430 0 0 141380 0 404140 0 394430 0 0 0 0 279710 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18390000 0 0 0 0 0 0 0 0 0 0 0 0 10482000 0 0 0 0 14239000 52178000 0 0 0 0 0 0 0 10591000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 20035000 13558000 0 0 0 12942000 0 26710000 0 0 0 0 0 0 0 0 0 28059000 0 0 0 0 0 0 0 0 39809000 0 0 0 0 0 0 0 0 0 0 0 20109000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 2 0 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 2 0 24 24 2646;4820;11973;13150;18771;18911;22509;27887 True;True;True;True;True;True;True;True 2987;5435;13437;14737;21021;21173;25626;31692 183202;183203;183204;183205;183206;183207;183208;183209;183210;183211;183212;183213;183214;183215;183216;183217;183218;183219;183220;183221;183222;183223;183224;183225;183226;310825;310826;310827;310828;310829;310830;310831;310832;310833;310834;310835;310836;310837;773924;773925;773926;773927;773928;773929;773930;773931;773932;857333;857334;857335;857336;857337;857338;857339;857340;857341;857342;857343;857344;857345;1243627;1243628;1243629;1243630;1243631;1243632;1243633;1243634;1243635;1243636;1243637;1243638;1243639;1243640;1243641;1243642;1252839;1252840;1252841;1252842;1252843;1252844;1252845;1252846;1252847;1252848;1252849;1488170;1816897;1816898;1816899;1816900;1816901;1816902;1816903;1816904;1816905;1816906;1816907;1816908 165985;165986;165987;165988;165989;165990;165991;165992;165993;165994;282261;713362;713363;713364;792134;792135;792136;792137;792138;792139;1149676;1149677;1149678;1149679;1149680;1157733;1157734;1157735;1157736;1157737;1375695;1678743;1678744;1678745;1678746;1678747;1678748;1678749 165990;282261;713362;792139;1149677;1157733;1375695;1678746 -1;-1;-1;-1;-1;-1;-1 +2;2;1;1;1 2;2;1;1;1 2;2;1;1;1 Protein HEATR9 HEATR9 HEAT9_HUMAN Isoform 4 of Protein HEATR9 OS=Homo sapiens GN=HEATR9;HEAT9_HUMAN Protein HEATR9 OS=Homo sapiens GN=HEATR9 PE=1 SV=2;HEAT9_HUMAN Isoform 2 of Protein HEATR9 OS=Homo sapiens GN=HEATR9;HEAT9_HUMAN Isoform 5 of Protein HEATR9 OS=Homo sapiens GN=HE 5 2 2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 2 0 0 2 1 1 2 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 2 0 0 2 1 1 2 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 1 1 0 0 0 0 1 0 0 0 0 2 0 0 2 1 1 2 2 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 6.6 6.6 6.6 44.086 380 380;570;219;340;530 1 -2 By matching By MS/MS By MS/MS By MS/MS By matching By matching By matching By matching By matching By MS/MS By MS/MS By matching By matching By matching By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By MS/MS By matching By matching By MS/MS 0 0 0 0 0 0 0 0 0 0 0 0 0 2.6 0 0 0 0 0 2.6 0 0 0 0 2.6 0 0 0 0 0 0 2.6 0 3.9 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3.9 0 3.9 0 3.9 3.9 0 0 0 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5.1 5.1 5.1 0 5.1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5.1 0 76949000 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 9125800 0 5927800 8546500 0 0 0 0 0 0 0 0 7014300 0 0 0 6480200 4045700 4467200 10772000 5066400 0 4329700 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 11174000 0 15 5129900 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 608390 0 395180 569770 0 0 0 0 0 0 0 0 467620 0 0 0 432020 269710 297810 718110 337760 0 288650 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 744900 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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--git a/ui/runs/forms/importing.py b/ui/runs/forms/importing.py index ec0a93fe..cc799be3 100644 --- a/ui/runs/forms/importing.py +++ b/ui/runs/forms/importing.py @@ -34,6 +34,12 @@ class EmptyEnum(Enum): pass +class AggregationMethods(Enum): + sum = "Sum" + median = "Median" + mean = "Mean" + + class MaxQuantImportForm(MethodForm): file_path = CustomFileField(label="MaxQuant intensities file") intensity_name = CustomChoiceField( @@ -42,6 +48,9 @@ class MaxQuantImportForm(MethodForm): map_to_uniprot = CustomBooleanField( label="Map to Uniprot IDs using Biomart (online)", required=False ) + aggregation_method = CustomChoiceField( + choices=AggregationMethods, label="Aggregation method", initial="Sum" + ) class DiannImportForm(MethodForm): @@ -49,6 +58,9 @@ class DiannImportForm(MethodForm): map_to_uniprot = CustomBooleanField( label="Map to Uniprot IDs using Biomart (online)", required=False ) + aggregation_method = CustomChoiceField( + choices=AggregationMethods, label="Aggregation method", initial="Sum" + ) class MSFraggerImportForm(MethodForm): @@ -59,6 +71,9 @@ class MSFraggerImportForm(MethodForm): map_to_uniprot = CustomBooleanField( label="Map to Uniprot IDs using Biomart (online)", required=False ) + aggregation_method = CustomChoiceField( + choices=AggregationMethods, label="Aggregation method", initial="Sum" + ) class MetadataImportForm(MethodForm): diff --git a/user_data/workflows/only_import.yaml b/user_data/workflows/only_import.yaml index b3ec0ce1..0ee5fb53 100644 --- a/user_data/workflows/only_import.yaml +++ b/user_data/workflows/only_import.yaml @@ -2,6 +2,7 @@ steps: - form_inputs: intensity_name: iBAQ map_to_uniprot: false + aggregation_mode: Sum inputs: { } type: MaxQuantImport - form_inputs: diff --git a/user_data/workflows/only_import_and_filter_proteins.yaml b/user_data/workflows/only_import_and_filter_proteins.yaml index cfaa9a3a..b77b4935 100644 --- a/user_data/workflows/only_import_and_filter_proteins.yaml +++ b/user_data/workflows/only_import_and_filter_proteins.yaml @@ -2,6 +2,7 @@ steps: - form_inputs: intensity_name: iBAQ map_to_uniprot: false + aggregation_method: Sum inputs: { } type: MaxQuantImport - form_inputs: diff --git a/user_data/workflows/standard.yaml b/user_data/workflows/standard.yaml index d1608481..0b93bd80 100644 --- a/user_data/workflows/standard.yaml +++ b/user_data/workflows/standard.yaml @@ -2,6 +2,7 @@ steps: - form_inputs: intensity_name: iBAQ map_to_uniprot: false + aggregation_mode: Sum inputs: { } type: MaxQuantImport - form_inputs: From 08564845501a5cc1bb7c43db54f95ccb8a2a7387 Mon Sep 17 00:00:00 2001 From: JanniRoebbecke <50395935+JanniRoebbecke@users.noreply.github.com> Date: Mon, 24 Jun 2024 21:30:01 +0200 Subject: [PATCH 10/22] PTM basic statistics amount (#508) * evidence import * tests * filter proteins * filter sampels * Select Peptides by group or sample for coverage visualisation (#410) * untested working, needs cleanup * linter * remove dupliacte method (merge screw-up) * add doc strings, fix first few tests (miss. args) * metadata df now optional param + adjusted asserts, fixed test * formatting * conditional metadata input for protein graphs * import logging levels, formatting * add grouping tests for peptides, add error handling * spelling, formatting * remove unused import, format * fix error msgs in tests * fix line length * Convert has_metadata to property * Use logger instead of print statement * An attempt at consistent text-casing * formatting --------- Co-authored-by: henninggaertner * tests for filter_proteins with peptide file * tests for filter_proteins with peptide file, better mock dataframe * output types * added testing that only filtered Proteins are removed from peptides_df * tests for filter_samples with peptide file * extract test that peptide filtering matches protein filtering into Method * refactor: shortened tests by integrating extracted method and mock data * exclude peptides * tests * minor fixes in test * minor fixes in test * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * match mock protein dataframe to mock peptide dataframe * extract test that peptide filtering matches protein filtering into Method * implementation * tests * tests improve error logging * tests git testing * peptide import added missing column * implements form, form_mapping, method and the actual functionality (untested) * merge peptide filtering and outlier_detection * merge peptide transformation * implement passing of peptide data between preprocessing steps * complete implement passing of peptide data between preprocessing steps * implement overhead for new step evidence import * enable selecting multiple proteins of interest and implements tests * Fix EvidenceImport class (now the right method is called) * implement option to choose protein from list of significant proteins * implement option to let the system choose the most expressed protein * include messages for peptide filtering * remove selecting multiple proteins and add substring search * add cleaning of protein groups to evidence import * implement suggested changes: remove default values and correct grammar mistake * make preprocessing work without peptide_df * clean up * add cleaning of protein groups to evidence import * rename * clean Protein IDs for peptide_import and name intensity column Intensity * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used * unify into one method * cleanup old names * cleanup names * cleanup names * setup form and method * small refactor: utilize fill_helper better * implemented method * cleanup rename and docstring * tests and named sample column from "0" to "Sample" * implemented slow version * enable selecting multiple proteins * complete merge * rename file and fix selecting multiple proteins * improve efficiency of methods ptms_per_sample and ptms_per_protein_and_sample * added test for ptms_per_protein_and_sample * complete merge (fix testes after merge) * cleanup * implements suggested changes --------- Co-authored-by: janni.roebbecke --- protzilla/data_analysis/ptm_analysis.py | 98 ++++++++++++++++++- protzilla/methods/data_analysis.py | 49 +++++++++- protzilla/utilities/transform_dfs.py | 4 +- tests/conftest.py | 43 ++++++++ .../data_analysis/test_peptide_analysis.py | 44 +++++++++ ui/runs/form_mapping.py | 2 + ui/runs/forms/data_analysis.py | 68 ++++++++++--- 7 files changed, 291 insertions(+), 17 deletions(-) create mode 100644 tests/protzilla/data_analysis/test_peptide_analysis.py diff --git a/protzilla/data_analysis/ptm_analysis.py b/protzilla/data_analysis/ptm_analysis.py index e2b3d565..7917699d 100644 --- a/protzilla/data_analysis/ptm_analysis.py +++ b/protzilla/data_analysis/ptm_analysis.py @@ -1,6 +1,11 @@ import logging +from math import log +import numpy as np import pandas as pd +import re + +from protzilla.utilities.transform_dfs import long_to_wide def filter_peptides_of_protein( @@ -27,4 +32,95 @@ def filter_peptides_of_protein( "level": logging.INFO if len(filtered_peptides) > 0 else logging.WARNING, "msg": f"Selected {len(filtered_peptides)} entry's from the peptide dataframe." }], - ) \ No newline at end of file + ) + + +def ptms_per_sample(peptide_df: pd.DataFrame) -> dict: + """ + This function calculates the amount of every PTMs per sample. + + :param peptide_df: the pandas dataframe containing the peptide information + + :return: dict containing a dataframe one row per sample and one column per PTM that occurs in the peptide_df, + with the cells containing the amount of the PTM in the sample + """ + + modification_df = peptide_df[["Sample", "Modifications"]] + + modification_df = pd.concat( + [modification_df["Sample"], + (modification_df['Modifications'].str.get_dummies(sep=","))], axis=1) + + for column, data in modification_df.iteritems(): + amount, name = from_string(column) + if amount > 1: + modification_df[column] = modification_df[column].multiply(amount) + modification_df = modification_df.rename(columns={column: name}) + + modification_df = modification_df.groupby(["Sample"]).sum() + + modification_df = modification_df.groupby(modification_df.columns, axis=1).sum() + + modification_df = modification_df.reset_index() + + return dict(ptm_df=modification_df) + + +def ptms_per_protein_and_sample(peptide_df: pd.DataFrame) -> dict: + """ + This function calculates the amount of every PTM per sample and protein. + + :param peptide_df: the pandas dataframe containing the peptide information + + :return: dict containing a dataframe one row per sample and one column per protein, + with the cells containing a list of PTMs that occur in the peptide_df for the protein and sample and + their amount in the protein and sample + """ + + modification_df = peptide_df[["Sample", "Protein ID", "Modifications"]] + + modification_df = modification_df[["Sample", "Protein ID"]].join( + modification_df['Modifications'].str.get_dummies(sep=",") + ) + + for column, data in modification_df.iteritems(): + amount, name = from_string(column) + if amount > 1: + modification_df[column] = modification_df[column].multiply(amount) + modification_df = modification_df.rename(columns={column: name}) + + modification_df = modification_df.groupby(["Sample", "Protein ID"]).sum() + + modification_df = modification_df.groupby(modification_df.columns, axis=1).sum() + + modification_df = modification_df.reset_index() + + modi = ( + modification_df.drop(["Sample", "Protein ID"], axis=1).apply(lambda x: ('(' + x.astype(str) + ') ' + x.name + ", "))) + + for column, data in modi.iteritems(): + modi[column] = np.where(modification_df[column] > 0, modi[column], "") + + modification_df["Modifications"] = modi.apply(''.join, axis=1) + modification_df = modification_df[['Sample', 'Protein ID', 'Modifications']] + + modification_df = long_to_wide(modification_df, "Modifications").fillna("").reset_index() + + return dict(ptm_df=modification_df) + + +def from_string(mod_string: str) -> tuple[int, str]: + """ + This function extracts the amount and name of a modification from its listing in the evidence file. + + :param mod_string: a string containing the amount and name of the modification + + :return: tuple containing the amount and name of the modification + """ + + re_search = re.search(r'\d+', mod_string) + amount = int(re_search.group()) if re_search else 1 + name = re.search(r'\D+', mod_string).group() + name = name[1:] if name[0] == " " else name + + return amount, name diff --git a/protzilla/methods/data_analysis.py b/protzilla/methods/data_analysis.py index 79982ea2..95b71468 100644 --- a/protzilla/methods/data_analysis.py +++ b/protzilla/methods/data_analysis.py @@ -10,7 +10,8 @@ from protzilla.data_analysis.differential_expression_linear_model import linear_model from protzilla.data_analysis.differential_expression_t_test import t_test from protzilla.data_analysis.dimension_reduction import t_sne, umap -from protzilla.data_analysis.ptm_analysis import filter_peptides_of_protein +from protzilla.data_analysis.ptm_analysis import filter_peptides_of_protein, ptms_per_sample, \ + ptms_per_protein_and_sample from protzilla.data_analysis.model_evaluation import evaluate_classification_model from protzilla.data_analysis.plots import ( clustergram_plot, @@ -641,3 +642,49 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: }) return inputs + + +class PTMsPerSample(DataAnalysisStep): + display_name = "PTMs per Sample" + operation = "Peptide analysis" + method_description = ("Analyze the post-translational modifications (PTMs) of a single protein of interest. " + "This function requires a peptide dataframe with PTM information.") + + input_keys = [ + "peptide_df", + ] + output_keys = [ + "ptm_df", + ] + + def method(self, inputs: dict) -> dict: + return ptms_per_sample(**inputs) + + def insert_dataframes(self, steps: StepManager, inputs) -> dict: + inputs["peptide_df"] = steps.get_step_output( + Step, "peptide_df", inputs["peptide_df"] + ) + return inputs + + +class PTMsProteinAndPerSample(DataAnalysisStep): + display_name = "PTMs per Sample and Protein" + operation = "Peptide analysis" + method_description = ("Analyze the post-translational modifications (PTMs) of all Proteins. " + "This function requires a peptide dataframe with PTM information.") + + input_keys = [ + "peptide_df", + ] + output_keys = [ + "ptm_df", + ] + + def method(self, inputs: dict) -> dict: + return ptms_per_protein_and_sample(**inputs) + + def insert_dataframes(self, steps: StepManager, inputs) -> dict: + inputs["peptide_df"] = steps.get_step_output( + Step, "peptide_df", inputs["peptide_df"] + ) + return inputs \ No newline at end of file diff --git a/protzilla/utilities/transform_dfs.py b/protzilla/utilities/transform_dfs.py index 55140b10..59a83259 100644 --- a/protzilla/utilities/transform_dfs.py +++ b/protzilla/utilities/transform_dfs.py @@ -3,7 +3,7 @@ from protzilla.utilities import default_intensity_column -def long_to_wide(intensity_df: pd.DataFrame): +def long_to_wide(intensity_df: pd.DataFrame, value_name: str | None = None): """ This function transforms the dataframe to a wide format that can be more easily handled by packages such as sklearn. @@ -17,7 +17,7 @@ def long_to_wide(intensity_df: pd.DataFrame): packages such as sklearn :rtype: pd.DataFrame """ - values_name = default_intensity_column(intensity_df) + values_name = default_intensity_column(intensity_df) if value_name is None else value_name return pd.pivot( intensity_df, index="Sample", columns="Protein ID", values=values_name ) diff --git a/tests/conftest.py b/tests/conftest.py index d94bc40f..ea8728b1 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -203,6 +203,49 @@ def peptides_df(): return df +@pytest.fixture +def evidence_peptide_df(): + df = pd.DataFrame( + ( + ["Sample1", "Protein1", "SEQA", 1000000, "Unmodified", "_SEQA_", 1, 0.00001, "Raw_File_1"], + ["Sample1", "Protein2", "SEQB", 2000000, "Unmodified", "_SEQB_", None, 0.00002, "Raw_File_1"], + ["Sample1", "Protein2", "SEQC", 3000000, "Acetyl (Protein N-term)", "_(Acetyl (Protein N-term))SEQC_", None, 0.00003, "Raw_File_1"], + ["Sample1", "Protein2", "SEQD", 4000000, "Acetyl (Protein N-term),Oxidation (M)", "_(Acetyl (Protein N-term))SE(Oxidation (M))QD_", None, 0.00004, "Raw_File_1"], + ["Sample1", "Protein3", "SEQE", 5000000, "Unmodified", "_SEQE_", None, 0.00005, "Raw_File_1"], + ["Sample1", "Protein3", "SEQF", 6000000, "Unmodified", "_SEQF_", None, 0.00006, "Raw_File_1"], + ["Sample1", "Protein3", "SEQG", 7000000, "Unmodified", "_SEQG_", None, 0.00007, "Raw_File_1"], + ["Sample1", "Protein4", "SEQH", 8000000, "Unmodified", "_SEQH_", None, 0.00008, "Raw_File_1"], + ["Sample1", "Protein5", "SEQI", 9000000, "Unmodified", "_SEQI_", None, 0.00009, "Raw_File_1"], + ["Sample2", "Protein1", "SEQJ", 10000000, "Acetyl (Protein N-term)", "_(Acetyl (Protein N-term))SEQJ_", None, 0.0001, "Raw_File_2"], + ["Sample2", "Protein2", "SEQK", 11000000, "Unmodified", "_SEQK_", None, 0.00011, "Raw_File_2"], + ["Sample2", "Protein3", "SEQL", 12000000, "Unmodified", "_SEQL_", None, 0.00012, "Raw_File_2"], + ["Sample2", "Protein4", "SEQM", 13000000, "Unmodified", "_SEQM_", None, 0.00013, "Raw_File_2"], + ["Sample2", "Protein5", "SEQN", 14000000, "Unmodified", "_SEQN_", None, 0.00014, "Raw_File_2"], + ["Sample3", "Protein1", "SEQO", 15000000, "Unmodified", "_SEQO_", None, 0.00015, "Raw_File_3"], + ["Sample3", "Protein2", "SEQP", 16000000, "Unmodified", "_SEQP_", None, 0.00016, "Raw_File_3"], + ["Sample3", "Protein3", "SEQQ", 17000000, "Unmodified", "_SEQQ_", None, 0.00017, "Raw_File_3"], + ["Sample3", "Protein4", "SEQR", 18000000, "Unmodified", "_SEQR_", None, 0.00018, "Raw_File_3"], + ["Sample3", "Protein5", "SEQS", 19000000, "Unmodified", "_SEQS_", None, 0.00019, "Raw_File_3"], + ["Sample4", "Protein1", "SEQT", 20000000, "Unmodified", "_SEQT_", None, 0.0002, "Raw_File_4"], + ["Sample4", "Protein2", "SEQU", 21000000, "Unmodified", "_SEQU_", None, 0.00021, "Raw_File_4"], + ["Sample4", "Protein3", "SEQV", 22000000, "Unmodified", "_SEQV_", None, 0.00022, "Raw_File_4"], + ["Sample4", "Protein4", "SEQW", 23000000, "Unmodified", "_SEQW_", None, 0.00023, "Raw_File_4"], + ), + columns=[ + "Sample", + "Protein ID", + "Sequence", + "Intensity", + "Modifications", + "Modified sequence", + "Missed cleavages", + "PEP", + "Raw file", + ] + ) + return df + + class Helpers: @staticmethod def open_graph_from_base64(encoded_string): diff --git a/tests/protzilla/data_analysis/test_peptide_analysis.py b/tests/protzilla/data_analysis/test_peptide_analysis.py new file mode 100644 index 00000000..b2848a65 --- /dev/null +++ b/tests/protzilla/data_analysis/test_peptide_analysis.py @@ -0,0 +1,44 @@ +import pytest + +from protzilla.data_analysis.ptm_analysis import filter_peptides_of_protein, ptms_per_sample, \ + ptms_per_protein_and_sample + + +@pytest.mark.parametrize("df_num", [0, 1]) +def test_filter_peptides_of_protein(peptides_df, evidence_peptide_df, df_num): + peptide_df = [peptides_df, evidence_peptide_df][df_num] + + filtered_peptides_df = filter_peptides_of_protein(peptide_df, ["Protein2"])["peptide_df"] + + assert len(filtered_peptides_df) == 6 + assert filtered_peptides_df["Sequence"].tolist() == [ + 'SEQB', 'SEQC', 'SEQD', 'SEQK', 'SEQP', 'SEQU' + ] + assert (filtered_peptides_df["Protein ID"] == "Protein2").all() + + +def test_ptms_per_sampel(evidence_peptide_df): + ptm_df = ptms_per_sample(evidence_peptide_df)["ptm_df"] + + assert ptm_df.columns.tolist() == ["Sample", "Acetyl (Protein N-term)", "Oxidation (M)", "Unmodified"] + assert ptm_df["Sample"].tolist() == ["Sample1", "Sample2", "Sample3", "Sample4"] + assert ptm_df["Unmodified"].tolist() == [7, 4, 5, 4] + assert ptm_df["Acetyl (Protein N-term)"].tolist() == [2, 1, 0, 0] + assert ptm_df["Oxidation (M)"].tolist() == [1, 0, 0, 0] + + +def test_ptms_per_protein_and_sample(evidence_peptide_df): + ptm_df = ptms_per_protein_and_sample(evidence_peptide_df)["ptm_df"] + + assert ptm_df.columns.tolist() == ["Sample", "Protein1", "Protein2", "Protein3", "Protein4", "Protein5"] + assert ptm_df["Sample"].tolist() == ["Sample1", "Sample2", "Sample3", "Sample4"] + assert (ptm_df["Protein1"].tolist() == + ["(1) Unmodified, ", "(1) Acetyl (Protein N-term), ", "(1) Unmodified, ", "(1) Unmodified, "]) + assert (ptm_df["Protein2"].tolist() == + ["(2) Acetyl (Protein N-term), (1) Oxidation (M), (1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, "]) + assert (ptm_df["Protein3"].tolist() == + ["(3) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, "]) + assert (ptm_df["Protein4"].tolist() == + ["(1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, "]) + assert (ptm_df["Protein5"].tolist() == + ["(1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, ", ""]) \ No newline at end of file diff --git a/ui/runs/form_mapping.py b/ui/runs/form_mapping.py index ab27a219..600eb4db 100644 --- a/ui/runs/form_mapping.py +++ b/ui/runs/form_mapping.py @@ -60,6 +60,8 @@ data_analysis.ProteinGraphPeptidesToIsoform: data_analysis_forms.ProteinGraphPeptidesToIsoformForm, data_analysis.ProteinGraphVariationGraph: data_analysis_forms.ProteinGraphVariationGraphForm, data_analysis.SelectPeptidesForProtein: data_analysis_forms.SelectPeptidesForProteinForm, + data_analysis.PTMsPerSample: data_analysis_forms.PTMsPerSampleForm, + data_analysis.PTMsProteinAndPerSample: data_analysis_forms.PTMsPerProteinAndSampleForm, data_preprocessing.ImputationByMinPerSample: data_preprocessing_forms.ImputationByMinPerSampleForms, data_integration.EnrichmentAnalysisGOAnalysisWithString: data_integration_forms.EnrichmentAnalysisGOAnalysisWithStringForm, data_integration.EnrichmentAnalysisGOAnalysisWithEnrichr: data_integration_forms.EnrichmentAnalysisGOAnalysisWithEnrichrForm, diff --git a/ui/runs/forms/data_analysis.py b/ui/runs/forms/data_analysis.py index 09c6a73c..10777c0a 100644 --- a/ui/runs/forms/data_analysis.py +++ b/ui/runs/forms/data_analysis.py @@ -5,7 +5,7 @@ from protzilla.methods.data_analysis import ( DifferentialExpressionLinearModel, DifferentialExpressionTTest, - DimensionReductionUMAP, DataAnalysisStep, + DimensionReductionUMAP, DataAnalysisStep, SelectPeptidesForProtein, ) from protzilla.run import Run from protzilla.steps import Step @@ -17,7 +17,8 @@ CustomChoiceField, CustomFloatField, CustomMultipleChoiceField, - CustomNumberField, CustomBooleanField, + CustomNumberField, + CustomBooleanField, ) @@ -221,8 +222,8 @@ def fill_form(self, run: Run) -> None: # Set choices for group2 field based on selected grouping and group1 if ( - "group1" in self.data - and self.data["group1"] in run.steps.metadata_df[grouping].unique() + "group1" in self.data + and self.data["group1"] in run.steps.metadata_df[grouping].unique() ): self.fields["group2"].choices = [ (el, el) @@ -262,8 +263,8 @@ def fill_form(self, run: Run) -> None: # Set choices for group2 field based on selected grouping and group1 if ( - "group1" in self.data - and self.data["group1"] in run.steps.metadata_df[grouping].unique() + "group1" in self.data + and self.data["group1"] in run.steps.metadata_df[grouping].unique() ): self.fields["group2"].choices = [ (el, el) @@ -398,9 +399,9 @@ def fill_form(self, run: Run) -> None: initial=0, ) if ( - "similarity" not in self.data - or float(self.data["similarity"]) < -1 - or float(self.data["similarity"]) > 1 + "similarity" not in self.data + or float(self.data["similarity"]) < -1 + or float(self.data["similarity"]) > 1 ): self.data["similarity"] = 0 else: @@ -412,9 +413,9 @@ def fill_form(self, run: Run) -> None: initial=1, ) if ( - "similarity" not in self.data - or float(self.data["similarity"]) < 0 - or float(self.data["similarity"]) > 999 + "similarity" not in self.data + or float(self.data["similarity"]) < 0 + or float(self.data["similarity"]) > 999 ): self.data["similarity"] = 1 @@ -954,4 +955,45 @@ def fill_form(self, run: Run) -> None: ) else: self.toggle_visibility("sort_proteins", False) - self.toggle_visibility("protein_ids", False) \ No newline at end of file + self.toggle_visibility("protein_ids", False) + + +class PTMsPerSampleForm(MethodForm): + peptide_df = CustomChoiceField( + choices=[], + label="Peptide dataframe containing the peptides of a single protein", + ) + + def fill_form(self, run: Run) -> None: + single_protein_peptides = run.steps.get_instance_identifiers( + SelectPeptidesForProtein, "peptide_df" + ) + self.fields["peptide_df"].choices = fill_helper.to_choices(single_protein_peptides) + + self.fields["peptide_df"].choices = fill_helper.get_choices( + run, "peptide_df" + ) + + single_protein_peptides = run.steps.get_instance_identifiers( + SelectPeptidesForProtein, "peptide_df" + ) + if single_protein_peptides: + self.fields["peptide_df"].initial = single_protein_peptides[0] + + +class PTMsPerProteinAndSampleForm(MethodForm): + peptide_df = CustomChoiceField( + choices=[], + label="Peptide dataframe containing the peptides of a single protein", + ) + + def fill_form(self, run: Run) -> None: + self.fields["peptide_df"].choices = fill_helper.get_choices( + run, "peptide_df" + ) + + single_protein_peptides = run.steps.get_instance_identifiers( + SelectPeptidesForProtein, "peptide_df" + ) + if single_protein_peptides: + self.fields["peptide_df"].initial = single_protein_peptides[0] \ No newline at end of file From c695159a672d55cb69a11a509b05a20840c03508 Mon Sep 17 00:00:00 2001 From: JanniRoebbecke <50395935+JanniRoebbecke@users.noreply.github.com> Date: Tue, 25 Jun 2024 11:32:56 +0200 Subject: [PATCH 11/22] Remove groups in output (#517) * removed superfluous group1 and group2 from output of differential expression methods --------- Co-authored-by: janni.roebbecke --- .../differential_expression_linear_model.py | 2 - .../differential_expression_mann_whitney.py | 164 ++++++++++++++++++ .../differential_expression_t_test.py | 8 +- protzilla/methods/data_analysis.py | 80 ++++++++- protzilla/steps.py | 2 +- protzilla/utilities/transform_dfs.py | 2 +- .../data_analysis/test_analysis_plots.py | 25 +-- .../test_differential_expression.py | 33 ++-- ui/runs/form_mapping.py | 2 + ui/runs/forms/data_analysis.py | 96 +++++++++- 10 files changed, 373 insertions(+), 41 deletions(-) create mode 100644 protzilla/data_analysis/differential_expression_mann_whitney.py diff --git a/protzilla/data_analysis/differential_expression_linear_model.py b/protzilla/data_analysis/differential_expression_linear_model.py index 716f4827..a0ce0708 100644 --- a/protzilla/data_analysis/differential_expression_linear_model.py +++ b/protzilla/data_analysis/differential_expression_linear_model.py @@ -168,6 +168,4 @@ def linear_model( corrected_alpha=corrected_alpha, filtered_proteins=filtered_proteins, messages=messages, - group1=group1, - group2=group2, ) diff --git a/protzilla/data_analysis/differential_expression_mann_whitney.py b/protzilla/data_analysis/differential_expression_mann_whitney.py new file mode 100644 index 00000000..89041261 --- /dev/null +++ b/protzilla/data_analysis/differential_expression_mann_whitney.py @@ -0,0 +1,164 @@ +import logging + +import numpy as np +import pandas as pd +from scipy import stats + +from protzilla.data_analysis.differential_expression_helper import _map_log_base, apply_multiple_testing_correction +from protzilla.utilities.transform_dfs import long_to_wide + + +def mann_whitney_test_on_intensity_data( + intensity_df: pd.DataFrame, + metadata_df: pd.DataFrame, + grouping: str, + group1: str, + group2: str, + log_base: str = None, + alpha=0.05, + multiple_testing_correction_method: str = "", +) -> dict: + wide_df = long_to_wide(intensity_df) + + outputs = mann_whitney_test_on_columns( + df=wide_df, + metadata_df=metadata_df, + grouping=grouping, + group1=group1, + group2=group2, + log_base=log_base, + alpha=alpha, + multiple_testing_correction_method=multiple_testing_correction_method, + columns_name="Protein ID", + ) + differentially_expressed_proteins_df = pd.merge(intensity_df, outputs["differential_expressed_columns_df"], on="Protein ID", how="left") + differentially_expressed_proteins_df = differentially_expressed_proteins_df.loc[ + differentially_expressed_proteins_df["Protein ID"].isin(outputs["differential_expressed_columns_df"]["Protein ID"]) + ] + significant_proteins_df = pd.merge(intensity_df, outputs["significant_columns_df"], on="Protein ID", how="left") + significant_proteins_df = significant_proteins_df.loc[ + significant_proteins_df["Protein ID"].isin(outputs["significant_columns_df"]["Protein ID"]) + ] + + return dict( + differentially_expressed_proteins_df=differentially_expressed_proteins_df, + significant_proteins_df=significant_proteins_df, + corrected_p_values_df=outputs["corrected_p_values_df"], + u_statistic_df=outputs["u_statistic_df"], + log2_fold_change_df=outputs["log2_fold_change_df"], + corrected_alpha=outputs["corrected_alpha"], + messages=outputs["messages"], + ) + +def mann_whitney_test_on_columns( + df: pd.DataFrame, + metadata_df: pd.DataFrame, + grouping: str, + group1: str, + group2: str, + log_base: str = None, + alpha=0.05, + multiple_testing_correction_method: str = "", + columns_name: str = "Protein ID", +) -> dict: + """ + Perform Mann-Whitney U test on all columns of the data frame. + + @param df: The data frame containing the data in columns and a + "Sample" column that can be mapped to the metadata, to assign the groups. + @param metadata_df: The metadata data frame containing the grouping information. + @param grouping: The column name in the metadata data frame that contains the grouping information, + that should be used. + @param group1: The name of the first group for the Mann-Whitney U test. + @param group2: The name of the second group for the Mann-Whitney U test. + @param log_base: The base of the logarithm that was used to transform the data. + @param alpha: The significance level for the test. + @param multiple_testing_correction_method: The method for multiple testing correction. + + :return: a dict containing + - a df differentially_expressed_column_df in wide format containing the t-test results + - a df significant_columns_df, containing the columns of differentially_expressed_column_df, + that are significant after multiple testing correction + - a df corrected_p_values, containing the p_values after application of multiple testing correction, + - a df log2_fold_change, containing the log2 fold changes per column, + - a df t_statistic_df, containing the t-statistic per protein, + - a float corrected_alpha, containing the alpha value after application of multiple testing correction (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha), + - a list messages, containing messages for the user + """ + + df_with_groups = pd.merge( + left=df, + right=metadata_df[["Sample", grouping]], + on="Sample", + copy=False, + ) + log_base = _map_log_base(log_base) # now log_base in [2, 10, None] + + valid_columns = [] + p_values = [] + log2_fold_changes = [] + u_statistics = [] + invalid_columns = [] + data_columns = df.columns[~df.columns.isin(["Sample", grouping])] + + for column in data_columns: + group1_data = df_with_groups[df_with_groups[grouping] == group1][column] + group2_data = df_with_groups[df_with_groups[grouping] == group2][column] + u_statistic, p_value = stats.mannwhitneyu(group1_data, group2_data, alternative="two-sided") + + if not np.isnan(p_value): + log2_fold_change = ( + np.log2( + np.power(log_base, group2_data).mean() + / np.power(log_base, group1_data).mean() + ) + if log_base + else np.log2(group2_data.mean() / group1_data.mean()) + ) + + valid_columns.append(column) + p_values.append(p_value) + u_statistics.append(u_statistic) + log2_fold_changes.append(log2_fold_change) + else: + invalid_columns.append(column) + + corrected_p_values, corrected_alpha = apply_multiple_testing_correction( + p_values=p_values, + method=multiple_testing_correction_method, + alpha=alpha, + ) + + corrected_p_values_df = pd.DataFrame( + list(zip(valid_columns, corrected_p_values)), + columns=[columns_name, "corrected_p_value"], + ) + log2_fold_change_df = pd.DataFrame( + list(zip(valid_columns, log2_fold_changes)), + columns=[columns_name, "log2_fold_change"], + ) + u_statistic_df = pd.DataFrame( + list(zip(valid_columns, u_statistics)), + columns=[columns_name, "t_statistic"], + ) + + combined_df = pd.DataFrame( + list(zip(valid_columns, corrected_p_values, log2_fold_changes, u_statistics)), + columns=[columns_name, "corrected_p_value", "log2_fold_change", "u_statistic"], + ) + + significant_columns_df = combined_df[ + combined_df["corrected_p_value"] <= corrected_alpha + ] + + messages = [dict(level=logging.INFO, msg=f"Invalid columns: {invalid_columns}")] if invalid_columns else [] + + return dict( + differential_expressed_columns_df=combined_df, + significant_columns_df=significant_columns_df, + corrected_p_values_df=corrected_p_values_df, + u_statistic_df=u_statistic_df, + log2_fold_change_df=log2_fold_change_df, + corrected_alpha=corrected_alpha, + messages=messages, + ) diff --git a/protzilla/data_analysis/differential_expression_t_test.py b/protzilla/data_analysis/differential_expression_t_test.py index 71d8db09..59d43ef1 100644 --- a/protzilla/data_analysis/differential_expression_t_test.py +++ b/protzilla/data_analysis/differential_expression_t_test.py @@ -155,12 +155,8 @@ def t_test( for df in dataframes: intensity_df = pd.merge(intensity_df, df, on="Protein ID", how="left") - differentially_expressed_proteins = [ - protein for protein, p in zip(valid_protein_groups, corrected_p_values) - ] - differentially_expressed_proteins_df = intensity_df.loc[ - intensity_df["Protein ID"].isin(differentially_expressed_proteins) + intensity_df["Protein ID"].isin(valid_protein_groups) ] significant_proteins_df = differentially_expressed_proteins_df[ @@ -178,6 +174,4 @@ def t_test( corrected_alpha=corrected_alpha, # filtered_proteins=filtered_proteins, messages=messages, - group1=group1, - group2=group2, ) diff --git a/protzilla/methods/data_analysis.py b/protzilla/methods/data_analysis.py index 95b71468..5289d6bc 100644 --- a/protzilla/methods/data_analysis.py +++ b/protzilla/methods/data_analysis.py @@ -8,6 +8,8 @@ ) from protzilla.data_analysis.differential_expression_anova import anova from protzilla.data_analysis.differential_expression_linear_model import linear_model +from protzilla.data_analysis.differential_expression_mann_whitney import mann_whitney_test_on_columns, \ + mann_whitney_test_on_intensity_data from protzilla.data_analysis.differential_expression_t_test import t_test from protzilla.data_analysis.dimension_reduction import t_sne, umap from protzilla.data_analysis.ptm_analysis import filter_peptides_of_protein, ptms_per_sample, \ @@ -99,8 +101,6 @@ class DifferentialExpressionTTest(DataAnalysisStep): "t_statistic_df", "log2_fold_change_df", "corrected_alpha", - "group1", - "group2", ] def method(self, inputs: dict) -> dict: @@ -138,8 +138,6 @@ class DifferentialExpressionLinearModel(DataAnalysisStep): "log2_fold_change_df", "corrected_alpha", "filtered_proteins", - "group1", - "group2", ] def method(self, inputs: dict) -> dict: @@ -155,6 +153,80 @@ def plot(self, inputs): raise NotImplementedError("Plotting is not implemented yet for this step.") +class DifferentialExpressionMannWhitneyOnIntensity(DataAnalysisStep): + display_name = "Mann-Whitney Test" + operation = "differential_expression" + method_description = ("A function to conduct a Mann-Whitney U test between groups defined in the clinical data." + "The p-values are corrected for multiple testing.") + + input_keys = [ + "intensity_df", + "metadata_df", + "grouping", + "group1", + "group2", + "alpha", + "multiple_testing_correction_method", + ] + output_keys = [ + "differentially_expressed_proteins_df", + "significant_proteins_df", + "corrected_p_values_df", + "log2_fold_change_df", + "corrected_alpha", + ] + + def method(self, inputs: dict) -> dict: + return mann_whitney_test_on_intensity_data(**inputs) + + def insert_dataframes(self, steps: StepManager, inputs) -> dict: + if steps.get_step_output(Step, "protein_df", inputs["intensity_df"]) is not None: + inputs["intensity_df"] = steps.get_step_output(Step, "protein_df", inputs["intensity_df"]) + inputs["metadata_df"] = steps.metadata_df + inputs["log_base"] = steps.get_step_input(TransformationLog, "log_base") + return inputs + + +class DifferentialExpressionMannWhitneyOnPTM(DataAnalysisStep): + display_name = "Mann-Whitney Test" + operation = "Peptide analysis" + method_description = ("A function to conduct a Mann-Whitney U test between groups defined in the clinical data." + "The p-values are corrected for multiple testing.") + + input_keys = [ + "df", + "metadata_df", + "grouping", + "group1", + "group2", + "alpha", + "multiple_testing_correction_method", + "columns_name", + ] + output_keys = [ + "differentially_expressed_ptm_df", + "significant_ptm_df", + "corrected_p_values_df", + "log2_fold_change_df", + "corrected_alpha", + ] + + def method(self, inputs: dict) -> dict: + return mann_whitney_test_on_columns(**inputs) + + def insert_dataframes(self, steps: StepManager, inputs) -> dict: + inputs["df"] = steps.get_step_output(Step, "ptm_df", inputs["ptm_df"]) + inputs["columns_name"] = "PTM" + inputs["metadata_df"] = steps.metadata_df + inputs["log_base"] = steps.get_step_input(TransformationLog, "log_base") + return inputs + + def handle_outputs(self, outputs: dict) -> None: + outputs["differentially_expressed_ptm_df"] = outputs.pop("differential_expressed_columns_df", None) + outputs["significant_ptm_df"] = outputs.pop("significant_columns_df", None) + super().handle_outputs(outputs) + + class PlotVolcano(PlotStep): display_name = "Volcano Plot" operation = "plot" diff --git a/protzilla/steps.py b/protzilla/steps.py index ad898d9e..d5fb124e 100644 --- a/protzilla/steps.py +++ b/protzilla/steps.py @@ -189,7 +189,7 @@ def validate_outputs( if required_keys is None: required_keys = self.output_keys for key in required_keys: - if key not in self.output: + if key not in self.output or self.output[key] is None: if not soft_check: raise ValueError( f"Output validation failed: missing output {key} in outputs." diff --git a/protzilla/utilities/transform_dfs.py b/protzilla/utilities/transform_dfs.py index 59a83259..f3605b08 100644 --- a/protzilla/utilities/transform_dfs.py +++ b/protzilla/utilities/transform_dfs.py @@ -3,7 +3,7 @@ from protzilla.utilities import default_intensity_column -def long_to_wide(intensity_df: pd.DataFrame, value_name: str | None = None): +def long_to_wide(intensity_df: pd.DataFrame, value_name: str = None): """ This function transforms the dataframe to a wide format that can be more easily handled by packages such as sklearn. diff --git a/tests/protzilla/data_analysis/test_analysis_plots.py b/tests/protzilla/data_analysis/test_analysis_plots.py index 99659c71..3b665b0b 100644 --- a/tests/protzilla/data_analysis/test_analysis_plots.py +++ b/tests/protzilla/data_analysis/test_analysis_plots.py @@ -6,7 +6,7 @@ @pytest.fixture -def ttest_output(): +def ttest_input(): test_intensity_list = ( ["Sample1", "Protein1", "Gene1", 18], ["Sample1", "Protein2", "Gene1", 16], @@ -53,9 +53,9 @@ def ttest_output(): test_alpha = 0.05 - return t_test( - test_intensity_df, - test_metadata_df, + return dict( + intensity_df=test_intensity_df, + metadata_df=test_metadata_df, ttest_type="Student's t-Test", grouping="Group", group1="Group1", @@ -65,28 +65,33 @@ def ttest_output(): ) -def test_plots_volcano_plot_no_annotation(ttest_output, show_figures): +@pytest.fixture +def ttest_output(ttest_input): + return t_test(**ttest_input) + + +def test_plots_volcano_plot_no_annotation(ttest_input, ttest_output, show_figures): fig = create_volcano_plot( p_values=ttest_output["corrected_p_values_df"], log2_fc=ttest_output["log2_fold_change_df"], fc_threshold=0, alpha=ttest_output["corrected_alpha"], - group1=ttest_output["group1"], - group2=ttest_output["group2"], + group1=ttest_input["group1"], + group2=ttest_input["group2"], ) if show_figures: fig.show() -def test_plots_volcano_plot_multiple_annotations(ttest_output, show_figures): +def test_plots_volcano_plot_multiple_annotations(ttest_input, ttest_output, show_figures): fig = create_volcano_plot( p_values=ttest_output["corrected_p_values_df"], log2_fc=ttest_output["log2_fold_change_df"], fc_threshold=0, alpha=0, proteins_of_interest=["Protein1", "Protein2"], - group1=ttest_output["group1"], - group2=ttest_output["group2"], + group1=ttest_input["group1"], + group2=ttest_input["group2"], ) if show_figures: fig.show() diff --git a/tests/protzilla/data_analysis/test_differential_expression.py b/tests/protzilla/data_analysis/test_differential_expression.py index 33530752..43ad6021 100644 --- a/tests/protzilla/data_analysis/test_differential_expression.py +++ b/tests/protzilla/data_analysis/test_differential_expression.py @@ -69,9 +69,9 @@ def test_differential_expression_linear_model( test_alpha = 0.05 test_fc_threshold = 0 - current_out = linear_model( - test_intensity_df, - test_metadata_df, + current_input = dict( + intensity_df=test_intensity_df, + metadata_df=test_metadata_df, grouping="Group", group1="Group1", group2="Group2", @@ -79,13 +79,14 @@ def test_differential_expression_linear_model( alpha=test_alpha, log_base="log2", ) + current_out = linear_model(**current_input) fig = create_volcano_plot( p_values=current_out["corrected_p_values_df"], log2_fc=current_out["log2_fold_change_df"], alpha=current_out["corrected_alpha"], - group1=current_out["group1"], - group2=current_out["group2"], + group1=current_input["group1"], + group2=current_input["group2"], fc_threshold=test_fc_threshold, ) @@ -117,9 +118,9 @@ def test_differential_expression_student_t_test(diff_expr_test_data, show_figure test_alpha = 0.05 test_fc_threshold = 0.9 - current_out = t_test( - test_intensity_df, - test_metadata_df, + current_input = dict( + intensity_df=test_intensity_df, + metadata_df=test_metadata_df, ttest_type="Student's t-Test", grouping="Group", group1="Group1", @@ -128,14 +129,15 @@ def test_differential_expression_student_t_test(diff_expr_test_data, show_figure multiple_testing_correction_method="Benjamini-Hochberg", alpha=test_alpha, ) + current_out = t_test(**current_input) fig = create_volcano_plot( current_out["corrected_p_values_df"], current_out["log2_fold_change_df"], test_fc_threshold, current_out["corrected_alpha"], - current_out["group1"], - current_out["group2"], + current_input["group1"], + current_input["group2"], ) if show_figures: fig.show() @@ -173,9 +175,9 @@ def test_differential_expression_welch_t_test(diff_expr_test_data, show_figures) test_alpha = 0.05 test_fc_threshold = 0.9 - current_out = t_test( - test_intensity_df, - test_metadata_df, + current_input = dict( + intensity_df=test_intensity_df, + metadata_df=test_metadata_df, ttest_type="Welch's t-Test", grouping="Group", group1="Group1", @@ -184,14 +186,15 @@ def test_differential_expression_welch_t_test(diff_expr_test_data, show_figures) multiple_testing_correction_method="Benjamini-Hochberg", alpha=test_alpha, ) + current_out = t_test(**current_input) fig = create_volcano_plot( current_out["corrected_p_values_df"], current_out["log2_fold_change_df"], test_fc_threshold, current_out["corrected_alpha"], - current_out["group1"], - current_out["group2"], + current_input["group1"], + current_input["group2"], ) if show_figures: fig.show() diff --git a/ui/runs/form_mapping.py b/ui/runs/form_mapping.py index 600eb4db..6b951140 100644 --- a/ui/runs/form_mapping.py +++ b/ui/runs/form_mapping.py @@ -43,6 +43,8 @@ data_analysis.DifferentialExpressionANOVA: data_analysis_forms.DifferentialExpressionANOVAForm, data_analysis.DifferentialExpressionTTest: data_analysis_forms.DifferentialExpressionTTestForm, data_analysis.DifferentialExpressionLinearModel: data_analysis_forms.DifferentialExpressionLinearModelForm, + data_analysis.DifferentialExpressionMannWhitneyOnPTM: data_analysis_forms.DifferentialExpressionMannWhitneyOnPTMForm, + data_analysis.DifferentialExpressionMannWhitneyOnIntensity: data_analysis_forms.DifferentialExpressionMannWhitneyOnIntensityForm, data_analysis.PlotVolcano: data_analysis_forms.PlotVolcanoForm, data_analysis.PlotScatterPlot: data_analysis_forms.PlotScatterPlotForm, data_analysis.PlotClustergram: data_analysis_forms.PlotClustergramForm, diff --git a/ui/runs/forms/data_analysis.py b/ui/runs/forms/data_analysis.py index 10777c0a..8d6f7ebb 100644 --- a/ui/runs/forms/data_analysis.py +++ b/ui/runs/forms/data_analysis.py @@ -5,7 +5,7 @@ from protzilla.methods.data_analysis import ( DifferentialExpressionLinearModel, DifferentialExpressionTTest, - DimensionReductionUMAP, DataAnalysisStep, SelectPeptidesForProtein, + DimensionReductionUMAP, DataAnalysisStep, SelectPeptidesForProtein, PTMsPerSample, ) from protzilla.run import Run from protzilla.steps import Step @@ -277,6 +277,100 @@ def fill_form(self, run: Run) -> None: ) +class DifferentialExpressionMannWhitneyOnIntensityForm(MethodForm): + is_dynamic = True + + intensity_df = CustomChoiceField( + choices=[], label="Step to use intensity data from" + ) + multiple_testing_correction_method = CustomChoiceField( + choices=MultipleTestingCorrectionMethod, + label="Multiple testing correction", + initial=MultipleTestingCorrectionMethod.benjamini_hochberg, + ) + alpha = CustomFloatField( + label="Error rate (alpha)", min_value=0, max_value=1, initial=0.05 + ) + grouping = CustomChoiceField(choices=[], label="Grouping from metadata") + group1 = CustomChoiceField(choices=[], label="Group 1") + group2 = CustomChoiceField(choices=[], label="Group 2") + + def fill_form(self, run: Run) -> None: + self.fields["intensity_df"].choices = fill_helper.get_choices_for_protein_df_steps(run) + + self.fields["grouping"].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) + + grouping = self.data.get("grouping", self.fields["grouping"].choices[0][0]) + + # Set choices for group1 field based on selected grouping + self.fields["group1"].choices = fill_helper.to_choices( + run.steps.metadata_df[grouping].unique() + ) + + # Set choices for group2 field based on selected grouping and group1 + if ( + "group1" in self.data + and self.data["group1"] in run.steps.metadata_df[grouping].unique() + ): + self.fields["group2"].choices = [ + (el, el) + for el in run.steps.metadata_df[grouping].unique() + if el != self.data["group1"] + ] + else: + self.fields["group2"].choices = reversed( + fill_helper.to_choices(run.steps.metadata_df[grouping].unique()) + ) + + +class DifferentialExpressionMannWhitneyOnPTMForm(MethodForm): + is_dynamic = True + + ptm_df = CustomChoiceField( + choices=[], label="Step to use ptm data from" + ) + multiple_testing_correction_method = CustomChoiceField( + choices=MultipleTestingCorrectionMethod, + label="Multiple testing correction", + initial=MultipleTestingCorrectionMethod.benjamini_hochberg, + ) + alpha = CustomFloatField( + label="Error rate (alpha)", min_value=0, max_value=1, initial=0.05 + ) + grouping = CustomChoiceField(choices=[], label="Grouping from metadata") + group1 = CustomChoiceField(choices=[], label="Group 1") + group2 = CustomChoiceField(choices=[], label="Group 2") + + def fill_form(self, run: Run) -> None: + self.fields["df"].choices = fill_helper.to_choices( + run.steps.get_instance_identifiers(PTMsPerSample, "ptm_df") + ) + + self.fields["grouping"].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) + + grouping = self.data.get("grouping", self.fields["grouping"].choices[0][0]) + + # Set choices for group1 field based on selected grouping + self.fields["group1"].choices = fill_helper.to_choices( + run.steps.metadata_df[grouping].unique() + ) + + # Set choices for group2 field based on selected grouping and group1 + if ( + "group1" in self.data + and self.data["group1"] in run.steps.metadata_df[grouping].unique() + ): + self.fields["group2"].choices = [ + (el, el) + for el in run.steps.metadata_df[grouping].unique() + if el != self.data["group1"] + ] + else: + self.fields["group2"].choices = reversed( + fill_helper.to_choices(run.steps.metadata_df[grouping].unique()) + ) + + class PlotVolcanoForm(MethodForm): is_dynamic = True From 7545bf2652ae603bc67a276bf678333e91ae7ec5 Mon Sep 17 00:00:00 2001 From: Luca Jakob Treide <59066505+lucatreide@users.noreply.github.com> Date: Tue, 9 Jul 2024 11:12:44 +0200 Subject: [PATCH 12/22] fix key error after reload (#516) --- ui/runs/views.py | 25 +++++++++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/ui/runs/views.py b/ui/runs/views.py index 87aa685d..b95be756 100644 --- a/ui/runs/views.py +++ b/ui/runs/views.py @@ -18,8 +18,8 @@ from django.shortcuts import render from django.urls import reverse -from protzilla.run_helper import log_messages from protzilla.run import Run, get_available_run_names +from protzilla.run_helper import log_messages from protzilla.stepfactory import StepFactory from protzilla.steps import Step from protzilla.utilities.utilities import ( @@ -242,6 +242,8 @@ def next_(request, run_name): :return: the rendered detail page of the run with the next step/method :rtype: HttpResponse """ + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] name = request.POST.get("name", None) if name: @@ -263,6 +265,8 @@ def back(request, run_name): :return: the rendered detail page of the run with the previous step/method :rtype: HttpResponse """ + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] run.step_previous() return HttpResponseRedirect(reverse("runs:detail", args=(run_name,))) @@ -283,6 +287,8 @@ def plot(request, run_name): :return: the rendered detail page of the run, now with the plot :rtype: HttpResponse """ + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] parameters = parameters_from_post(request.POST) @@ -353,6 +359,8 @@ def add(request: HttpRequest, run_name: str): :return: the rendered detail page of the run, new method visible in sidebar :rtype: HttpResponse """ + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] method = dict(request.POST)["method"][0] @@ -373,6 +381,8 @@ def export_workflow(request: HttpRequest, run_name: str): :return: the rendered detail page of the run :rtype: HttpResponse """ + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] requested_workflow_name = request.POST["name"] run._workflow_export(requested_workflow_name) @@ -392,7 +402,8 @@ def download_plots(request: HttpRequest, run_name: str): :return: a FileResponse with the plots :rtype: FileResponse """ - + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] format_ = request.GET["format"] index = run.steps.current_step_index @@ -428,6 +439,8 @@ def delete_step(request: HttpRequest, run_name: str): :return: the rendered detail page of the run, deleted method no longer visible in sidebar :rtype: HttpResponse """ + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] post = dict(request.POST) @@ -447,6 +460,8 @@ def navigate(request, run_name: str): :return: the rendered detail page of the run with the specified step/method """ + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] post = dict(request.POST) @@ -460,6 +475,8 @@ def navigate(request, run_name: str): def tables_content(request, run_name, index, key): + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] # TODO this will change with df_mode implementation if index < len(run.steps.previous_steps): @@ -585,6 +602,8 @@ def fill_form(request: HttpRequest, run_name: str): :return: the filled form :rtype: HttpResponse """ + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] method_form = get_filled_form_by_request(request, run) form_html = "" @@ -607,6 +626,8 @@ def add_name(request, run_name): :return: the rendered detail page of the run :rtype: HttpResponse """ + if run_name not in active_runs: + active_runs[run_name] = Run(run_name) run = active_runs[run_name] run.name_step(int(request.POST["index"]), request.POST["name"]) return HttpResponseRedirect(reverse("runs:detail", args=(run_name,))) From 81e30c06b9069cd2034d347767ece781673fb4a5 Mon Sep 17 00:00:00 2001 From: Luca Jakob Treide <59066505+lucatreide@users.noreply.github.com> Date: Thu, 11 Jul 2024 16:44:49 +0200 Subject: [PATCH 13/22] Add FLEXIQuant-LF to data analysis steps (#512) * Improve error handling to only handle specific errors, also print stack_trace based on debug mode of Django * Remove outdated tests * add flexiquantlf to protzilla * add final changes for flexiquant-lf * remove unnecessary commit * add messages to flexiquant * improve comments for flexiquant * complete docstrings * add grouping column option to flexiquant * remove duplicated code * fix tests --------- Co-authored-by: henning --- protzilla/data_analysis/ptm_quantification.py | 616 ++++++++++++++++++ protzilla/importing/peptide_import.py | 37 +- protzilla/methods/data_analysis.py | 113 +++- ui/runs/form_mapping.py | 1 + ui/runs/forms/data_analysis.py | 112 +++- ui/runs/forms/fill_helper.py | 4 +- 6 files changed, 810 insertions(+), 73 deletions(-) create mode 100644 protzilla/data_analysis/ptm_quantification.py diff --git a/protzilla/data_analysis/ptm_quantification.py b/protzilla/data_analysis/ptm_quantification.py new file mode 100644 index 00000000..a98ea22b --- /dev/null +++ b/protzilla/data_analysis/ptm_quantification.py @@ -0,0 +1,616 @@ +import logging + +import matplotlib +import pandas as pd +from numpy import array, nan, sqrt, square +from scipy.stats import f, median_abs_deviation +from sklearn import linear_model + +matplotlib.use("Agg") +import matplotlib.pyplot as plt +from matplotlib import gridspec +from seaborn import distplot, diverging_palette, lineplot, scatterplot + +from protzilla.utilities.utilities import fig_to_base64 + +CONFIDENCE_BAND_ALPHA = 0.3 + + +def flexiquant_lf( + peptide_df: pd.DataFrame, + metadata_df: pd.DataFrame, + reference_group: str, + protein_id: str, + grouping_column: str, + num_init: int = 50, + mod_cutoff: float = 0.5, +) -> dict: + """ + FLEXIQuant-LF is a method to quantify protein modification extent in label-free proteomics data. + + Parts of the implementation have been adapted from https://github.com/SteenOmicsLab/FLEXIQuantLF. + + :param peptide_df: DataFrame containing peptide intensities. + :param metadata_df: DataFrame containing metadata. + :param reference_group: Name of the reference group. + :param protein_id: Protein ID that should be analysed. + :param num_init: Number of initializations for RANSAC regression. + :param mod_cutoff: RM score cutoff value for modified peptides. + """ + + df = peptide_df[peptide_df["Protein ID"] == protein_id].pivot_table( + index="Sample", columns="Sequence", values="Intensity", aggfunc="first" + ) + df.reset_index(inplace=True) + + df = pd.merge( + left=df, + right=metadata_df[["Sample", grouping_column]], + on="Sample", + copy=False, + ) + + if not grouping_column in df: + return dict( + messages=[ + dict( + level=logging.ERROR, + msg=f"No {grouping_column} column found in provided dataframe.", + ) + ] + ) + + # delete columns where all entries are nan + df.dropna(how="all", axis=1, inplace=True) + + if reference_group not in df[grouping_column].unique(): + return dict( + messages=[ + dict( + level=logging.ERROR, + msg=f"Reference sample '{reference_group}' not found in provided data.", + ) + ] + ) + else: + # filter dataframe for controls + df_control = df[df[grouping_column] == reference_group] + + # get modified peptides + modified = [] + for elm in df_control.columns: + if "ph" in elm or "ac" in elm or "gly" in elm: + modified.append(elm) + + # delete modified peptides + df_control: pd.DataFrame = df_control.drop(modified, axis=1) + df.drop(modified, inplace=True, axis=1) + + # delete Group column + group_column = df[grouping_column] + df_control.drop(grouping_column, axis=1, inplace=True) + df.drop(grouping_column, axis=1, inplace=True) + + sample_column = df["Sample"] + + # calculate median intensities for unmodified peptides of control + median_intensities = df_control.median(axis=0) + + # initiate empty lists to save results of linear regressions + slope_list = [] + r2_score_list_model = [] + r2_score_list_data = [] + reproducibility_list = [] + + df_distance_RL = df.copy() + + regression_plots = [] + + plot_dict = {} + + for idx, row in df.iterrows(): + df_train = pd.DataFrame( + {"Sample intensity": row, "Reference intensity": median_intensities} + ) + + df_train.dropna(inplace=True) + + df_train.sort_index(inplace=True, axis=0) + + # if number of peptides is smaller than 5, skip sample and continue with next interation + if len(df_train) < 5: + # set all metrices to nan + df_distance_RL.loc[idx] = nan + df_train["CB low"] = nan + df_train["CB high"] = nan + slope_list.append(nan) + r2_score_list_model.append(nan) + r2_score_list_data.append(nan) + reproducibility_list.append(nan) + + continue + + # initiate empty list to save results of linear regression + list_model_slopes = [] + list_r2_model = [] + list_r2_data = [] + all_iter_slopes = [] + all_iter_r2_data = [] + all_iter_r2_model = [] + + # set training data + X = array(df_train["Reference intensity"]).reshape(-1, 1) + y = df_train["Sample intensity"] + + sq_mad = square(median_abs_deviation(df_train["Sample intensity"])) + + for i in range(num_init): + ransac_model = linear_model.RANSACRegressor( + estimator=linear_model.LinearRegression(fit_intercept=False, n_jobs=-2), + max_trials=1000, + stop_probability=1, + min_samples=0.5, + residual_threshold=sq_mad, + ) + + ransac_model.fit(X, y) + + slope = float(ransac_model.estimator_.coef_) + + inlier_mask = ransac_model.inlier_mask_ + + df_train["Outlier"] = ~inlier_mask.astype(bool) + + # calculate R2 score based on inliers + df_train_inlier = df_train[~df_train["Outlier"]] + X_inlier = array(df_train_inlier["Reference intensity"]).reshape(-1, 1) + y_inlier = df_train_inlier["Sample intensity"] + r2_score_model = round(ransac_model.score(X_inlier, y_inlier), 4) + r2_score_data = round(ransac_model.score(X, y), 4) + + list_model_slopes.append(slope) + list_r2_model.append(r2_score_model) + list_r2_data.append(r2_score_data) + + all_iter_slopes.append(list_model_slopes) + all_iter_r2_model.append(list_r2_model) + all_iter_r2_data.append(list_r2_data) + + # determine best model based on r2 scores + best_model = list_r2_model.index(max(list_r2_model)) + + # save slope of best model to slope_list + slope_list.append(list_model_slopes[best_model]) + slope = list_model_slopes[best_model] + + # calculate reproducibility factor and save to list + series_slopes = pd.Series(list_model_slopes) + reproducibility_factor = max(series_slopes.value_counts()) / num_init + reproducibility_list.append(reproducibility_factor) + + # get r2 scores of best model + r2_score_model = list_r2_model[best_model] + r2_score_data = list_r2_data[best_model] + + # save best r2 score to lists + r2_score_list_model.append(r2_score_model) + r2_score_list_data.append(r2_score_data) + + # calculate confidence band + alpha = 0.3 + df_distance_RL, df_train = calculate_confidence_band( + slope, + median_intensities, + df_train, + X, + y, + row, + idx, + df_distance_RL, + CONFIDENCE_BAND_ALPHA, + ) + + # plot scatter plot with regression line + + plot_dict[sample_column[idx]] = [ + df_train, + idx, + r2_score_model, + r2_score_data, + slope, + alpha, + ] + + df_distance_RL["Slope"] = slope_list + df_raw_scores = calc_raw_scores(df_distance_RL, median_intensities) + + # Assume df_raw_scores is your input DataFrame + # calculate MAD per sample + df_raw_scores.drop("Slope", axis=1, inplace=True) + df_raw_scores_T = df_raw_scores.T + df_raw_scores_T = df_raw_scores_T.apply(pd.to_numeric, errors="coerce") + mad = df_raw_scores_T.mad(axis=0) + median = df_raw_scores_T.median(axis=0) + + # calculate cutoff value for each time point (> 3*MAD) + cutoff = median + 3 * mad + + # remove peptides with raw scores > cutoff for each sample + df_raw_scores_T_cutoff = df_raw_scores_T[ + round(df_raw_scores_T, 5) <= round(cutoff, 5) + ] + removed = pd.Series( + df_raw_scores_T_cutoff.index[df_raw_scores_T_cutoff.isna().all(axis=1)] + ) + df_raw_scores_T_cutoff.dropna(axis=0, how="all", inplace=True) + df_raw_scores_cutoff = df_raw_scores_T_cutoff.T + + # apply t3median normalization to calculate RM scores + df_RM = normalize_t3median(df_raw_scores_cutoff) + + # check if peptides are modified (RM score below modification cutoff) + df_RM_mod = df_RM < mod_cutoff + + df_raw_scores["Slope"] = slope_list + df_raw_scores["R2 model"] = r2_score_list_model + df_raw_scores["R2 data"] = r2_score_list_data + df_raw_scores["Reproducibility factor"] = reproducibility_list + + df_RM["Slope"] = slope_list + df_RM["R2 model"] = r2_score_list_model + df_RM["R2 data"] = r2_score_list_data + df_RM["Reproducibility factor"] = reproducibility_list + + # add Group column again + df_raw_scores[grouping_column] = group_column + df_RM[grouping_column] = group_column + df_RM_mod[grouping_column] = group_column + + # add Sample column again + df_raw_scores["Sample"] = sample_column + df_RM["Sample"] = sample_column + df_RM_mod["Sample"] = sample_column + + for sample in sample_column: + if sample in plot_dict: + regression_plots.append( + fig_to_base64( + create_regression_plots( + *plot_dict[sample], + sample_column, + df_RM[df_RM["Sample"] == sample].iloc[0], + mod_cutoff=mod_cutoff, + grouping_column=grouping_column, + ) + ) + ) + + messages = [] + if len(regression_plots) == 0: + messages.append( + dict( + level=logging.WARNING, + msg="No samples were processed. This is probably due to the fact that there are not enough valid peptides in the samples.", + ) + ) + else: + if len(regression_plots) == len(sample_column): + messages.append( + dict( + level=logging.INFO, + msg=f"All {len(sample_column)} samples have been processed successfully. {len(removed)} peptides have been removed.", + ) + ) + else: + messages.append( + dict( + level=logging.INFO, + msg=f"{len(regression_plots)}/{len(sample_column)} samples have been processed successfully. " + f"The remaining samples have been skipped due to insufficient valid peptides. {len(removed)} peptides have been removed.", + ) + ) + + return dict( + raw_scores=df_raw_scores, + RM_scores=df_RM[df_RM.columns[::-1]], + diff_modified=df_RM_mod[df_RM_mod.columns[::-1]], + removed_peptides=removed.to_list(), + plots=regression_plots, + messages=messages, + ) + + +def calculate_confidence_band( + slope: float, + median_int: float, + dataframe_train: pd.DataFrame, + X: array, + y: pd.Series, + row: pd.Series, + idx: int, + matrix_distance_RL: pd.DataFrame, + alpha: float, +): + """ + Calculates confidence bands arround the regression line. + + :param slope: Slope of the regression line. + :param median_int: Median intensity of the reference group. + :param dataframe_train: DataFrame containing the training data. + :param X: Array containing the reference intensities. + :param y: Series containing the sample intensities. + :param row: Series containing the sample intensities. + :param idx: Index of the sample. + :param matrix_distance_RL: DataFrame containing the distances to the regression line. + :param alpha: Alpha value for the confidence band. + """ + + # calculate predicted intensity with Reference intensity of a peptide and slope of the sample (Y hat) + Y_pred = slope * median_int + + # calculate W + N = len(dataframe_train) + F = f.ppf(q=1 - alpha, dfn=2, dfd=N - 1) + W = sqrt(2 * F) + + # calculate standard deviation (s(Y hat)) + # calculate prediction error + error = y - Y_pred + error.dropna(inplace=True) + + # calculate mean squared error + MSE = sum(error.apply(square)) / (N - 1) + + # calculate mean X intensity + X_bar = dataframe_train["Reference intensity"].mean() + + # iterate over all peptides of the sample + CB_low = [] + CB_high = [] + + # iterate through median peptide intensities + for idx_2, elm in dataframe_train["Reference intensity"].items(): + # calculate squared distance to mean X (numerator) + dist_X_bar = square(elm - X_bar) + + # calculate sum of squared distances to mean X(denominator) + sum_dist_X_bar = sum(square(X - X_bar)) + + # calculate standard deviation + s = float(sqrt(MSE * ((1 / N) + (dist_X_bar / sum_dist_X_bar)))) + + # calculate predicted intensity for given X + Y_hat = slope * elm + + # calculate high and low CB values and append to list + cb_low = Y_hat - W * s + cb_high = Y_hat + W * s + + CB_low.append(cb_low) + CB_high.append(cb_high) + + # calculate predicted intensities + pred_ints = median_int * slope + + # calculate distance to regression line + distance_RL = pred_ints - row + + # save distances in matrix_distance + matrix_distance_RL.loc[idx] = distance_RL + + # add CBs as columns to dataframe_train + dataframe_train["CB low"] = CB_low + dataframe_train["CB high"] = CB_high + + return matrix_distance_RL, dataframe_train + + +def create_regression_plots( + dataframe_train: pd.DataFrame, + idx: int, + r2_score_model: float, + r2_score_data: float, + slope: float, + alpha: float, + sample_column: pd.Series, + rm_scores: pd.DataFrame, + mod_cutoff: float, + grouping_column: str, +): + """ + Creates a scatter plot with regression line and confidence bands. + + :param dataframe_train: DataFrame containing the training data. + :param idx: Index of the sample. + :param r2_score_model: R2 score of the model. + :param r2_score_data: R2 score of the data. + :param slope: Slope of the regression line. + :param alpha: Alpha value for the confidence band. + :param sample_column: Series containing the sample names. + :param rm_scores: DataFrame containing the RM scores. + :param mod_cutoff: RM score cutoff value for modified peptides. + :param grouping_column: Name of the grouping column. + """ + + # create new figure with two subplots + fig = plt.figure(figsize=(16, 9)) + gs = gridspec.GridSpec(2, 1, height_ratios=[1, 6]) + ax1 = plt.subplot(gs[1]) + ax0 = plt.subplot(gs[0], sharex=ax1) + + # set space between subplots + gs.update(hspace=0.05) + + # plot histogram in upper subplot + plt.sca(ax0) + + # add title + plt.title("RANSAC Linear Regression of Sample " + str(sample_column[idx])) + + # plot histogram + distplot(a=dataframe_train["Reference intensity"], bins=150, kde=False) + + # remove axis and tick labels + plt.xlabel("") + plt.tick_params( + axis="x", # changes apply to the x-axis + which="both", # both major and minor ticks are affected + bottom=True, # ticks along the bottom edge are off + top=False, # ticks along the top edge are off + labelbottom=False, + ) # labels along the bottom edge are off + + # plot scatter plot + plt.sca(ax1) + + rm_scores = rm_scores.drop( + [ + "Slope", + "R2 model", + "R2 data", + "Reproducibility factor", + grouping_column, + "Sample", + ] + ) + # rm_scores.dropna(inplace=True) + rm_scores.clip(0, 1, inplace=True) + + rm_scores = rm_scores.to_frame(name="RM score") + # outliers in dataframe_train don't have an RM score + rm_scores = dataframe_train.merge( + rm_scores, left_index=True, right_index=True, how="left" + ) + rm_scores.fillna(-1, inplace=True) + + palette = diverging_palette(h_neg=0, h_pos=120, as_cmap=True, center="dark") + + def cmap(values: list[float]): + nanIdx = set([i for i, x in enumerate(values) if x == -1]) + return [ + color if i not in nanIdx else [0.75, 0.75, 0.75, 1.0] + for i, color in enumerate(palette(values)) + ] + + scatterplot( + x="Reference intensity", + y="Sample intensity", + data=dataframe_train, + hue=list(rm_scores.index), + palette=cmap(scale_to_mod_cutoff(list(rm_scores["RM score"]), mod_cutoff)), + ) + + # draw regression line + line_label = "R2 model: " + str(r2_score_model) + "\nR2 data: " + str(r2_score_data) + max_int = dataframe_train["Reference intensity"].max() + min_int = min( + dataframe_train["Reference intensity"].min(), + dataframe_train["Sample intensity"].min(), + ) + X = [min_int - 2, max_int] + y = [min_int - 2, slope * max_int] + plt.plot(X, y, color="darkblue", linestyle="-", label=line_label) + + # draw confidence band + lineplot( + x="Reference intensity", + y="CB low", + data=dataframe_train, + color="darkgreen", + label="CB, alpha=" + str(alpha), + ) + lineplot( + x="Reference intensity", y="CB high", data=dataframe_train, color="darkgreen" + ) + + # set line style of CB lines to dashed + for i in [len(ax1.lines) - 1, len(ax1.lines) - 2]: + ax1.lines[i].set_linestyle("--") + + # create legend if sample has 20 peptides or less otherwise don't create a legend + if len(dataframe_train) <= 20: + # set right x axis limit + plt.gca().set_xlim(right=1.4 * max_int) + plt.legend() + else: + plt.gca().get_legend().remove() + + # set y axis label + plt.ylabel("Intensity sample " + str(sample_column[idx])) + plt.xlabel("Reference intensity") + + return fig + + +def calc_raw_scores(df_distance: pd.DataFrame, median_int: pd.Series): + """ + Calculates raw scores for each sample based on the distance to the regression line. + + :param df_distance: DataFrame containing the distances to the regression line. + :param median_int: Median intensity of the reference group + """ + # copy df_distance + df_rs = df_distance.copy() + + # iterate through rows of df_distance (samples) + for idx, row in df_distance.iterrows(): + # extract slope + slope = row["Slope"] + + # delete slope from row + row.drop("Slope", inplace=True) + + # calculate raw scores + raw_scores = 1 - row / (slope * median_int) + + # add slope to raw scores + raw_scores["Slope"] = slope + + # replace idx row in df_RM_score with calculated raw scores + df_rs.loc[idx] = raw_scores + + return df_rs + + +def normalize_t3median(dataframe: pd.DataFrame): + """ + Applies Top3 median normalization to dataframe. + Determines the median of the three highest values in each row and divides every value in the row by it. + + :param dataframe: DataFrame containing the data to be normalized. + """ + # copy dataframe + dataframe_t3med = dataframe.copy() + + # for each row, normalize values by dividing each value by the median + # of the three highest values of the row + # iterate over rows of dataframe + for idx, row in dataframe.iterrows(): + # calculate the median of the three highest values + median_top3 = row.nlargest(3).median() + + # normalize each value of row by dividing by median_top3 + row_norm = row / median_top3 + + # update row in dataframe_norm with row_norm + dataframe_t3med.loc[idx] = row_norm + + return dataframe_t3med + + +def scale_to_mod_cutoff(values: list[float], cutoff: float) -> list[float]: + """ + Scales values to a cutoff value. + + :param values: List of values to be scaled. + :param cutoff: Cutoff value. + """ + + return [ + 0.5 + (v - cutoff) * 0.5 / (1 - cutoff) + if v >= 0.5 + else v * 0.5 / cutoff + if v >= 0 + else v + for v in values + ] diff --git a/protzilla/importing/peptide_import.py b/protzilla/importing/peptide_import.py index d38495dd..e7842ccb 100644 --- a/protzilla/importing/peptide_import.py +++ b/protzilla/importing/peptide_import.py @@ -33,23 +33,30 @@ def peptide_import(file_path, intensity_name, map_to_uniprot) -> dict: keep_default_na=True, ) - df = read.drop(columns=["Intensity"]) - id_df = df[id_columns] - intensity_df = df.filter(regex=f"^{peptide_intensity_name} ", axis=1) - intensity_df.columns = [ - c[len(peptide_intensity_name) + 1 :] for c in intensity_df.columns - ] - molten = pd.melt( - pd.concat([id_df, intensity_df], axis=1), - id_vars=id_columns, - var_name="Sample", - value_name="Intensity", - ) + if peptide_intensity_name != "Intensity": + df = read.drop(columns=["Intensity"]) + else: + df = read + + if "Sample" not in df.columns: + id_df = df[id_columns] + intensity_df = df.filter(regex=f"^{peptide_intensity_name} ", axis=1) + intensity_df.columns = [ + c[len(peptide_intensity_name) + 1 :] for c in intensity_df.columns + ] + molten = pd.melt( + pd.concat([id_df, intensity_df], axis=1), + id_vars=id_columns, + var_name="Sample", + value_name="Intensity", + ) + + else: + final_df = df.rename(columns={"Proteins": "Protein ID"}) + ordered = final_df[["Sample", "Protein ID", "Sequence", "Intensity", "PEP"]] molten = molten.rename(columns={"Leading razor protein": "Protein ID"}) - ordered = molten[ - ["Sample", "Protein ID", "Sequence", "Intensity", "PEP"] - ] + ordered = molten[["Sample", "Protein ID", "Sequence", "Intensity", "PEP"]] ordered.dropna(subset=["Protein ID"], inplace=True) ordered.sort_values(by=["Sample", "Protein ID"], ignore_index=True, inplace=True) diff --git a/protzilla/methods/data_analysis.py b/protzilla/methods/data_analysis.py index 5289d6bc..001a2fca 100644 --- a/protzilla/methods/data_analysis.py +++ b/protzilla/methods/data_analysis.py @@ -8,12 +8,12 @@ ) from protzilla.data_analysis.differential_expression_anova import anova from protzilla.data_analysis.differential_expression_linear_model import linear_model -from protzilla.data_analysis.differential_expression_mann_whitney import mann_whitney_test_on_columns, \ - mann_whitney_test_on_intensity_data +from protzilla.data_analysis.differential_expression_mann_whitney import ( + mann_whitney_test_on_columns, + mann_whitney_test_on_intensity_data, +) from protzilla.data_analysis.differential_expression_t_test import t_test from protzilla.data_analysis.dimension_reduction import t_sne, umap -from protzilla.data_analysis.ptm_analysis import filter_peptides_of_protein, ptms_per_sample, \ - ptms_per_protein_and_sample from protzilla.data_analysis.model_evaluation import evaluate_classification_model from protzilla.data_analysis.plots import ( clustergram_plot, @@ -22,6 +22,12 @@ scatter_plot, ) from protzilla.data_analysis.protein_graphs import peptides_to_isoform, variation_graph +from protzilla.data_analysis.ptm_analysis import ( + filter_peptides_of_protein, + ptms_per_protein_and_sample, + ptms_per_sample, +) +from protzilla.data_analysis.ptm_quantification import flexiquant_lf from protzilla.methods.data_preprocessing import TransformationLog from protzilla.steps import Plots, Step, StepManager @@ -156,8 +162,10 @@ def plot(self, inputs): class DifferentialExpressionMannWhitneyOnIntensity(DataAnalysisStep): display_name = "Mann-Whitney Test" operation = "differential_expression" - method_description = ("A function to conduct a Mann-Whitney U test between groups defined in the clinical data." - "The p-values are corrected for multiple testing.") + method_description = ( + "A function to conduct a Mann-Whitney U test between groups defined in the clinical data." + "The p-values are corrected for multiple testing." + ) input_keys = [ "intensity_df", @@ -180,8 +188,13 @@ def method(self, inputs: dict) -> dict: return mann_whitney_test_on_intensity_data(**inputs) def insert_dataframes(self, steps: StepManager, inputs) -> dict: - if steps.get_step_output(Step, "protein_df", inputs["intensity_df"]) is not None: - inputs["intensity_df"] = steps.get_step_output(Step, "protein_df", inputs["intensity_df"]) + if ( + steps.get_step_output(Step, "protein_df", inputs["intensity_df"]) + is not None + ): + inputs["intensity_df"] = steps.get_step_output( + Step, "protein_df", inputs["intensity_df"] + ) inputs["metadata_df"] = steps.metadata_df inputs["log_base"] = steps.get_step_input(TransformationLog, "log_base") return inputs @@ -190,8 +203,10 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: class DifferentialExpressionMannWhitneyOnPTM(DataAnalysisStep): display_name = "Mann-Whitney Test" operation = "Peptide analysis" - method_description = ("A function to conduct a Mann-Whitney U test between groups defined in the clinical data." - "The p-values are corrected for multiple testing.") + method_description = ( + "A function to conduct a Mann-Whitney U test between groups defined in the clinical data." + "The p-values are corrected for multiple testing." + ) input_keys = [ "df", @@ -222,7 +237,9 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: return inputs def handle_outputs(self, outputs: dict) -> None: - outputs["differentially_expressed_ptm_df"] = outputs.pop("differential_expressed_columns_df", None) + outputs["differentially_expressed_ptm_df"] = outputs.pop( + "differential_expressed_columns_df", None + ) outputs["significant_ptm_df"] = outputs.pop("significant_columns_df", None) super().handle_outputs(outputs) @@ -679,6 +696,38 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: return inputs +class FLEXIQuantLF(PlotStep): + display_name = "FLEXIQuant-LF" + operation = "modification_quantification" + method_description = "FLEXIQuant-LF is an unbiased, label-free computational tool to indirectly detect modified peptides and to quantify the degree of modification based solely on the unmodified peptide species." + + input_keys = [ + "peptide_df", + "metadata_df", + "reference_group", + "protein_id", + "num_init", + "mod_cutoff", + "grouping_column", + ] + output_keys = [ + "raw_scores", + "RM_scores", + "diff_modified", + "removed_peptides", + ] + + def method(self, inputs: dict) -> dict: + return flexiquant_lf(**inputs) + + def insert_dataframes(self, steps: StepManager, inputs) -> dict: + inputs["peptide_df"] = steps.get_step_output( + Step, "peptide_df", inputs["peptide_df"] + ) + + inputs["metadata_df"] = steps.metadata_df + + class SelectPeptidesForProtein(DataAnalysisStep): display_name = "Filter Peptides of Protein" operation = "Peptide analysis" @@ -700,18 +749,26 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: Step, "peptide_df", inputs["peptide_df"] ) + inputs["metadata_df"] = steps.metadata_df + if inputs["auto_select"]: - significant_proteins = ( - steps.get_step_output(DataAnalysisStep, "significant_proteins_df", inputs["protein_list"])) - index_of_most_significant_protein = significant_proteins['corrected_p_value'].idxmin() - most_significant_protein = significant_proteins.loc[index_of_most_significant_protein] + significant_proteins = steps.get_step_output( + DataAnalysisStep, "significant_proteins_df", inputs["protein_list"] + ) + index_of_most_significant_protein = significant_proteins[ + "corrected_p_value" + ].idxmin() + most_significant_protein = significant_proteins.loc[ + index_of_most_significant_protein + ] inputs["protein_id"] = [most_significant_protein["Protein ID"]] - self.messages.append({ - "level": logging.INFO, - "msg": - f"Selected the most significant Protein: {most_significant_protein['Protein ID']}, " - f"from {inputs['protein_list']}" - }) + self.messages.append( + { + "level": logging.INFO, + "msg": f"Selected the most significant Protein: {most_significant_protein['Protein ID']}, " + f"from {inputs['protein_list']}", + } + ) return inputs @@ -719,8 +776,10 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: class PTMsPerSample(DataAnalysisStep): display_name = "PTMs per Sample" operation = "Peptide analysis" - method_description = ("Analyze the post-translational modifications (PTMs) of a single protein of interest. " - "This function requires a peptide dataframe with PTM information.") + method_description = ( + "Analyze the post-translational modifications (PTMs) of a single protein of interest. " + "This function requires a peptide dataframe with PTM information." + ) input_keys = [ "peptide_df", @@ -742,8 +801,10 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: class PTMsProteinAndPerSample(DataAnalysisStep): display_name = "PTMs per Sample and Protein" operation = "Peptide analysis" - method_description = ("Analyze the post-translational modifications (PTMs) of all Proteins. " - "This function requires a peptide dataframe with PTM information.") + method_description = ( + "Analyze the post-translational modifications (PTMs) of all Proteins. " + "This function requires a peptide dataframe with PTM information." + ) input_keys = [ "peptide_df", @@ -759,4 +820,4 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: inputs["peptide_df"] = steps.get_step_output( Step, "peptide_df", inputs["peptide_df"] ) - return inputs \ No newline at end of file + return inputs diff --git a/ui/runs/form_mapping.py b/ui/runs/form_mapping.py index 6b951140..bc78fd71 100644 --- a/ui/runs/form_mapping.py +++ b/ui/runs/form_mapping.py @@ -62,6 +62,7 @@ data_analysis.ProteinGraphPeptidesToIsoform: data_analysis_forms.ProteinGraphPeptidesToIsoformForm, data_analysis.ProteinGraphVariationGraph: data_analysis_forms.ProteinGraphVariationGraphForm, data_analysis.SelectPeptidesForProtein: data_analysis_forms.SelectPeptidesForProteinForm, + data_analysis.FLEXIQuantLF: data_analysis_forms.FLEXIQuantLFForm, data_analysis.PTMsPerSample: data_analysis_forms.PTMsPerSampleForm, data_analysis.PTMsProteinAndPerSample: data_analysis_forms.PTMsPerProteinAndSampleForm, data_preprocessing.ImputationByMinPerSample: data_preprocessing_forms.ImputationByMinPerSampleForms, diff --git a/ui/runs/forms/data_analysis.py b/ui/runs/forms/data_analysis.py index 8d6f7ebb..1eb6441b 100644 --- a/ui/runs/forms/data_analysis.py +++ b/ui/runs/forms/data_analysis.py @@ -1,24 +1,26 @@ -import logging from enum import Enum, StrEnum -from protzilla.methods.data_preprocessing import DataPreprocessingStep from protzilla.methods.data_analysis import ( + DataAnalysisStep, DifferentialExpressionLinearModel, DifferentialExpressionTTest, - DimensionReductionUMAP, DataAnalysisStep, SelectPeptidesForProtein, PTMsPerSample, + DimensionReductionUMAP, + PTMsPerSample, + SelectPeptidesForProtein, ) +from protzilla.methods.data_preprocessing import DataPreprocessingStep from protzilla.run import Run from protzilla.steps import Step from . import fill_helper from .base import MethodForm from .custom_fields import ( + CustomBooleanField, CustomCharField, CustomChoiceField, CustomFloatField, CustomMultipleChoiceField, CustomNumberField, - CustomBooleanField, ) @@ -296,9 +298,13 @@ class DifferentialExpressionMannWhitneyOnIntensityForm(MethodForm): group2 = CustomChoiceField(choices=[], label="Group 2") def fill_form(self, run: Run) -> None: - self.fields["intensity_df"].choices = fill_helper.get_choices_for_protein_df_steps(run) + self.fields[ + "intensity_df" + ].choices = fill_helper.get_choices_for_protein_df_steps(run) - self.fields["grouping"].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) + self.fields[ + "grouping" + ].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) grouping = self.data.get("grouping", self.fields["grouping"].choices[0][0]) @@ -326,9 +332,7 @@ def fill_form(self, run: Run) -> None: class DifferentialExpressionMannWhitneyOnPTMForm(MethodForm): is_dynamic = True - ptm_df = CustomChoiceField( - choices=[], label="Step to use ptm data from" - ) + ptm_df = CustomChoiceField(choices=[], label="Step to use ptm data from") multiple_testing_correction_method = CustomChoiceField( choices=MultipleTestingCorrectionMethod, label="Multiple testing correction", @@ -346,7 +350,9 @@ def fill_form(self, run: Run) -> None: run.steps.get_instance_identifiers(PTMsPerSample, "ptm_df") ) - self.fields["grouping"].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) + self.fields[ + "grouping" + ].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) grouping = self.data.get("grouping", self.fields["grouping"].choices[0][0]) @@ -720,7 +726,7 @@ class ClassificationRandomForestForm(MethodForm): # TODO: Workflow_meta line 1763 train_val_split = CustomNumberField( label="Choose the size of the validation data set (you can either enter the absolute number of validation " - "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", + "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", initial=0.20, ) # TODO: Workflow_meta line 1770 @@ -808,7 +814,7 @@ class ClassificationSVMForm(MethodForm): ) train_val_split = CustomNumberField( label="Choose the size of the validation data set (you can either enter the absolute number of validation " - "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", + "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", initial=0.20, ) # TODO: Workflow_meta line 1973 @@ -925,7 +931,7 @@ class DimensionReductionUMAPForm(MethodForm): ) n_neighbors = CustomNumberField( label="The size of local neighborhood (in terms of number of neighboring sample points) used for manifold " - "approximation", + "approximation", min_value=2, max_value=100, step_size=1, @@ -966,7 +972,7 @@ class ProteinGraphPeptidesToIsoformForm(MethodForm): k = CustomNumberField(label="k-mer length", min_value=1, step_size=1, initial=5) allowed_mismatches = CustomNumberField( label="Number of allowed mismatched amino acids per peptide. For many allowed mismatches, this can take a " - "long time.", + "long time.", min_value=0, step_size=1, initial=2, @@ -980,6 +986,48 @@ class ProteinGraphVariationGraphForm(MethodForm): # TODO: workflow_meta line 2291 - 2295 +class FLEXIQuantLFForm(MethodForm): + is_dynamic = True + + peptide_df = CustomChoiceField(label="Peptide dataframe", choices=[]) + grouping_column = CustomChoiceField(label="Grouping column in metadata", choices=[]) + reference_group = CustomChoiceField(label="Reference group", choices=[]) + protein_id = CustomChoiceField(label="Protein ID", choices=[]) + num_init = CustomNumberField( + label="Number of RANSAC initiations", + initial=30, + min_value=1, + max_value=60, + step_size=1, + ) + mod_cutoff = CustomFloatField( + label="Modification cutoff", initial=0.5, min_value=0, max_value=1 + ) + + def fill_form(self, run: Run) -> None: + self.fields["peptide_df"].choices = fill_helper.get_choices(run, "peptide_df") + self.fields["grouping_column"].choices = fill_helper.to_choices( + run.steps.metadata_df.drop("Sample", axis=1).columns[1:] + ) + + chosen_grouping_column = self.data.get( + "grouping_column", self.fields["grouping_column"].choices[0][0] + ) + + self.fields["reference_group"].choices = fill_helper.to_choices( + run.steps.metadata_df[chosen_grouping_column].unique() + ) + peptide_df_instance_id = self.data.get( + "peptide_df", self.fields["peptide_df"].choices[0][0] + ) + + self.fields["protein_id"].choices = fill_helper.to_choices( + run.steps.get_step_output(Step, "peptide_df", peptide_df_instance_id)[ + "Protein ID" + ].unique() + ) + + class SelectPeptidesForProteinForm(MethodForm): is_dynamic = True @@ -1017,29 +1065,33 @@ def fill_form(self, run: Run) -> None: selected_auto_select = self.data.get("auto_select") - choices = fill_helper.to_choices([] if selected_auto_select else ["all proteins"]) - choices.extend(fill_helper.get_choices( - run, "significant_proteins_df", DataAnalysisStep - )) + choices = fill_helper.to_choices( + [] if selected_auto_select else ["all proteins"] + ) + choices.extend( + fill_helper.get_choices(run, "significant_proteins_df", DataAnalysisStep) + ) self.fields["protein_list"].choices = choices - chosen_list = self.data.get("protein_list", self.fields["protein_list"].choices[0][0]) + chosen_list = self.data.get( + "protein_list", self.fields["protein_list"].choices[0][0] + ) if not selected_auto_select: self.toggle_visibility("sort_proteins", True) self.toggle_visibility("protein_ids", True) if chosen_list == "all proteins": self.fields["protein_ids"].choices = fill_helper.to_choices( - run.steps.get_step_output( - Step, "protein_df" - )["Protein ID"].unique() + run.steps.get_step_output(Step, "protein_df")["Protein ID"].unique() ) else: if self.data.get("sort_proteins"): self.fields["protein_ids"].choices = fill_helper.to_choices( run.steps.get_step_output( DataAnalysisStep, "significant_proteins_df", chosen_list - ).sort_values(by="corrected_p_value")["Protein ID"].unique() + ) + .sort_values(by="corrected_p_value")["Protein ID"] + .unique() ) else: self.fields["protein_ids"].choices = fill_helper.to_choices( @@ -1062,12 +1114,12 @@ def fill_form(self, run: Run) -> None: single_protein_peptides = run.steps.get_instance_identifiers( SelectPeptidesForProtein, "peptide_df" ) - self.fields["peptide_df"].choices = fill_helper.to_choices(single_protein_peptides) - - self.fields["peptide_df"].choices = fill_helper.get_choices( - run, "peptide_df" + self.fields["peptide_df"].choices = fill_helper.to_choices( + single_protein_peptides ) + self.fields["peptide_df"].choices = fill_helper.get_choices(run, "peptide_df") + single_protein_peptides = run.steps.get_instance_identifiers( SelectPeptidesForProtein, "peptide_df" ) @@ -1082,12 +1134,10 @@ class PTMsPerProteinAndSampleForm(MethodForm): ) def fill_form(self, run: Run) -> None: - self.fields["peptide_df"].choices = fill_helper.get_choices( - run, "peptide_df" - ) + self.fields["peptide_df"].choices = fill_helper.get_choices(run, "peptide_df") single_protein_peptides = run.steps.get_instance_identifiers( SelectPeptidesForProtein, "peptide_df" ) if single_protein_peptides: - self.fields["peptide_df"].initial = single_protein_peptides[0] \ No newline at end of file + self.fields["peptide_df"].initial = single_protein_peptides[0] diff --git a/ui/runs/forms/fill_helper.py b/ui/runs/forms/fill_helper.py index 0b416f8f..eef83be6 100644 --- a/ui/runs/forms/fill_helper.py +++ b/ui/runs/forms/fill_helper.py @@ -23,7 +23,9 @@ def get_choices( :param output_key: the output key (e.g. "protein_df" or "enrichment_df" :return: a list of tuples containing the instance identifier and the instance identifier """ - return to_choices(run.steps.get_instance_identifiers(step_type, output_key)) + return reversed( + to_choices(run.steps.get_instance_identifiers(step_type, output_key)) + ) def get_choices_for_metadata_non_sample_columns(run: Run) -> list[tuple[str, str]]: From e6c29e256817780e61e5cc8803812670039c4e6d Mon Sep 17 00:00:00 2001 From: JanniRoebbecke <50395935+JanniRoebbecke@users.noreply.github.com> Date: Wed, 14 Aug 2024 20:57:05 +0200 Subject: [PATCH 14/22] Mann whitney test (#518) * evidence import * tests * filter proteins * filter sampels * Select Peptides by group or sample for coverage visualisation (#410) * untested working, needs cleanup * linter * remove dupliacte method (merge screw-up) * add doc strings, fix first few tests (miss. args) * metadata df now optional param + adjusted asserts, fixed test * formatting * conditional metadata input for protein graphs * import logging levels, formatting * add grouping tests for peptides, add error handling * spelling, formatting * remove unused import, format * fix error msgs in tests * fix line length * Convert has_metadata to property * Use logger instead of print statement * An attempt at consistent text-casing * formatting --------- Co-authored-by: henninggaertner * tests for filter_proteins with peptide file * tests for filter_proteins with peptide file, better mock dataframe * output types * added testing that only filtered Proteins are removed from peptides_df * tests for filter_samples with peptide file * extract test that peptide filtering matches protein filtering into Method * refactor: shortened tests by integrating extracted method and mock data * exclude peptides * tests * minor fixes in test * minor fixes in test * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * match mock protein dataframe to mock peptide dataframe * extract test that peptide filtering matches protein filtering into Method * implementation * tests * tests improve error logging * tests git testing * peptide import added missing column * implements form, form_mapping, method and the actual functionality (untested) * merge peptide filtering and outlier_detection * merge peptide transformation * implement passing of peptide data between preprocessing steps * complete implement passing of peptide data between preprocessing steps * implement overhead for new step evidence import * enable selecting multiple proteins of interest and implements tests * Fix EvidenceImport class (now the right method is called) * implement option to choose protein from list of significant proteins * implement option to let the system choose the most expressed protein * include messages for peptide filtering * remove selecting multiple proteins and add substring search * add cleaning of protein groups to evidence import * implement suggested changes: remove default values and correct grammar mistake * make preprocessing work without peptide_df * clean up * add cleaning of protein groups to evidence import * rename * clean Protein IDs for peptide_import and name intensity column Intensity * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used * unify into one method * cleanup old names * cleanup names * cleanup names * setup form and method * small refactor: utilize fill_helper better * implemented method * cleanup rename and docstring * tests and named sample column from "0" to "Sample" * implemented slow version * enable selecting multiple proteins * complete merge * rename file and fix selecting multiple proteins * improve efficiency of methods ptms_per_sample and ptms_per_protein_and_sample * added test for ptms_per_protein_and_sample * complete merge (fix testes after merge) * cleanup * implement differential expression with mann-whitney-test on ptm data * implement differential expression with mann-whitney-test on protein data * refactor input and output handling for Mann-Whitney-test on ptm data * add additional parameter form user and docstrings * fix typo * add test for mann whitney on intensity data * add test for mann whitney on ptm data * add mann whitney to volcano plot form * add missing value to output_keys of mann whitney test * Implement requested changes --------- Co-authored-by: janni.roebbecke --- .../differential_expression_mann_whitney.py | 115 ++++++++++-- protzilla/methods/data_analysis.py | 26 +-- protzilla/utilities/transform_dfs.py | 2 +- .../test_differential_expression.py | 165 ++++++++++++++++-- ui/runs/forms/data_analysis.py | 15 +- 5 files changed, 277 insertions(+), 46 deletions(-) diff --git a/protzilla/data_analysis/differential_expression_mann_whitney.py b/protzilla/data_analysis/differential_expression_mann_whitney.py index 89041261..3ef4f663 100644 --- a/protzilla/data_analysis/differential_expression_mann_whitney.py +++ b/protzilla/data_analysis/differential_expression_mann_whitney.py @@ -16,8 +16,34 @@ def mann_whitney_test_on_intensity_data( group2: str, log_base: str = None, alpha=0.05, - multiple_testing_correction_method: str = "", + multiple_testing_correction_method: str = "Benjamini-Hochberg", + p_value_calculation_method: str = "auto" ) -> dict: + """ + Perform Mann-Whitney U test on all proteins in the given intensity data frame. + + :param intensity_df: A protein dataframe in typical PROTzilla long format. + :param metadata_df: The metadata data frame containing the grouping information. + :param grouping: The column name in the metadata data frame that contains the grouping information, + that should be used. + :param group1: The name of the first group for the Mann-Whitney U test. + :param group2: The name of the second group for the Mann-Whitney U test. + :param log_base: The base of the logarithm that was used to transform the data. + :param alpha: The significance level for the test. + :param multiple_testing_correction_method: The method for multiple testing correction. + :param p_value_calculation_method: The method for p-value calculation. + + :return: a dict containing + - a df differentially_expressed_proteins_df in long format containing all test results + - a df significant_proteins_df, containing the proteins of differentially_expressed_column_df, + that are significant after multiple testing correction + - a df corrected_p_values, containing the p_values after application of multiple testing correction + - a df log2_fold_change, containing the log2 fold changes per protein + - a df u_statistic_df, containing the u-statistic per protein + - a float corrected_alpha, containing the alpha value after application of multiple testing correction + (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha) + - a list messages (optional), containing messages for the user + """ wide_df = long_to_wide(intensity_df) outputs = mann_whitney_test_on_columns( @@ -30,6 +56,7 @@ def mann_whitney_test_on_intensity_data( alpha=alpha, multiple_testing_correction_method=multiple_testing_correction_method, columns_name="Protein ID", + p_value_calculation_method=p_value_calculation_method ) differentially_expressed_proteins_df = pd.merge(intensity_df, outputs["differential_expressed_columns_df"], on="Protein ID", how="left") differentially_expressed_proteins_df = differentially_expressed_proteins_df.loc[ @@ -50,6 +77,65 @@ def mann_whitney_test_on_intensity_data( messages=outputs["messages"], ) +def mann_whitney_test_on_ptm_data( + ptm_df: pd.DataFrame, + metadata_df: pd.DataFrame, + grouping: str, + group1: str, + group2: str, + alpha=0.05, + multiple_testing_correction_method: str = "Benjamini-Hochberg", + p_value_calculation_method: str = "auto" +) -> dict: + """ + Perform Mann-Whitney U test on all PTMs in the given PTM data frame. + + :param ptm_df: The data frame containing the PTM data in columns and a + "Sample" column that can be mapped to the metadata, to assign the groups. + :param metadata_df: The metadata data frame containing the grouping information. + :param grouping: The column name in the metadata data frame that contains the grouping information, + that should be used. + :param group1: The name of the first group for the Mann-Whitney U test. + :param group2: The name of the second group for the Mann-Whitney U test. + :param log_base: The base of the logarithm that was used to transform the data. + :param alpha: The significance level for the test. + :param multiple_testing_correction_method: The method for multiple testing correction. + :param p_value_calculation_method: The method for p-value calculation. + + :return: a dict containing + - a df differentially_expressed_ptm_df in wide format containing all test results + - a df significant_ptm_df, containing the ptm of differentially_expressed_column_df, + that are significant after multiple testing correction + - a df corrected_p_values, containing the p_values after application of multiple testing correction, + - a df log2_fold_change, containing the log2 fold changes per column, + - a df t_statistic_df, containing the t-statistic per protein, + - a float corrected_alpha, containing the alpha value after application of multiple testing correction (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha), + - a list messages, containing messages for the user + """ + output = mann_whitney_test_on_columns( + df=ptm_df, + metadata_df=metadata_df, + grouping=grouping, + group1=group1, + group2=group2, + log_base=None, + alpha=alpha, + multiple_testing_correction_method=multiple_testing_correction_method, + columns_name="PTM", + p_value_calculation_method=p_value_calculation_method + ) + + return dict( + differentially_expressed_ptm_df=output["differential_expressed_columns_df"], + significant_ptm_df=output["significant_columns_df"], + corrected_p_values_df=output["corrected_p_values_df"], + u_statistic_df=output["u_statistic_df"], + log2_fold_change_df=output["log2_fold_change_df"], + corrected_alpha=output["corrected_alpha"], + messages=output["messages"], + ) + + def mann_whitney_test_on_columns( df: pd.DataFrame, metadata_df: pd.DataFrame, @@ -58,25 +144,28 @@ def mann_whitney_test_on_columns( group2: str, log_base: str = None, alpha=0.05, - multiple_testing_correction_method: str = "", + multiple_testing_correction_method: str = "Benjamini-Hochberg", columns_name: str = "Protein ID", + p_value_calculation_method: str = "auto" ) -> dict: """ Perform Mann-Whitney U test on all columns of the data frame. - @param df: The data frame containing the data in columns and a + :param df: The data frame containing the data in columns and a "Sample" column that can be mapped to the metadata, to assign the groups. - @param metadata_df: The metadata data frame containing the grouping information. - @param grouping: The column name in the metadata data frame that contains the grouping information, + :param metadata_df: The metadata data frame containing the grouping information. + :param grouping: The column name in the metadata data frame that contains the grouping information, that should be used. - @param group1: The name of the first group for the Mann-Whitney U test. - @param group2: The name of the second group for the Mann-Whitney U test. - @param log_base: The base of the logarithm that was used to transform the data. - @param alpha: The significance level for the test. - @param multiple_testing_correction_method: The method for multiple testing correction. + :param group1: The name of the first group for the Mann-Whitney U test. + :param group2: The name of the second group for the Mann-Whitney U test. + :param log_base: The base of the logarithm that was used to transform the data. + :param alpha: The significance level for the test. + :param multiple_testing_correction_method: The method for multiple testing correction. + :param columns_name: The semantics of the column names. This is used to name the columns in the output data frames. + :param p_value_calculation_method: The method for p-value calculation. :return: a dict containing - - a df differentially_expressed_column_df in wide format containing the t-test results + - a df differentially_expressed_column_df in wide format containing the test results - a df significant_columns_df, containing the columns of differentially_expressed_column_df, that are significant after multiple testing correction - a df corrected_p_values, containing the p_values after application of multiple testing correction, @@ -104,7 +193,7 @@ def mann_whitney_test_on_columns( for column in data_columns: group1_data = df_with_groups[df_with_groups[grouping] == group1][column] group2_data = df_with_groups[df_with_groups[grouping] == group2][column] - u_statistic, p_value = stats.mannwhitneyu(group1_data, group2_data, alternative="two-sided") + u_statistic, p_value = stats.mannwhitneyu(group1_data, group2_data, alternative="two-sided", method=p_value_calculation_method) if not np.isnan(p_value): log2_fold_change = ( @@ -139,7 +228,7 @@ def mann_whitney_test_on_columns( ) u_statistic_df = pd.DataFrame( list(zip(valid_columns, u_statistics)), - columns=[columns_name, "t_statistic"], + columns=[columns_name, "u_statistic"], ) combined_df = pd.DataFrame( diff --git a/protzilla/methods/data_analysis.py b/protzilla/methods/data_analysis.py index 001a2fca..7ad45ddd 100644 --- a/protzilla/methods/data_analysis.py +++ b/protzilla/methods/data_analysis.py @@ -9,9 +9,7 @@ from protzilla.data_analysis.differential_expression_anova import anova from protzilla.data_analysis.differential_expression_linear_model import linear_model from protzilla.data_analysis.differential_expression_mann_whitney import ( - mann_whitney_test_on_columns, - mann_whitney_test_on_intensity_data, -) + mann_whitney_test_on_intensity_data, mann_whitney_test_on_ptm_data) from protzilla.data_analysis.differential_expression_t_test import t_test from protzilla.data_analysis.dimension_reduction import t_sne, umap from protzilla.data_analysis.model_evaluation import evaluate_classification_model @@ -175,11 +173,13 @@ class DifferentialExpressionMannWhitneyOnIntensity(DataAnalysisStep): "group2", "alpha", "multiple_testing_correction_method", + "p_value_calculation_method", ] output_keys = [ "differentially_expressed_proteins_df", "significant_proteins_df", "corrected_p_values_df", + "u_statistic_df", "log2_fold_change_df", "corrected_alpha", ] @@ -209,40 +209,32 @@ class DifferentialExpressionMannWhitneyOnPTM(DataAnalysisStep): ) input_keys = [ - "df", + "ptm_df", "metadata_df", "grouping", "group1", "group2", "alpha", "multiple_testing_correction_method", - "columns_name", + "p_value_calculation_method", ] output_keys = [ "differentially_expressed_ptm_df", "significant_ptm_df", "corrected_p_values_df", + "u_statistic_df", "log2_fold_change_df", "corrected_alpha", ] def method(self, inputs: dict) -> dict: - return mann_whitney_test_on_columns(**inputs) + return mann_whitney_test_on_ptm_data(**inputs) def insert_dataframes(self, steps: StepManager, inputs) -> dict: - inputs["df"] = steps.get_step_output(Step, "ptm_df", inputs["ptm_df"]) - inputs["columns_name"] = "PTM" + inputs["ptm_df"] = steps.get_step_output(Step, "ptm_df", inputs["ptm_df"]) inputs["metadata_df"] = steps.metadata_df - inputs["log_base"] = steps.get_step_input(TransformationLog, "log_base") return inputs - - def handle_outputs(self, outputs: dict) -> None: - outputs["differentially_expressed_ptm_df"] = outputs.pop( - "differential_expressed_columns_df", None - ) - outputs["significant_ptm_df"] = outputs.pop("significant_columns_df", None) - super().handle_outputs(outputs) - + class PlotVolcano(PlotStep): display_name = "Volcano Plot" diff --git a/protzilla/utilities/transform_dfs.py b/protzilla/utilities/transform_dfs.py index f3605b08..59a83259 100644 --- a/protzilla/utilities/transform_dfs.py +++ b/protzilla/utilities/transform_dfs.py @@ -3,7 +3,7 @@ from protzilla.utilities import default_intensity_column -def long_to_wide(intensity_df: pd.DataFrame, value_name: str = None): +def long_to_wide(intensity_df: pd.DataFrame, value_name: str | None = None): """ This function transforms the dataframe to a wide format that can be more easily handled by packages such as sklearn. diff --git a/tests/protzilla/data_analysis/test_differential_expression.py b/tests/protzilla/data_analysis/test_differential_expression.py index 43ad6021..15a000e4 100644 --- a/tests/protzilla/data_analysis/test_differential_expression.py +++ b/tests/protzilla/data_analysis/test_differential_expression.py @@ -3,6 +3,8 @@ import pytest from protzilla.data_analysis.differential_expression import anova, linear_model, t_test +from protzilla.data_analysis.differential_expression_mann_whitney import mann_whitney_test_on_intensity_data, \ + mann_whitney_test_on_ptm_data from protzilla.data_analysis.plots import create_volcano_plot @@ -62,8 +64,8 @@ def diff_expr_test_data(): def test_differential_expression_linear_model( - diff_expr_test_data, - show_figures, + diff_expr_test_data, + show_figures, ): test_intensity_df, test_metadata_df = diff_expr_test_data test_alpha = 0.05 @@ -107,8 +109,8 @@ def test_differential_expression_linear_model( assert p_values_rounded == corrected_p_values assert log2fc_rounded == log2_fc assert ( - list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) - == differentially_expressed_proteins + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == differentially_expressed_proteins ) assert current_out["corrected_alpha"] == test_alpha @@ -160,13 +162,13 @@ def test_differential_expression_student_t_test(diff_expr_test_data, show_figure assert p_values_rounded == corrected_p_values assert ( - list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) - == differentially_expressed_proteins + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == differentially_expressed_proteins ) assert current_out["corrected_alpha"] == test_alpha assert ( - list(current_out["significant_proteins_df"]["Protein ID"].unique()) - == significant_proteins + list(current_out["significant_proteins_df"]["Protein ID"].unique()) + == significant_proteins ) @@ -217,13 +219,13 @@ def test_differential_expression_welch_t_test(diff_expr_test_data, show_figures) assert p_values_rounded == corrected_p_values assert ( - list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) - == differentially_expressed_proteins + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == differentially_expressed_proteins ) assert current_out["corrected_alpha"] == test_alpha assert ( - list(current_out["significant_proteins_df"]["Protein ID"].unique()) - == significant_proteins + list(current_out["significant_proteins_df"]["Protein ID"].unique()) + == significant_proteins ) @@ -386,4 +388,141 @@ def test_differential_expression_anova(show_figures): 1.0000, ] - assert assertion_p_values == p_values_rounded \ No newline at end of file + assert assertion_p_values == p_values_rounded + + +def test_differential_expression_mann_whitney_on_intensity( + diff_expr_test_data, + show_figures, +): + test_intensity_df, test_metadata_df = diff_expr_test_data + test_alpha = 0.05 + + current_input = dict( + intensity_df=test_intensity_df, + metadata_df=test_metadata_df, + grouping="Group", + group1="Group1", + group2="Group2", + multiple_testing_correction_method="Benjamini-Hochberg", + alpha=test_alpha, + log_base="log2", + p_value_calculation_method="auto", + ) + current_out = mann_whitney_test_on_intensity_data(**current_input) + + fig = create_volcano_plot( + p_values=current_out["corrected_p_values_df"], + log2_fc=current_out["log2_fold_change_df"], + alpha=current_out["corrected_alpha"], + group1=current_input["group1"], + group2=current_input["group2"], + fc_threshold=0, + ) + + if show_figures: + fig.show() + + expected_corrected_p_values = [0.2, 0.4916, 1.0, 0.2] + expected_u_statistics = [9.0, 7.0, 4.5, 9.0] + expected_log2_fc = [-10.1926, -1.0, 0.0, -5.0] + expected_differentially_expressed_proteins = ["Protein1", "Protein2", "Protein3", "Protein4"] + + p_values_rounded = [ + round(x, 4) for x in current_out["corrected_p_values_df"]["corrected_p_value"] + ] + u_statistics = current_out["u_statistic_df"]["u_statistic"] + log2fc_rounded = [ + round(x, 4) for x in current_out["log2_fold_change_df"]["log2_fold_change"] + ] + + assert p_values_rounded == expected_corrected_p_values + assert all(u_statistics == expected_u_statistics) + assert log2fc_rounded == expected_log2_fc + assert ( + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == expected_differentially_expressed_proteins + ) + assert current_out["corrected_alpha"] == test_alpha + + +@pytest.fixture +def ptm_test_data(): + test_amount_list = ( + ["Sample1", 1, 1, 10, 1, 100], + ["Sample2", 2, 2, 10, 1, 100], + ["Sample3", 3, 3, 10, 1, 100], + ["Sample4", 4, 4, 10, 1, 100], + ["Sample5", 5, 5, 10, 1, 100], + ["Sample6", 6, 3, 11, 111, 100], + ["Sample7", 7, 4, 12, 222, 100], + ["Sample8", 8, 5, 13, 333, 100], + ["Sample9", 9, 6, 14, 444, 100], + ["Sample10", 10, 7, 15, 555, 100], + ) + test_amount_df = pd.DataFrame( + data=test_amount_list, + columns=["Sample", "Oxidation", "Acetyl", "GlyGly", "Phospho", "Unmodified"], + ) + + test_metadata_list = ( + ["Sample1", "Group1"], + ["Sample2", "Group1"], + ["Sample3", "Group1"], + ["Sample4", "Group1"], + ["Sample5", "Group1"], + ["Sample6", "Group2"], + ["Sample7", "Group2"], + ["Sample8", "Group2"], + ["Sample9", "Group2"], + ["Sample10", "Group2"], + ) + test_metadata_df = pd.DataFrame( + data=test_metadata_list, + columns=["Sample", "Group"], + ) + + return test_amount_df, test_metadata_df + + +def test_differential_expression_mann_whitney_on_ptm( + ptm_test_data, + show_figures, +): + test_amount_df, test_metadata_df = ptm_test_data + test_alpha = 0.05 + + current_input = dict( + ptm_df=test_amount_df, + metadata_df=test_metadata_df, + grouping="Group", + group1="Group1", + group2="Group2", + multiple_testing_correction_method="Benjamini-Hochberg", + alpha=test_alpha, + p_value_calculation_method="auto", + ) + current_out = mann_whitney_test_on_ptm_data(**current_input) + + expected_corrected_p_values = [0.0132, 0.1423, 0.0132, 0.0132, 1.00000] + expected_u_statistics = [0.0, 4.5, 0.0, 0.0, 12.5] + expected_log2_fc = [1.415, 0.737, 0.3785, 8.3794, 0.0] + + expected_significant_ptms = ["Oxidation", "GlyGly", "Phospho"] + + p_values_rounded = [ + round(x, 4) for x in current_out["corrected_p_values_df"]["corrected_p_value"] + ] + u_statistics = current_out["u_statistic_df"]["u_statistic"] + log2_fc_rounded = [ + round(x, 4) for x in current_out["log2_fold_change_df"]["log2_fold_change"] + ] + + assert p_values_rounded == expected_corrected_p_values + assert all(u_statistics == expected_u_statistics) + assert log2_fc_rounded == expected_log2_fc + assert ( + list(current_out["significant_ptm_df"]["PTM"].unique()) + == expected_significant_ptms + ) + assert current_out["corrected_alpha"] == test_alpha \ No newline at end of file diff --git a/ui/runs/forms/data_analysis.py b/ui/runs/forms/data_analysis.py index 1eb6441b..2363832d 100644 --- a/ui/runs/forms/data_analysis.py +++ b/ui/runs/forms/data_analysis.py @@ -38,6 +38,12 @@ class MultipleTestingCorrectionMethod(Enum): bonferroni = "Bonferroni" +class PValueCalculationMethod(Enum): + auto = "Auto" + exact = "Exact" + asymptotic = "Asymptotic" + + class YesNo(Enum): yes = "Yes" no = "No" @@ -341,12 +347,17 @@ class DifferentialExpressionMannWhitneyOnPTMForm(MethodForm): alpha = CustomFloatField( label="Error rate (alpha)", min_value=0, max_value=1, initial=0.05 ) + p_value_calculation_method = CustomChoiceField( + choices=PValueCalculationMethod, + label="P-value calculation method", + initial=PValueCalculationMethod.auto, + ) grouping = CustomChoiceField(choices=[], label="Grouping from metadata") group1 = CustomChoiceField(choices=[], label="Group 1") group2 = CustomChoiceField(choices=[], label="Group 2") def fill_form(self, run: Run) -> None: - self.fields["df"].choices = fill_helper.to_choices( + self.fields["ptm_df"].choices = fill_helper.to_choices( run.steps.get_instance_identifiers(PTMsPerSample, "ptm_df") ) @@ -395,7 +406,7 @@ class PlotVolcanoForm(MethodForm): def fill_form(self, run: Run) -> None: self.fields["input_dict"].choices = fill_helper.to_choices( run.steps.get_instance_identifiers( - DifferentialExpressionTTest | DifferentialExpressionLinearModel, + DifferentialExpressionTTest | DifferentialExpressionLinearModel | DifferentialExpressionMannWhitneyOnIntensityForm, "differentially_expressed_proteins_df", ) ) From 7098ed4ecebcacb917dce98104af8710eb97ccb8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henning=20G=C3=A4rtner?= <104069093+henninggaertner@users.noreply.github.com> Date: Wed, 11 Sep 2024 11:34:24 +0200 Subject: [PATCH 15/22] Update README.md Fix link to documentation --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a9e5225c..0f173f91 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ PROTzilla aims to be a one-stop-shop for proteomics-researchers (with or without ## Quick(-and-easy)-Start-Guide > [!NOTE] -> For Documentation on how to use PROTzilla, look [here](./docs/user-guide.md). +> For Documentation on how to use PROTzilla, look [here](https://github.com/cschlaffner/PROTzilla2/wiki). To make it easy getting started we have written `run_protzilla.sh` for the macOS and Linux users and `run_protzilla.bat` for windows-users. The script takes care of installing miniconda, creating the environment, installing the requirements and starting the Server at `http://127.0.0.1:8000`. Everytime you want to run PROTzilla, **just execute the script**. If you don't have some version of conda installed, the script will install miniconda. PROTzilla (and the script) will only work as we planned with it installed. Therefore, when installing conda you have to accept their terms and conditions and should accept the suggested folders and commands to be run (tldr: `Enter` and `yes` are your friends ;) ) From 905ce0aa40859ea8aea7652ede4d158d5de26dff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Henning=20G=C3=A4rtner?= <104069093+henninggaertner@users.noreply.github.com> Date: Wed, 11 Sep 2024 11:36:49 +0200 Subject: [PATCH 16/22] Update README.md Fix broken ref and link --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 0f173f91..bddc94cc 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,7 @@ PROTzilla aims to be a one-stop-shop for proteomics-researchers (with or without ## Quick(-and-easy)-Start-Guide > [!NOTE] -> For Documentation on how to use PROTzilla, look [here](https://github.com/cschlaffner/PROTzilla2/wiki). +> For Documentation on how to use PROTzilla, look [here](./docs/user-guide.md). To make it easy getting started we have written `run_protzilla.sh` for the macOS and Linux users and `run_protzilla.bat` for windows-users. The script takes care of installing miniconda, creating the environment, installing the requirements and starting the Server at `http://127.0.0.1:8000`. Everytime you want to run PROTzilla, **just execute the script**. If you don't have some version of conda installed, the script will install miniconda. PROTzilla (and the script) will only work as we planned with it installed. Therefore, when installing conda you have to accept their terms and conditions and should accept the suggested folders and commands to be run (tldr: `Enter` and `yes` are your friends ;) ) @@ -26,7 +26,7 @@ Once the script has done most of its work, something along the lines of `Startin ## Start-Guide - a little more technical > [!NOTE] -> For further information on how to contribute on PROTzilla read our [dev-guide](./docs/dev-guide.md). +> For further information on how to contribute on PROTzilla read our [dev-guide](https://github.com/cschlaffner/PROTzilla2/wiki). PROTzilla2 uses Python 3.11 and conda to manage the environment and pip for installing packages. From 8906b0a2285083bdafa7245c6184505c7d895827 Mon Sep 17 00:00:00 2001 From: henning Date: Wed, 11 Sep 2024 13:34:00 +0200 Subject: [PATCH 17/22] Add more info in frontend in import section, fix typo --- protzilla/importing/metadata_import.py | 2 +- ui/runs/forms/importing.py | 8 +++++--- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/protzilla/importing/metadata_import.py b/protzilla/importing/metadata_import.py index 64ab0d50..3c4763fe 100644 --- a/protzilla/importing/metadata_import.py +++ b/protzilla/importing/metadata_import.py @@ -68,7 +68,7 @@ def metadata_import_method( messages.append( { "level": logging.INFO, - "msg": "The imported dataframe indicates an incorrent orientation. Consider viewing the table to ensure the orientation is correct.", + "msg": "The imported dataframe indicates an incorrect orientation. Consider viewing the table to ensure the orientation is correct.", } ) diff --git a/ui/runs/forms/importing.py b/ui/runs/forms/importing.py index cc799be3..b154eee3 100644 --- a/ui/runs/forms/importing.py +++ b/ui/runs/forms/importing.py @@ -41,7 +41,7 @@ class AggregationMethods(Enum): class MaxQuantImportForm(MethodForm): - file_path = CustomFileField(label="MaxQuant intensities file") + file_path = CustomFileField(label="MaxQuant intensities file (proteinGroups.txt)") intensity_name = CustomChoiceField( choices=IntensityType, label="Intensity parameter" ) @@ -54,7 +54,7 @@ class MaxQuantImportForm(MethodForm): class DiannImportForm(MethodForm): - file_path = CustomFileField(label="DIA-NN intensities file:") + file_path = CustomFileField(label="DIA-NN intensities file (*.pg_matrix.tsv)") map_to_uniprot = CustomBooleanField( label="Map to Uniprot IDs using Biomart (online)", required=False ) @@ -64,7 +64,9 @@ class DiannImportForm(MethodForm): class MSFraggerImportForm(MethodForm): - file_path = CustomFileField(label="MSFragger intensities file") + file_path = CustomFileField( + label="MSFragger intensities file (combined_proteins.tsv)" + ) intensity_name = CustomChoiceField( choices=IntensityNameType, label="intensity name" ) From 22292afae8d6411c0cd4a1f35e24627e854768e0 Mon Sep 17 00:00:00 2001 From: henning Date: Wed, 11 Sep 2024 14:05:08 +0200 Subject: [PATCH 18/22] Improve description of aggregation method in importing section --- ui/runs/forms/importing.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/ui/runs/forms/importing.py b/ui/runs/forms/importing.py index b154eee3..8975e3ba 100644 --- a/ui/runs/forms/importing.py +++ b/ui/runs/forms/importing.py @@ -49,7 +49,9 @@ class MaxQuantImportForm(MethodForm): label="Map to Uniprot IDs using Biomart (online)", required=False ) aggregation_method = CustomChoiceField( - choices=AggregationMethods, label="Aggregation method", initial="Sum" + choices=AggregationMethods, + label="Aggregation method used to aggregate duplicate values for protein groups", + initial="Sum", ) @@ -59,7 +61,9 @@ class DiannImportForm(MethodForm): label="Map to Uniprot IDs using Biomart (online)", required=False ) aggregation_method = CustomChoiceField( - choices=AggregationMethods, label="Aggregation method", initial="Sum" + choices=AggregationMethods, + label="Aggregation method used to aggregate duplicate values for protein groups", + initial="Sum", ) @@ -74,7 +78,9 @@ class MSFraggerImportForm(MethodForm): label="Map to Uniprot IDs using Biomart (online)", required=False ) aggregation_method = CustomChoiceField( - choices=AggregationMethods, label="Aggregation method", initial="Sum" + choices=AggregationMethods, + label="Aggregation method used to aggregate duplicate values for protein groups", + initial="Sum", ) From 50ac80105e499066a6459c09276d97c22f0c4594 Mon Sep 17 00:00:00 2001 From: Jonas0000 <40774974+Jonas0000@users.noreply.github.com> Date: Sat, 9 Nov 2024 11:54:49 +0100 Subject: [PATCH 19/22] Calculate spinner and hide next button (#536) * Hide next button in last step * calculate spinner * button bugfix --- ui/runs/static/runs/runs.js | 10 ++++++++++ ui/runs/templates/runs/details.html | 21 +++++++++++++-------- 2 files changed, 23 insertions(+), 8 deletions(-) diff --git a/ui/runs/static/runs/runs.js b/ui/runs/static/runs/runs.js index 58637bdb..4944769e 100644 --- a/ui/runs/static/runs/runs.js +++ b/ui/runs/static/runs/runs.js @@ -32,4 +32,14 @@ $(document).ready(function () { let id = $(this).attr("id"); $('#chosen-' + id).text(this.files[0].name); }); + + + // calculate button spinner + $('.calculateSpinner').on('click', function() { + // Change button content to show 'Calculating...' with a spinner + $(this).html(` + + Calculating... + `); + }); }); \ No newline at end of file diff --git a/ui/runs/templates/runs/details.html b/ui/runs/templates/runs/details.html index 5809d356..70b76bf6 100644 --- a/ui/runs/templates/runs/details.html +++ b/ui/runs/templates/runs/details.html @@ -24,8 +24,8 @@ {% endblock %} {% block content %} - -
+ +
{# include sidebar #}
{% endfor %}
- +
@@ -174,8 +176,10 @@

{{ display_name }}

{% endfor %}
- + {% if section == "data_preprocessing" %} Plot {% endif %} @@ -277,8 +281,9 @@
{% endif %} - + {% if not last_step %} + + {% endif %} From 8537578ff11e6c990d365c1e2b0bb03bb5847a65 Mon Sep 17 00:00:00 2001 From: JanniRoebbecke <50395935+JanniRoebbecke@users.noreply.github.com> Date: Mon, 11 Nov 2024 15:30:57 +0100 Subject: [PATCH 20/22] Kruskal wallis test (#522) * evidence import * tests * filter proteins * filter sampels * Select Peptides by group or sample for coverage visualisation (#410) * untested working, needs cleanup * linter * remove dupliacte method (merge screw-up) * add doc strings, fix first few tests (miss. args) * metadata df now optional param + adjusted asserts, fixed test * formatting * conditional metadata input for protein graphs * import logging levels, formatting * add grouping tests for peptides, add error handling * spelling, formatting * remove unused import, format * fix error msgs in tests * fix line length * Convert has_metadata to property * Use logger instead of print statement * An attempt at consistent text-casing * formatting --------- Co-authored-by: henninggaertner * tests for filter_proteins with peptide file * tests for filter_proteins with peptide file, better mock dataframe * output types * added testing that only filtered Proteins are removed from peptides_df * tests for filter_samples with peptide file * extract test that peptide filtering matches protein filtering into Method * refactor: shortened tests by integrating extracted method and mock data * exclude peptides * tests * minor fixes in test * minor fixes in test * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * match mock protein dataframe to mock peptide dataframe * extract test that peptide filtering matches protein filtering into Method * implementation * tests * tests improve error logging * tests git testing * peptide import added missing column * implements form, form_mapping, method and the actual functionality (untested) * merge peptide filtering and outlier_detection * merge peptide transformation * implement passing of peptide data between preprocessing steps * complete implement passing of peptide data between preprocessing steps * implement overhead for new step evidence import * enable selecting multiple proteins of interest and implements tests * Fix EvidenceImport class (now the right method is called) * implement option to choose protein from list of significant proteins * implement option to let the system choose the most expressed protein * include messages for peptide filtering * remove selecting multiple proteins and add substring search * add cleaning of protein groups to evidence import * implement suggested changes: remove default values and correct grammar mistake * make preprocessing work without peptide_df * clean up * add cleaning of protein groups to evidence import * rename * clean Protein IDs for peptide_import and name intensity column Intensity * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used * unify into one method * cleanup old names * cleanup names * cleanup names * setup form and method * small refactor: utilize fill_helper better * implemented method * cleanup rename and docstring * tests and named sample column from "0" to "Sample" * implemented slow version * enable selecting multiple proteins * complete merge * rename file and fix selecting multiple proteins * improve efficiency of methods ptms_per_sample and ptms_per_protein_and_sample * added test for ptms_per_protein_and_sample * complete merge (fix testes after merge) * cleanup * implement differential expression with mann-whitney-test on ptm data * implement differential expression with mann-whitney-test on protein data * refactor input and output handling for Mann-Whitney-test on ptm data * add additional parameter form user and docstrings * fix typo * add test for mann whitney on intensity data * add test for mann whitney on ptm data * add mann whitney to volcano plot form * implement kruskal wallis test on ptm data * implement kruskal wallis test on protein data * adds tests for kruskal wallis test * complete merge --------- Co-authored-by: janni.roebbecke --- .../data_analysis/differential_expression.py | 7 + .../differential_expression_anova.py | 27 +-- .../differential_expression_helper.py | 81 ++++++- .../differential_expression_kruskal_wallis.py | 223 ++++++++++++++++++ .../differential_expression_mann_whitney.py | 32 +-- protzilla/methods/data_analysis.py | 103 ++++++-- .../test_differential_expression.py | 185 +++++++++++++-- ui/runs/form_mapping.py | 4 +- ui/runs/forms/data_analysis.py | 130 +++++++--- 9 files changed, 657 insertions(+), 135 deletions(-) create mode 100644 protzilla/data_analysis/differential_expression_kruskal_wallis.py diff --git a/protzilla/data_analysis/differential_expression.py b/protzilla/data_analysis/differential_expression.py index fb6dacf2..a4ba5be1 100644 --- a/protzilla/data_analysis/differential_expression.py +++ b/protzilla/data_analysis/differential_expression.py @@ -2,6 +2,9 @@ from .differential_expression_anova import anova from .differential_expression_linear_model import linear_model from .differential_expression_t_test import t_test +from .differential_expression_mann_whitney import mann_whitney_test_on_intensity_data, mann_whitney_test_on_ptm_data +from .differential_expression_kruskal_wallis import kruskal_wallis_test_on_intensity_data, kruskal_wallis_test_on_ptm_data + # call methods for precommit hook not to delete imports @@ -9,3 +12,7 @@ def unused(): anova(**{}) t_test(**{}) linear_model(**{}) + mann_whitney_test_on_ptm_data(**{}) + mann_whitney_test_on_intensity_data(**{}) + kruskal_wallis_test_on_intensity_data(**{}) + kruskal_wallis_test_on_ptm_data(**{}) diff --git a/protzilla/data_analysis/differential_expression_anova.py b/protzilla/data_analysis/differential_expression_anova.py index a45b4a55..56375483 100644 --- a/protzilla/data_analysis/differential_expression_anova.py +++ b/protzilla/data_analysis/differential_expression_anova.py @@ -9,7 +9,7 @@ from .differential_expression_helper import ( INVALID_PROTEINGROUP_DATA_MSG, _map_log_base, - apply_multiple_testing_correction, + apply_multiple_testing_correction, preprocess_grouping, ) @@ -57,30 +57,7 @@ def anova( - a df filtered_proteins, containing the filtered out proteins (due to missing values or identical values), """ - assert grouping in metadata_df.columns, f"{grouping} not found in metadata_df" - messages = [] - # Check if the selected groups are present in the metadata_df - - # Select all groups if none or less than two were selected - if not selected_groups or isinstance(selected_groups, str): - selected_groups = metadata_df[grouping].unique() - selected_groups_str = "".join([" " + str(group) for group in selected_groups]) - messages.append( - { - "level": logging.INFO, - "msg": f"Auto-selected the groups {selected_groups_str} for comparison because none or only one group was selected.", - } - ) - elif len(selected_groups) >= 2: - for group in selected_groups: - if group not in metadata_df[grouping].unique(): - messages.append( - { - "level": logging.ERROR, - "msg": f"Group {group} not found in metadata_df.", - } - ) - return {"messages": messages} + selected_groups, messages = preprocess_grouping(metadata_df, grouping, selected_groups) # Merge the intensity and metadata dataframes in order to assign to each Sample # their corresponding group diff --git a/protzilla/data_analysis/differential_expression_helper.py b/protzilla/data_analysis/differential_expression_helper.py index 4c10d72d..f7fa138f 100644 --- a/protzilla/data_analysis/differential_expression_helper.py +++ b/protzilla/data_analysis/differential_expression_helper.py @@ -1,11 +1,14 @@ import logging import math +import numpy as np +import pandas as pd + from statsmodels.stats.multitest import multipletests def apply_multiple_testing_correction( - p_values: list, method: str, alpha: float + p_values: list, method: str, alpha: float ) -> tuple: """ Applies a multiple testing correction method to a list of p-values @@ -46,8 +49,80 @@ def _map_log_base(log_base: str) -> int | None: return log_base_mapping.get(log_base, None) +def preprocess_grouping( + metadata_df: pd.DataFrame, grouping: str, selected_groups: list | str +) -> tuple[list, list[dict]]: + """ + Preprocesses the grouping column in the metadata_df and checks if the selected groups are present. + :param metadata_df: the metadata dataframe + :param grouping: the column name in the metadata_df that contains the grouping information + :param selected_groups: the groups that should be compared + :return: a tuple containing the selected groups and a list of messages + """ + assert grouping in metadata_df.columns, f"{grouping} not found in metadata_df" + messages = [] + + # Check if the selected groups are present in the metadata_df + removed_groups = [] + for group in selected_groups: + if group not in metadata_df[grouping].unique(): + selected_groups.remove(group) + removed_groups.append(group) + if removed_groups: + messages.append( + { + "level": logging.WARNING, + "msg": f"Group{'s' if len(removed_groups) > 1 else ''} " + f"{str(removed_groups)[1:-1]} were not found in metadata_df and thus removed.", + } + ) + + # Select all groups if none or less than two were selected + if not selected_groups or isinstance(selected_groups, str) or len(selected_groups) < 2: + selected_groups = metadata_df[grouping].unique() + selected_groups_str = "".join(["\'" + str(group) + "\', " for group in selected_groups])[0:-2] + messages.append( + { + "level": logging.WARNING, + "msg": f"Auto-selected the groups {selected_groups_str} for comparison, " + f"because none or only one {'valid ' if removed_groups else ''}group was selected.", + } + ) + + return selected_groups, messages + + +def calculate_log2_fold_change( + group1_data: pd.Series, group2_data: pd.Series, log_base: int +) -> float: + return ( + np.log2( + np.power(log_base, group2_data).mean() + / np.power(log_base, group1_data).mean() + ) + if log_base + else np.log2(group2_data.mean() / group1_data.mean()) + ) + + +def merge_differential_expression_and_significant_df( + intensity_df: pd.DataFrame, diff_exp_df: pd.DataFrame, sig_df: pd.DataFrame +) -> tuple[pd.DataFrame, pd.DataFrame]: + differentially_expressed_proteins_df = pd.merge(intensity_df, diff_exp_df, on="Protein ID", how="left") + differentially_expressed_proteins_df = differentially_expressed_proteins_df.loc[ + differentially_expressed_proteins_df["Protein ID"].isin(diff_exp_df["Protein ID"]) + ] + significant_proteins_df = pd.merge(intensity_df, sig_df, on="Protein ID", how="left") + significant_proteins_df = significant_proteins_df.loc[ + significant_proteins_df["Protein ID"].isin(sig_df["Protein ID"]) + ] + + return differentially_expressed_proteins_df, significant_proteins_df + + INVALID_PROTEINGROUP_DATA_MSG = { "level": logging.WARNING, - "msg": "Due do missing or identical values, the p-values for some protein groups could not be calculated. These groups were omitted from the analysis. " - "To prevent this, please add filtering and imputation steps to your workflow before running the analysis.", + "msg": "Due to missing or identical values, the p-values for some protein groups could not be calculated. " + "These groups were omitted from the analysis. " + "To prevent this, please add filtering and imputation steps to your workflow before running the analysis.", } diff --git a/protzilla/data_analysis/differential_expression_kruskal_wallis.py b/protzilla/data_analysis/differential_expression_kruskal_wallis.py new file mode 100644 index 00000000..e1f8bb97 --- /dev/null +++ b/protzilla/data_analysis/differential_expression_kruskal_wallis.py @@ -0,0 +1,223 @@ +import logging + +import numpy as np +import pandas as pd +from scipy import stats + +from protzilla.data_analysis.differential_expression_helper import _map_log_base, apply_multiple_testing_correction, \ + merge_differential_expression_and_significant_df, preprocess_grouping +from protzilla.utilities.transform_dfs import long_to_wide + + +def kruskal_wallis_test_on_intensity_data( + protein_df: pd.DataFrame, + metadata_df: pd.DataFrame, + grouping: str, + selected_groups: list = None, + log_base: str = None, + alpha=0.05, + multiple_testing_correction_method: str = "Benjamini-Hochberg", +) -> dict: + """ + Perform Kruskal Wallis test on all proteins in the given intensity data frame. + + @param protein_df: A protein dataframe in typical PROTzilla long format. + @param metadata_df: The metadata data frame containing the grouping information. + @param grouping: The column name in the metadata data frame that contains the grouping information, + that should be used. + @param selected_groups: groups to test against each other + @param log_base: The base of the logarithm that was used to transform the data. + @param alpha: The significance level for the test. + @param multiple_testing_correction_method: The method for multiple testing correction. + + :return: a dict containing + - a df differentially_expressed_proteins_df in long format containing all test results + - a df significant_proteins_df, containing the proteins of differentially_expressed_column_df, + that are significant after multiple testing correction + - a df corrected_p_values, containing the p_values after application of multiple testing correction + - a df h_statistic_df, containing the u-statistic per protein + - a float corrected_alpha, containing the alpha value after application of multiple testing correction + (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha) + - a list messages (optional), containing messages for the user + """ + wide_df = long_to_wide(protein_df) + + outputs = kruskal_wallis_test_on_columns( + df=wide_df, + metadata_df=metadata_df, + grouping=grouping, + selected_groups=selected_groups, + log_base=log_base, + alpha=alpha, + multiple_testing_correction_method=multiple_testing_correction_method, + columns_name="Protein ID", + ) + differentially_expressed_proteins_df, significant_proteins_df = ( + merge_differential_expression_and_significant_df( + intensity_df=protein_df, + diff_exp_df=outputs["differential_expressed_columns_df"], + sig_df=outputs["significant_columns_df"] + )) + + return dict( + differentially_expressed_proteins_df=differentially_expressed_proteins_df, + significant_proteins_df=significant_proteins_df, + corrected_p_values_df=outputs["corrected_p_values_df"], + h_statistic_df=outputs["h_statistic_df"], + corrected_alpha=outputs["corrected_alpha"], + messages=outputs["messages"], + ) + +def kruskal_wallis_test_on_ptm_data( + ptm_df: pd.DataFrame, + metadata_df: pd.DataFrame, + grouping: str, + selected_groups: list = None, + alpha=0.05, + multiple_testing_correction_method: str = "Benjamini-Hochberg", +) -> dict: + """ + Perform Kruskal Wallis test on all PTMs in the given PTM data frame. + + @param ptm_df: The data frame containing the PTM data in columns and a + "Sample" column that can be mapped to the metadata, to assign the groups. + @param metadata_df: The metadata data frame containing the grouping information. + @param grouping: The column name in the metadata data frame that contains the grouping information, + that should be used. + @param selected_groups: groups to test against each other + @param alpha: The significance level for the test. + @param multiple_testing_correction_method: The method for multiple testing correction. + + :return: a dict containing + - a df differentially_expressed_ptm_df in wide format containing all test results + - a df significant_ptm_df, containing the ptm of differentially_expressed_column_df, + that are significant after multiple testing correction + - a df corrected_p_values, containing the p_values after application of multiple testing correction, + - a df h_statistic_df, containing the t-statistic per protein, + - a float corrected_alpha, containing the alpha value after application of multiple testing correction (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha), + - a list messages, containing messages for the user + """ + output = kruskal_wallis_test_on_columns( + df=ptm_df, + metadata_df=metadata_df, + grouping=grouping, + selected_groups=selected_groups, + log_base=None, + alpha=alpha, + multiple_testing_correction_method=multiple_testing_correction_method, + columns_name="PTM", + ) + + return dict( + differentially_expressed_ptm_df=output["differential_expressed_columns_df"], + significant_ptm_df=output["significant_columns_df"], + corrected_p_values_df=output["corrected_p_values_df"], + h_statistic_df=output["h_statistic_df"], + corrected_alpha=output["corrected_alpha"], + messages=output["messages"], + ) + + +def kruskal_wallis_test_on_columns( + df: pd.DataFrame, + metadata_df: pd.DataFrame, + grouping: str, + selected_groups: list = None, + log_base: str = None, + alpha=0.05, + multiple_testing_correction_method: str = "Benjamini-Hochberg", + columns_name: str = "Protein ID", +) -> dict: + """ + Perform Kruskal Wallis test on all columns of the data frame. + + @param df: The data frame containing the data in columns and a + "Sample" column that can be mapped to the metadata, to assign the groups. + @param metadata_df: The metadata data frame containing the grouping information. + @param grouping: The column name in the metadata data frame that contains the grouping information, + that should be used. + @param selected_groups: groups to test against each other + @param log_base: The base of the logarithm that was used to transform the data. + @param alpha: The significance level for the test. + @param multiple_testing_correction_method: The method for multiple testing correction. + @param columns_name: The semantics of the column names. This is used to name the columns in the output data frames. + + :return: a dict containing + - a df differentially_expressed_column_df in wide format containing the test results + - a df significant_columns_df, containing the columns of differentially_expressed_column_df, + that are significant after multiple testing correction + - a df corrected_p_values, containing the p_values after application of multiple testing correction, + - a df h_statistic_df, containing the t-statistic per protein, + - a float corrected_alpha, containing the alpha value after application of multiple testing correction (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha), + - a list messages, containing messages for the user + """ + selected_groups, messages = preprocess_grouping(metadata_df, grouping, selected_groups) + + df_with_groups = pd.merge( + left=df, + right=metadata_df[["Sample", grouping]], + on="Sample", + copy=False, + ) + log_base = _map_log_base(log_base) # now log_base in [2, 10, None] + + valid_columns = [] + p_values = [] + h_statistics = [] + invalid_columns = [] + data_columns = df.columns[~df.columns.isin(["Sample", grouping])] + + for column in data_columns: + all_group_data = [] + for group in selected_groups: + all_group_data.append( + df_with_groups[df_with_groups[grouping] == group][column] + ) + try: + h_statistic, p_value = stats.kruskal(*all_group_data) + except ValueError: + invalid_columns.append(column) + continue + + if not np.isnan(p_value): + valid_columns.append(column) + p_values.append(p_value) + h_statistics.append(h_statistic) + else: + invalid_columns.append(column) + + corrected_p_values, corrected_alpha = apply_multiple_testing_correction( + p_values=p_values, + method=multiple_testing_correction_method, + alpha=alpha, + ) + + corrected_p_values_df = pd.DataFrame( + list(zip(valid_columns, corrected_p_values)), + columns=[columns_name, "corrected_p_value"], + ) + h_statistic_df = pd.DataFrame( + list(zip(valid_columns, h_statistics)), + columns=[columns_name, "h_statistic"], + ) + + combined_df = pd.DataFrame( + list(zip(valid_columns, corrected_p_values, h_statistics)), + columns=[columns_name, "corrected_p_value", "h_statistic"], + ) + + significant_columns_df = combined_df[ + combined_df["corrected_p_value"] <= corrected_alpha + ] + + if invalid_columns: + messages.append(dict(level=logging.INFO, msg=f"Invalid columns: {invalid_columns}")) + + return dict( + differential_expressed_columns_df=combined_df, + significant_columns_df=significant_columns_df, + corrected_p_values_df=corrected_p_values_df, + h_statistic_df=h_statistic_df, + corrected_alpha=corrected_alpha, + messages=messages, + ) diff --git a/protzilla/data_analysis/differential_expression_mann_whitney.py b/protzilla/data_analysis/differential_expression_mann_whitney.py index 3ef4f663..03482a2e 100644 --- a/protzilla/data_analysis/differential_expression_mann_whitney.py +++ b/protzilla/data_analysis/differential_expression_mann_whitney.py @@ -4,12 +4,13 @@ import pandas as pd from scipy import stats -from protzilla.data_analysis.differential_expression_helper import _map_log_base, apply_multiple_testing_correction +from protzilla.data_analysis.differential_expression_helper import _map_log_base, apply_multiple_testing_correction, \ + merge_differential_expression_and_significant_df from protzilla.utilities.transform_dfs import long_to_wide def mann_whitney_test_on_intensity_data( - intensity_df: pd.DataFrame, + protein_df: pd.DataFrame, metadata_df: pd.DataFrame, grouping: str, group1: str, @@ -22,7 +23,7 @@ def mann_whitney_test_on_intensity_data( """ Perform Mann-Whitney U test on all proteins in the given intensity data frame. - :param intensity_df: A protein dataframe in typical PROTzilla long format. + :param protein_df: A protein dataframe in typical PROTzilla long format. :param metadata_df: The metadata data frame containing the grouping information. :param grouping: The column name in the metadata data frame that contains the grouping information, that should be used. @@ -44,7 +45,7 @@ def mann_whitney_test_on_intensity_data( (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha) - a list messages (optional), containing messages for the user """ - wide_df = long_to_wide(intensity_df) + wide_df = long_to_wide(protein_df) outputs = mann_whitney_test_on_columns( df=wide_df, @@ -58,14 +59,12 @@ def mann_whitney_test_on_intensity_data( columns_name="Protein ID", p_value_calculation_method=p_value_calculation_method ) - differentially_expressed_proteins_df = pd.merge(intensity_df, outputs["differential_expressed_columns_df"], on="Protein ID", how="left") - differentially_expressed_proteins_df = differentially_expressed_proteins_df.loc[ - differentially_expressed_proteins_df["Protein ID"].isin(outputs["differential_expressed_columns_df"]["Protein ID"]) - ] - significant_proteins_df = pd.merge(intensity_df, outputs["significant_columns_df"], on="Protein ID", how="left") - significant_proteins_df = significant_proteins_df.loc[ - significant_proteins_df["Protein ID"].isin(outputs["significant_columns_df"]["Protein ID"]) - ] + differentially_expressed_proteins_df, significant_proteins_df = ( + merge_differential_expression_and_significant_df( + intensity_df=protein_df, + diff_exp_df=outputs["differential_expressed_columns_df"], + sig_df=outputs["significant_columns_df"] + )) return dict( differentially_expressed_proteins_df=differentially_expressed_proteins_df, @@ -77,6 +76,7 @@ def mann_whitney_test_on_intensity_data( messages=outputs["messages"], ) + def mann_whitney_test_on_ptm_data( ptm_df: pd.DataFrame, metadata_df: pd.DataFrame, @@ -97,7 +97,6 @@ def mann_whitney_test_on_ptm_data( that should be used. :param group1: The name of the first group for the Mann-Whitney U test. :param group2: The name of the second group for the Mann-Whitney U test. - :param log_base: The base of the logarithm that was used to transform the data. :param alpha: The significance level for the test. :param multiple_testing_correction_method: The method for multiple testing correction. :param p_value_calculation_method: The method for p-value calculation. @@ -108,7 +107,7 @@ def mann_whitney_test_on_ptm_data( that are significant after multiple testing correction - a df corrected_p_values, containing the p_values after application of multiple testing correction, - a df log2_fold_change, containing the log2 fold changes per column, - - a df t_statistic_df, containing the t-statistic per protein, + - a df u_statistic_df, containing the t-statistic per protein, - a float corrected_alpha, containing the alpha value after application of multiple testing correction (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha), - a list messages, containing messages for the user """ @@ -170,7 +169,7 @@ def mann_whitney_test_on_columns( that are significant after multiple testing correction - a df corrected_p_values, containing the p_values after application of multiple testing correction, - a df log2_fold_change, containing the log2 fold changes per column, - - a df t_statistic_df, containing the t-statistic per protein, + - a df u_statistic_df, containing the t-statistic per protein, - a float corrected_alpha, containing the alpha value after application of multiple testing correction (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha), - a list messages, containing messages for the user """ @@ -193,7 +192,8 @@ def mann_whitney_test_on_columns( for column in data_columns: group1_data = df_with_groups[df_with_groups[grouping] == group1][column] group2_data = df_with_groups[df_with_groups[grouping] == group2][column] - u_statistic, p_value = stats.mannwhitneyu(group1_data, group2_data, alternative="two-sided", method=p_value_calculation_method) + u_statistic, p_value = ( + stats.mannwhitneyu(group1_data, group2_data, alternative="two-sided", method=p_value_calculation_method)) if not np.isnan(p_value): log2_fold_change = ( diff --git a/protzilla/methods/data_analysis.py b/protzilla/methods/data_analysis.py index 7ad45ddd..051fae01 100644 --- a/protzilla/methods/data_analysis.py +++ b/protzilla/methods/data_analysis.py @@ -7,6 +7,8 @@ k_means, ) from protzilla.data_analysis.differential_expression_anova import anova +from protzilla.data_analysis.differential_expression_kruskal_wallis import kruskal_wallis_test_on_ptm_data, \ + kruskal_wallis_test_on_intensity_data from protzilla.data_analysis.differential_expression_linear_model import linear_model from protzilla.data_analysis.differential_expression_mann_whitney import ( mann_whitney_test_on_intensity_data, mann_whitney_test_on_ptm_data) @@ -160,13 +162,11 @@ def plot(self, inputs): class DifferentialExpressionMannWhitneyOnIntensity(DataAnalysisStep): display_name = "Mann-Whitney Test" operation = "differential_expression" - method_description = ( - "A function to conduct a Mann-Whitney U test between groups defined in the clinical data." - "The p-values are corrected for multiple testing." - ) + method_description = ("A function to conduct a Mann-Whitney U test between groups defined in the clinical data." + "The p-values are corrected for multiple testing.") input_keys = [ - "intensity_df", + "protein_df", "metadata_df", "grouping", "group1", @@ -188,13 +188,8 @@ def method(self, inputs: dict) -> dict: return mann_whitney_test_on_intensity_data(**inputs) def insert_dataframes(self, steps: StepManager, inputs) -> dict: - if ( - steps.get_step_output(Step, "protein_df", inputs["intensity_df"]) - is not None - ): - inputs["intensity_df"] = steps.get_step_output( - Step, "protein_df", inputs["intensity_df"] - ) + if steps.get_step_output(Step, "protein_df", inputs["protein_df"]) is not None: + inputs["protein_df"] = steps.get_step_output(Step, "protein_df", inputs["protein_df"]) inputs["metadata_df"] = steps.metadata_df inputs["log_base"] = steps.get_step_input(TransformationLog, "log_base") return inputs @@ -203,10 +198,8 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: class DifferentialExpressionMannWhitneyOnPTM(DataAnalysisStep): display_name = "Mann-Whitney Test" operation = "Peptide analysis" - method_description = ( - "A function to conduct a Mann-Whitney U test between groups defined in the clinical data." - "The p-values are corrected for multiple testing." - ) + method_description = ("A function to conduct a Mann-Whitney U test between groups defined in the clinical data." + "The p-values are corrected for multiple testing.") input_keys = [ "ptm_df", @@ -234,7 +227,69 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: inputs["ptm_df"] = steps.get_step_output(Step, "ptm_df", inputs["ptm_df"]) inputs["metadata_df"] = steps.metadata_df return inputs - + + +class DifferentialExpressionKruskalWallisOnIntensity(DataAnalysisStep): + display_name = "Kruskal-Wallis Test" + operation = "differential_expression" + method_description = ("A function to conduct a Kruskal-Wallis test between groups defined in the clinical data." + "The p-values are corrected for multiple testing.") + + input_keys = [ + "protein_df", + "metadata_df", + "grouping", + "selected_groups", + "alpha", + "log_base", + "multiple_testing_correction_method", + ] + output_keys = [ + "differentially_expressed_proteins_df", + "significant_proteins_df", + "corrected_p_values_df", + "corrected_alpha", + ] + + def method(self, inputs: dict) -> dict: + return kruskal_wallis_test_on_intensity_data(**inputs) + + def insert_dataframes(self, steps: StepManager, inputs) -> dict: + inputs["protein_df"] = steps.get_step_output(Step, "protein_df", inputs["protein_df"]) + inputs["metadata_df"] = steps.metadata_df + inputs["log_base"] = steps.get_step_input(TransformationLog, "log_base") + return inputs + + +class DifferentialExpressionKruskalWallisOnPTM(DataAnalysisStep): + display_name = "Kruskal-Wallis Test" + operation = "Peptide analysis" + method_description = ("A function to conduct a Kruskal-Wallis test between groups defined in the clinical data." + "The p-values are corrected for multiple testing.") + + input_keys = [ + "ptm_df", + "metadata_df", + "grouping", + "selected_groups", + "alpha", + "multiple_testing_correction_method", + ] + output_keys = [ + "differentially_expressed_ptm_df", + "significant_ptm_df", + "corrected_p_values_df", + "corrected_alpha", + ] + + def method(self, inputs: dict) -> dict: + return kruskal_wallis_test_on_ptm_data(**inputs) + + def insert_dataframes(self, steps: StepManager, inputs) -> dict: + inputs["ptm_df"] = steps.get_step_output(Step, "ptm_df", inputs["ptm_df"]) + inputs["metadata_df"] = steps.metadata_df + return inputs + class PlotVolcano(PlotStep): display_name = "Volcano Plot" @@ -768,10 +823,8 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: class PTMsPerSample(DataAnalysisStep): display_name = "PTMs per Sample" operation = "Peptide analysis" - method_description = ( - "Analyze the post-translational modifications (PTMs) of a single protein of interest. " - "This function requires a peptide dataframe with PTM information." - ) + method_description = ("Analyze the post-translational modifications (PTMs) of a single protein of interest. " + "This function requires a peptide dataframe with PTM information.") input_keys = [ "peptide_df", @@ -793,10 +846,8 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: class PTMsProteinAndPerSample(DataAnalysisStep): display_name = "PTMs per Sample and Protein" operation = "Peptide analysis" - method_description = ( - "Analyze the post-translational modifications (PTMs) of all Proteins. " - "This function requires a peptide dataframe with PTM information." - ) + method_description = ("Analyze the post-translational modifications (PTMs) of all Proteins. " + "This function requires a peptide dataframe with PTM information.") input_keys = [ "peptide_df", @@ -812,4 +863,4 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: inputs["peptide_df"] = steps.get_step_output( Step, "peptide_df", inputs["peptide_df"] ) - return inputs + return inputs \ No newline at end of file diff --git a/tests/protzilla/data_analysis/test_differential_expression.py b/tests/protzilla/data_analysis/test_differential_expression.py index 15a000e4..3000b9e9 100644 --- a/tests/protzilla/data_analysis/test_differential_expression.py +++ b/tests/protzilla/data_analysis/test_differential_expression.py @@ -1,10 +1,18 @@ +import logging + import numpy as np import pandas as pd import pytest -from protzilla.data_analysis.differential_expression import anova, linear_model, t_test -from protzilla.data_analysis.differential_expression_mann_whitney import mann_whitney_test_on_intensity_data, \ - mann_whitney_test_on_ptm_data +from protzilla.data_analysis.differential_expression import ( + anova, + linear_model, + t_test, + mann_whitney_test_on_intensity_data, + mann_whitney_test_on_ptm_data, + kruskal_wallis_test_on_intensity_data, + kruskal_wallis_test_on_ptm_data +) from protzilla.data_analysis.plots import create_volcano_plot @@ -64,8 +72,8 @@ def diff_expr_test_data(): def test_differential_expression_linear_model( - diff_expr_test_data, - show_figures, + diff_expr_test_data, + show_figures, ): test_intensity_df, test_metadata_df = diff_expr_test_data test_alpha = 0.05 @@ -109,8 +117,8 @@ def test_differential_expression_linear_model( assert p_values_rounded == corrected_p_values assert log2fc_rounded == log2_fc assert ( - list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) - == differentially_expressed_proteins + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == differentially_expressed_proteins ) assert current_out["corrected_alpha"] == test_alpha @@ -162,13 +170,13 @@ def test_differential_expression_student_t_test(diff_expr_test_data, show_figure assert p_values_rounded == corrected_p_values assert ( - list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) - == differentially_expressed_proteins + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == differentially_expressed_proteins ) assert current_out["corrected_alpha"] == test_alpha assert ( - list(current_out["significant_proteins_df"]["Protein ID"].unique()) - == significant_proteins + list(current_out["significant_proteins_df"]["Protein ID"].unique()) + == significant_proteins ) @@ -219,13 +227,13 @@ def test_differential_expression_welch_t_test(diff_expr_test_data, show_figures) assert p_values_rounded == corrected_p_values assert ( - list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) - == differentially_expressed_proteins + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == differentially_expressed_proteins ) assert current_out["corrected_alpha"] == test_alpha assert ( - list(current_out["significant_proteins_df"]["Protein ID"].unique()) - == significant_proteins + list(current_out["significant_proteins_df"]["Protein ID"].unique()) + == significant_proteins ) @@ -399,7 +407,7 @@ def test_differential_expression_mann_whitney_on_intensity( test_alpha = 0.05 current_input = dict( - intensity_df=test_intensity_df, + protein_df=test_intensity_df, metadata_df=test_metadata_df, grouping="Group", group1="Group1", @@ -446,19 +454,104 @@ def test_differential_expression_mann_whitney_on_intensity( assert current_out["corrected_alpha"] == test_alpha +def test_differential_expression_kruskal_wallis_on_intensity_three_groups( + diff_expr_test_data, + show_figures, +): + test_intensity_df, test_metadata_df = diff_expr_test_data + test_alpha = 0.05 + + current_input = dict( + protein_df=test_intensity_df, + metadata_df=test_metadata_df, + grouping="Group", + selected_groups=["Group1", "Group2", "Group3"], + multiple_testing_correction_method="Benjamini-Hochberg", + alpha=test_alpha, + log_base="log2", + ) + current_out = kruskal_wallis_test_on_intensity_data(**current_input) + + expected_corrected_p_values = [0.175, 0.33, 0.5712, 0.175] + + expected_h_statistics = [4.963, 2.7925, 1.12, 4.8727] + expected_differentially_expressed_proteins = ["Protein1", "Protein2", "Protein3", "Protein4"] + + p_values_rounded = [ + round(x, 4) for x in current_out["corrected_p_values_df"]["corrected_p_value"] + ] + h_statistics_rounded = [ + round(x, 4) for x in current_out["h_statistic_df"]["h_statistic"] + ] + + assert p_values_rounded == expected_corrected_p_values + assert h_statistics_rounded == expected_h_statistics + assert ( + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == expected_differentially_expressed_proteins + ) + assert current_out["corrected_alpha"] == test_alpha + + +def test_differential_expression_kruskal_wallis_on_intensity_group_handling( + diff_expr_test_data, + show_figures, +): + test_intensity_df, test_metadata_df = diff_expr_test_data + test_alpha = 0.05 + + current_input = dict( + protein_df=test_intensity_df, + metadata_df=test_metadata_df, + grouping="Group", + selected_groups=["Group1", "wrong_group"], + multiple_testing_correction_method="Benjamini-Hochberg", + alpha=test_alpha, + log_base="log2", + ) + current_out = kruskal_wallis_test_on_intensity_data(**current_input) + + assert "messages" in current_out + assert any( + message["level"] == logging.WARNING and + "Group \'wrong_group\' were not found in metadata" in message["msg"] + + for message in current_out["messages"] + ) + assert any( + message["level"] == logging.WARNING and + "Auto-selected the groups \'Group1\', \'Group2\', \'Group3\'" in message["msg"] + + for message in current_out["messages"] + ) + + expected_corrected_p_values = [0.175, 0.33, 0.5712, 0.175] + p_values_rounded = [ + round(x, 4) for x in current_out["corrected_p_values_df"]["corrected_p_value"] + ] + assert p_values_rounded == expected_corrected_p_values + + @pytest.fixture def ptm_test_data(): test_amount_list = ( - ["Sample1", 1, 1, 10, 1, 100], - ["Sample2", 2, 2, 10, 1, 100], - ["Sample3", 3, 3, 10, 1, 100], - ["Sample4", 4, 4, 10, 1, 100], - ["Sample5", 5, 5, 10, 1, 100], - ["Sample6", 6, 3, 11, 111, 100], - ["Sample7", 7, 4, 12, 222, 100], - ["Sample8", 8, 5, 13, 333, 100], - ["Sample9", 9, 6, 14, 444, 100], + ["Sample1", 1, 1, 10, 1, 100], + ["Sample2", 2, 2, 10, 1, 100], + ["Sample3", 3, 3, 10, 1, 100], + ["Sample4", 4, 4, 10, 1, 100], + ["Sample5", 5, 5, 10, 1, 100], + ["Sample6", 6, 3, 11, 111, 100], + ["Sample7", 7, 4, 12, 222, 100], + ["Sample8", 8, 5, 13, 333, 100], + ["Sample9", 9, 6, 14, 444, 100], ["Sample10", 10, 7, 15, 555, 100], + ["Sample11", 11, 5, 16, 1111, 100], + ["Sample12", 12, 6, 17, 2222, 100], + ["Sample13", 13, 7, 18, 3333, 100], + ["Sample14", 14, 8, 19, 4444, 100], + ["Sample15", 15, 9, 20, 5555, 100], + + ) test_amount_df = pd.DataFrame( data=test_amount_list, @@ -476,6 +569,11 @@ def ptm_test_data(): ["Sample8", "Group2"], ["Sample9", "Group2"], ["Sample10", "Group2"], + ["Sample11", "Group3"], + ["Sample12", "Group3"], + ["Sample13", "Group3"], + ["Sample14", "Group3"], + ["Sample15", "Group3"], ) test_metadata_df = pd.DataFrame( data=test_metadata_list, @@ -525,4 +623,41 @@ def test_differential_expression_mann_whitney_on_ptm( list(current_out["significant_ptm_df"]["PTM"].unique()) == expected_significant_ptms ) + assert current_out["corrected_alpha"] == test_alpha + + +def test_differential_expression_kruskal_wallis_on_ptm( + ptm_test_data, + show_figures, +): + test_amount_df, test_metadata_df = ptm_test_data + test_alpha = 0.05 + + current_input = dict( + ptm_df=test_amount_df, + metadata_df=test_metadata_df, + grouping="Group", + selected_groups=["Group1", "Group2", "Group3"], + multiple_testing_correction_method="Benjamini-Hochberg", + alpha=test_alpha, + ) + current_out = kruskal_wallis_test_on_ptm_data(**current_input) + + expected_corrected_p_values = [0.0026, 0.0173, 0.0026, 0.0026] + expected_h_statistics = [12.5, 8.1159, 12.963, 12.963] + expected_significant_ptms = ["Oxidation", "Acetyl", "GlyGly", "Phospho"] + + p_values_rounded = [ + round(x, 4) for x in current_out["corrected_p_values_df"]["corrected_p_value"] + ] + h_statistics_rounded = [ + round(x, 4) for x in current_out["h_statistic_df"]["h_statistic"] + ] + + assert p_values_rounded == expected_corrected_p_values + assert h_statistics_rounded == expected_h_statistics + assert ( + list(current_out["significant_ptm_df"]["PTM"].unique()) + == expected_significant_ptms + ) assert current_out["corrected_alpha"] == test_alpha \ No newline at end of file diff --git a/ui/runs/form_mapping.py b/ui/runs/form_mapping.py index bc78fd71..7221bba6 100644 --- a/ui/runs/form_mapping.py +++ b/ui/runs/form_mapping.py @@ -43,8 +43,10 @@ data_analysis.DifferentialExpressionANOVA: data_analysis_forms.DifferentialExpressionANOVAForm, data_analysis.DifferentialExpressionTTest: data_analysis_forms.DifferentialExpressionTTestForm, data_analysis.DifferentialExpressionLinearModel: data_analysis_forms.DifferentialExpressionLinearModelForm, - data_analysis.DifferentialExpressionMannWhitneyOnPTM: data_analysis_forms.DifferentialExpressionMannWhitneyOnPTMForm, data_analysis.DifferentialExpressionMannWhitneyOnIntensity: data_analysis_forms.DifferentialExpressionMannWhitneyOnIntensityForm, + data_analysis.DifferentialExpressionMannWhitneyOnPTM: data_analysis_forms.DifferentialExpressionMannWhitneyOnPTMForm, + data_analysis.DifferentialExpressionKruskalWallisOnIntensity: data_analysis_forms.DifferentialExpressionKruskalWallisOnIntensityForm, + data_analysis.DifferentialExpressionKruskalWallisOnPTM: data_analysis_forms.DifferentialExpressionKruskalWallisOnPTMForm, data_analysis.PlotVolcano: data_analysis_forms.PlotVolcanoForm, data_analysis.PlotScatterPlot: data_analysis_forms.PlotScatterPlotForm, data_analysis.PlotClustergram: data_analysis_forms.PlotClustergramForm, diff --git a/ui/runs/forms/data_analysis.py b/ui/runs/forms/data_analysis.py index 2363832d..e575abe5 100644 --- a/ui/runs/forms/data_analysis.py +++ b/ui/runs/forms/data_analysis.py @@ -21,6 +21,7 @@ CustomFloatField, CustomMultipleChoiceField, CustomNumberField, + CustomBooleanField, ) @@ -163,8 +164,8 @@ class DifferentialExpressionANOVAForm(MethodForm): label="Multiple testing correction", initial=MultipleTestingCorrectionMethod.benjamini_hochberg, ) - alpha = CustomNumberField( - label="Error rate (alpha)", min_value=0, max_value=1, initial=0.05 + alpha = CustomFloatField( + label="Error rate (alpha)", min_value=0, max_value=1, step_size=0.01, initial=0.05 ) grouping = CustomChoiceField(choices=[], label="Grouping from metadata") @@ -201,7 +202,7 @@ class DifferentialExpressionTTestForm(MethodForm): label="Error rate (alpha)", min_value=0, max_value=1, - step_size=0.05, + step_size=0.01, initial=0.05, ) # log_base = CustomChoiceField( @@ -251,7 +252,7 @@ class DifferentialExpressionLinearModelForm(MethodForm): initial=MultipleTestingCorrectionMethod.benjamini_hochberg, ) alpha = CustomFloatField( - label="Error rate (alpha)", min_value=0, max_value=1, initial=0.05 + label="Error rate (alpha)", min_value=0, max_value=1, step_size=0.01, initial=0.05 ) grouping = CustomChoiceField(choices=[], label="Grouping from metadata") group1 = CustomChoiceField(choices=[], label="Group 1") @@ -288,8 +289,8 @@ def fill_form(self, run: Run) -> None: class DifferentialExpressionMannWhitneyOnIntensityForm(MethodForm): is_dynamic = True - intensity_df = CustomChoiceField( - choices=[], label="Step to use intensity data from" + protein_df = CustomChoiceField( + choices=[], label="Step to use protein data from" ) multiple_testing_correction_method = CustomChoiceField( choices=MultipleTestingCorrectionMethod, @@ -297,20 +298,16 @@ class DifferentialExpressionMannWhitneyOnIntensityForm(MethodForm): initial=MultipleTestingCorrectionMethod.benjamini_hochberg, ) alpha = CustomFloatField( - label="Error rate (alpha)", min_value=0, max_value=1, initial=0.05 + label="Error rate (alpha)", min_value=0, max_value=1, step_size=0.01, initial=0.05 ) grouping = CustomChoiceField(choices=[], label="Grouping from metadata") group1 = CustomChoiceField(choices=[], label="Group 1") group2 = CustomChoiceField(choices=[], label="Group 2") def fill_form(self, run: Run) -> None: - self.fields[ - "intensity_df" - ].choices = fill_helper.get_choices_for_protein_df_steps(run) + self.fields["protein_df"].choices = fill_helper.get_choices_for_protein_df_steps(run) - self.fields[ - "grouping" - ].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) + self.fields["grouping"].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) grouping = self.data.get("grouping", self.fields["grouping"].choices[0][0]) @@ -345,7 +342,7 @@ class DifferentialExpressionMannWhitneyOnPTMForm(MethodForm): initial=MultipleTestingCorrectionMethod.benjamini_hochberg, ) alpha = CustomFloatField( - label="Error rate (alpha)", min_value=0, max_value=1, initial=0.05 + label="Error rate (alpha)", min_value=0, max_value=1, step_size=0.01, initial=0.05 ) p_value_calculation_method = CustomChoiceField( choices=PValueCalculationMethod, @@ -361,9 +358,7 @@ def fill_form(self, run: Run) -> None: run.steps.get_instance_identifiers(PTMsPerSample, "ptm_df") ) - self.fields[ - "grouping" - ].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) + self.fields["grouping"].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) grouping = self.data.get("grouping", self.fields["grouping"].choices[0][0]) @@ -388,6 +383,67 @@ def fill_form(self, run: Run) -> None: ) +class DifferentialExpressionKruskalWallisOnIntensityForm(MethodForm): + is_dynamic = True + + protein_df = CustomChoiceField( + choices=[], label="Step to use protein data from" + ) + multiple_testing_correction_method = CustomChoiceField( + choices=MultipleTestingCorrectionMethod, + label="Multiple testing correction", + initial=MultipleTestingCorrectionMethod.benjamini_hochberg, + ) + alpha = CustomFloatField( + label="Error rate (alpha)", min_value=0, max_value=1, step_size=0.01, initial=0.05 + ) + + grouping = CustomChoiceField(choices=[], label="Grouping from metadata") + selected_groups = CustomMultipleChoiceField( + choices=[], label="Select groups to perform Kruskal-Wallis Test on" + ) + + def fill_form(self, run: Run) -> None: + self.fields["protein_df"].choices = fill_helper.get_choices_for_protein_df_steps(run) + + self.fields["grouping"].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) + grouping = self.data.get("grouping", self.fields["grouping"].choices[0][0]) + self.fields["selected_groups"].choices = fill_helper.to_choices( + run.steps.metadata_df[grouping].unique() + ) + + +class DifferentialExpressionKruskalWallisOnPTMForm(MethodForm): + is_dynamic = True + + ptm_df = CustomChoiceField( + choices=[], label="Step to use ptm data from" + ) + multiple_testing_correction_method = CustomChoiceField( + choices=MultipleTestingCorrectionMethod, + label="Multiple testing correction", + initial=MultipleTestingCorrectionMethod.benjamini_hochberg, + ) + alpha = CustomFloatField( + label="Error rate (alpha)", min_value=0, max_value=1, step_size=0.01, initial=0.05 + ) + + grouping = CustomChoiceField(choices=[], label="Grouping from metadata") + selected_groups = CustomMultipleChoiceField( + choices=[], label="Select groups to perform Kruskal-Wallis Test on" + ) + + def fill_form(self, run: Run) -> None: + self.fields["ptm_df"].choices = fill_helper.to_choices( + run.steps.get_instance_identifiers(PTMsPerSample, "ptm_df") + ) + self.fields["grouping"].choices = fill_helper.get_choices_for_metadata_non_sample_columns(run) + grouping = self.data.get("grouping", self.fields["grouping"].choices[0][0]) + self.fields["selected_groups"].choices = fill_helper.to_choices( + run.steps.metadata_df[grouping].unique() + ) + + class PlotVolcanoForm(MethodForm): is_dynamic = True @@ -737,7 +793,7 @@ class ClassificationRandomForestForm(MethodForm): # TODO: Workflow_meta line 1763 train_val_split = CustomNumberField( label="Choose the size of the validation data set (you can either enter the absolute number of validation " - "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", + "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", initial=0.20, ) # TODO: Workflow_meta line 1770 @@ -825,7 +881,7 @@ class ClassificationSVMForm(MethodForm): ) train_val_split = CustomNumberField( label="Choose the size of the validation data set (you can either enter the absolute number of validation " - "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", + "samples or a number between 0.0 and 1.0 to represent the percentage of validation samples)", initial=0.20, ) # TODO: Workflow_meta line 1973 @@ -942,7 +998,7 @@ class DimensionReductionUMAPForm(MethodForm): ) n_neighbors = CustomNumberField( label="The size of local neighborhood (in terms of number of neighboring sample points) used for manifold " - "approximation", + "approximation", min_value=2, max_value=100, step_size=1, @@ -983,7 +1039,7 @@ class ProteinGraphPeptidesToIsoformForm(MethodForm): k = CustomNumberField(label="k-mer length", min_value=1, step_size=1, initial=5) allowed_mismatches = CustomNumberField( label="Number of allowed mismatched amino acids per peptide. For many allowed mismatches, this can take a " - "long time.", + "long time.", min_value=0, step_size=1, initial=2, @@ -1076,33 +1132,29 @@ def fill_form(self, run: Run) -> None: selected_auto_select = self.data.get("auto_select") - choices = fill_helper.to_choices( - [] if selected_auto_select else ["all proteins"] - ) - choices.extend( - fill_helper.get_choices(run, "significant_proteins_df", DataAnalysisStep) - ) + choices = fill_helper.to_choices([] if selected_auto_select else ["all proteins"]) + choices.extend(fill_helper.get_choices( + run, "significant_proteins_df", DataAnalysisStep + )) self.fields["protein_list"].choices = choices - chosen_list = self.data.get( - "protein_list", self.fields["protein_list"].choices[0][0] - ) + chosen_list = self.data.get("protein_list", self.fields["protein_list"].choices[0][0]) if not selected_auto_select: self.toggle_visibility("sort_proteins", True) self.toggle_visibility("protein_ids", True) if chosen_list == "all proteins": self.fields["protein_ids"].choices = fill_helper.to_choices( - run.steps.get_step_output(Step, "protein_df")["Protein ID"].unique() + run.steps.get_step_output( + Step, "protein_df" + )["Protein ID"].unique() ) else: if self.data.get("sort_proteins"): self.fields["protein_ids"].choices = fill_helper.to_choices( run.steps.get_step_output( DataAnalysisStep, "significant_proteins_df", chosen_list - ) - .sort_values(by="corrected_p_value")["Protein ID"] - .unique() + ).sort_values(by="corrected_p_value")["Protein ID"].unique() ) else: self.fields["protein_ids"].choices = fill_helper.to_choices( @@ -1125,11 +1177,11 @@ def fill_form(self, run: Run) -> None: single_protein_peptides = run.steps.get_instance_identifiers( SelectPeptidesForProtein, "peptide_df" ) - self.fields["peptide_df"].choices = fill_helper.to_choices( - single_protein_peptides - ) + self.fields["peptide_df"].choices = fill_helper.to_choices(single_protein_peptides) - self.fields["peptide_df"].choices = fill_helper.get_choices(run, "peptide_df") + self.fields["peptide_df"].choices = fill_helper.get_choices( + run, "peptide_df" + ) single_protein_peptides = run.steps.get_instance_identifiers( SelectPeptidesForProtein, "peptide_df" @@ -1151,4 +1203,4 @@ def fill_form(self, run: Run) -> None: SelectPeptidesForProtein, "peptide_df" ) if single_protein_peptides: - self.fields["peptide_df"].initial = single_protein_peptides[0] + self.fields["peptide_df"].initial = single_protein_peptides[0] \ No newline at end of file From 1924c0ca46db681ecf7fbc5011b0d98a37510fe5 Mon Sep 17 00:00:00 2001 From: JanniRoebbecke <50395935+JanniRoebbecke@users.noreply.github.com> Date: Mon, 11 Nov 2024 16:10:49 +0100 Subject: [PATCH 21/22] Normalize ptm data (#525) * implement differential expression with mann-whitney-test on ptm data * implement differential expression with mann-whitney-test on protein data * refactor input and output handling for Mann-Whitney-test on ptm data * add additional parameter form user and docstrings * fix typo * add test for mann whitney on intensity data * add test for mann whitney on ptm data * add mann whitney to volcano plot form * implement kruskal wallis test on ptm data * implement kruskal wallis test on protein data * adds tests for kruskal wallis test * implement normalization * adapt tests * add unit test for ptm_df normalization * change mock files as to not upset other tests * make names of select peptides for Protein Step consistent * complete merge --------- Co-authored-by: janni.roebbecke --- .../differential_expression_helper.py | 16 ++++++ .../differential_expression_kruskal_wallis.py | 7 ++- .../differential_expression_mann_whitney.py | 21 ++++--- protzilla/data_analysis/ptm_analysis.py | 4 +- protzilla/methods/data_analysis.py | 32 +++++------ tests/conftest.py | 1 + .../test_differential_expression.py | 56 +++++++++---------- .../test_differential_expression_helper.py | 15 +++++ .../test_filter_peptites_of_protein.py | 4 +- .../data_analysis/test_peptide_analysis.py | 13 +++-- ui/runs/forms/data_analysis.py | 10 ++-- 11 files changed, 107 insertions(+), 72 deletions(-) create mode 100644 tests/protzilla/data_analysis/test_differential_expression_helper.py diff --git a/protzilla/data_analysis/differential_expression_helper.py b/protzilla/data_analysis/differential_expression_helper.py index f7fa138f..de2a9192 100644 --- a/protzilla/data_analysis/differential_expression_helper.py +++ b/protzilla/data_analysis/differential_expression_helper.py @@ -120,6 +120,22 @@ def merge_differential_expression_and_significant_df( return differentially_expressed_proteins_df, significant_proteins_df +def normalize_ptm_df(ptm_df: pd.DataFrame) -> pd.DataFrame: + """ + Normalizes the PTM data frame by dividing the PTM values by the amount of peptides. + :param ptm_df: the PTM data frame + :return: the normalized PTM data frame + """ + ptm_df_without_sample = ptm_df.drop("Sample", axis=1) + + normalized_ptm_df = ptm_df_without_sample.div(ptm_df["Total Amount of Peptides"], axis=0) + + normalized_ptm_df = normalized_ptm_df.drop("Total Amount of Peptides", axis=1) + normalized_ptm_df.insert(0, "Sample", ptm_df["Sample"]) + + return normalized_ptm_df + + INVALID_PROTEINGROUP_DATA_MSG = { "level": logging.WARNING, "msg": "Due to missing or identical values, the p-values for some protein groups could not be calculated. " diff --git a/protzilla/data_analysis/differential_expression_kruskal_wallis.py b/protzilla/data_analysis/differential_expression_kruskal_wallis.py index e1f8bb97..9e778517 100644 --- a/protzilla/data_analysis/differential_expression_kruskal_wallis.py +++ b/protzilla/data_analysis/differential_expression_kruskal_wallis.py @@ -5,7 +5,7 @@ from scipy import stats from protzilla.data_analysis.differential_expression_helper import _map_log_base, apply_multiple_testing_correction, \ - merge_differential_expression_and_significant_df, preprocess_grouping + merge_differential_expression_and_significant_df, preprocess_grouping, normalize_ptm_df from protzilla.utilities.transform_dfs import long_to_wide @@ -97,8 +97,11 @@ def kruskal_wallis_test_on_ptm_data( - a float corrected_alpha, containing the alpha value after application of multiple testing correction (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha), - a list messages, containing messages for the user """ + + normalized_ptm_df = normalize_ptm_df(ptm_df) + output = kruskal_wallis_test_on_columns( - df=ptm_df, + df=normalized_ptm_df, metadata_df=metadata_df, grouping=grouping, selected_groups=selected_groups, diff --git a/protzilla/data_analysis/differential_expression_mann_whitney.py b/protzilla/data_analysis/differential_expression_mann_whitney.py index 03482a2e..a7a06c2e 100644 --- a/protzilla/data_analysis/differential_expression_mann_whitney.py +++ b/protzilla/data_analysis/differential_expression_mann_whitney.py @@ -5,7 +5,7 @@ from scipy import stats from protzilla.data_analysis.differential_expression_helper import _map_log_base, apply_multiple_testing_correction, \ - merge_differential_expression_and_significant_df + merge_differential_expression_and_significant_df, normalize_ptm_df from protzilla.utilities.transform_dfs import long_to_wide @@ -59,12 +59,14 @@ def mann_whitney_test_on_intensity_data( columns_name="Protein ID", p_value_calculation_method=p_value_calculation_method ) - differentially_expressed_proteins_df, significant_proteins_df = ( - merge_differential_expression_and_significant_df( - intensity_df=protein_df, - diff_exp_df=outputs["differential_expressed_columns_df"], - sig_df=outputs["significant_columns_df"] - )) + differentially_expressed_proteins_df = pd.merge(protein_df, outputs["differential_expressed_columns_df"], on="Protein ID", how="left") + differentially_expressed_proteins_df = differentially_expressed_proteins_df.loc[ + differentially_expressed_proteins_df["Protein ID"].isin(outputs["differential_expressed_columns_df"]["Protein ID"]) + ] + significant_proteins_df = pd.merge(protein_df, outputs["significant_columns_df"], on="Protein ID", how="left") + significant_proteins_df = significant_proteins_df.loc[ + significant_proteins_df["Protein ID"].isin(outputs["significant_columns_df"]["Protein ID"]) + ] return dict( differentially_expressed_proteins_df=differentially_expressed_proteins_df, @@ -111,8 +113,11 @@ def mann_whitney_test_on_ptm_data( - a float corrected_alpha, containing the alpha value after application of multiple testing correction (depending on the selected multiple testing correction method corrected_alpha may be equal to alpha), - a list messages, containing messages for the user """ + + normalized_ptm_df = normalize_ptm_df(ptm_df) + output = mann_whitney_test_on_columns( - df=ptm_df, + df=normalized_ptm_df, metadata_df=metadata_df, grouping=grouping, group1=group1, diff --git a/protzilla/data_analysis/ptm_analysis.py b/protzilla/data_analysis/ptm_analysis.py index 7917699d..a3a6fd01 100644 --- a/protzilla/data_analysis/ptm_analysis.py +++ b/protzilla/data_analysis/ptm_analysis.py @@ -8,7 +8,7 @@ from protzilla.utilities.transform_dfs import long_to_wide -def filter_peptides_of_protein( +def select_peptides_of_protein( peptide_df: pd.DataFrame, protein_ids: list[str], ) -> dict: """ @@ -61,6 +61,8 @@ def ptms_per_sample(peptide_df: pd.DataFrame) -> dict: modification_df = modification_df.groupby(modification_df.columns, axis=1).sum() + modification_df["Total Amount of Peptides"] = peptide_df[["Sample", "Modifications"]].groupby(["Sample"]).size() + modification_df = modification_df.reset_index() return dict(ptm_df=modification_df) diff --git a/protzilla/methods/data_analysis.py b/protzilla/methods/data_analysis.py index 051fae01..0d10a78b 100644 --- a/protzilla/methods/data_analysis.py +++ b/protzilla/methods/data_analysis.py @@ -23,7 +23,7 @@ ) from protzilla.data_analysis.protein_graphs import peptides_to_isoform, variation_graph from protzilla.data_analysis.ptm_analysis import ( - filter_peptides_of_protein, + select_peptides_of_protein, ptms_per_protein_and_sample, ptms_per_sample, ) @@ -776,7 +776,7 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: class SelectPeptidesForProtein(DataAnalysisStep): - display_name = "Filter Peptides of Protein" + display_name = "Select Peptides of Protein" operation = "Peptide analysis" method_description = "Filter peptides for the a selected Protein of Interest from a peptide dataframe" @@ -789,7 +789,7 @@ class SelectPeptidesForProtein(DataAnalysisStep): ] def method(self, inputs: dict) -> dict: - return filter_peptides_of_protein(**inputs) + return select_peptides_of_protein(**inputs) def insert_dataframes(self, steps: StepManager, inputs) -> dict: inputs["peptide_df"] = steps.get_step_output( @@ -799,23 +799,17 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: inputs["metadata_df"] = steps.metadata_df if inputs["auto_select"]: - significant_proteins = steps.get_step_output( - DataAnalysisStep, "significant_proteins_df", inputs["protein_list"] - ) - index_of_most_significant_protein = significant_proteins[ - "corrected_p_value" - ].idxmin() - most_significant_protein = significant_proteins.loc[ - index_of_most_significant_protein - ] + significant_proteins = ( + steps.get_step_output(DataAnalysisStep, "significant_proteins_df", inputs["protein_list"])) + index_of_most_significant_protein = significant_proteins['corrected_p_value'].idxmin() + most_significant_protein = significant_proteins.loc[index_of_most_significant_protein] inputs["protein_id"] = [most_significant_protein["Protein ID"]] - self.messages.append( - { - "level": logging.INFO, - "msg": f"Selected the most significant Protein: {most_significant_protein['Protein ID']}, " - f"from {inputs['protein_list']}", - } - ) + self.messages.append({ + "level": logging.INFO, + "msg": + f"Selected the most significant Protein: {most_significant_protein['Protein ID']}, " + f"from {inputs['protein_list']}" + }) return inputs diff --git a/tests/conftest.py b/tests/conftest.py index ea8728b1..7762bd7b 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -214,6 +214,7 @@ def evidence_peptide_df(): ["Sample1", "Protein3", "SEQE", 5000000, "Unmodified", "_SEQE_", None, 0.00005, "Raw_File_1"], ["Sample1", "Protein3", "SEQF", 6000000, "Unmodified", "_SEQF_", None, 0.00006, "Raw_File_1"], ["Sample1", "Protein3", "SEQG", 7000000, "Unmodified", "_SEQG_", None, 0.00007, "Raw_File_1"], + ["Sample1", "Protein3", "SEQGG", 7000000, "Unmodified", "_SEQGG_", None, 0.00007, "Raw_File_1"], ["Sample1", "Protein4", "SEQH", 8000000, "Unmodified", "_SEQH_", None, 0.00008, "Raw_File_1"], ["Sample1", "Protein5", "SEQI", 9000000, "Unmodified", "_SEQI_", None, 0.00009, "Raw_File_1"], ["Sample2", "Protein1", "SEQJ", 10000000, "Acetyl (Protein N-term)", "_(Acetyl (Protein N-term))SEQJ_", None, 0.0001, "Raw_File_2"], diff --git a/tests/protzilla/data_analysis/test_differential_expression.py b/tests/protzilla/data_analysis/test_differential_expression.py index 3000b9e9..3ecf569e 100644 --- a/tests/protzilla/data_analysis/test_differential_expression.py +++ b/tests/protzilla/data_analysis/test_differential_expression.py @@ -72,8 +72,8 @@ def diff_expr_test_data(): def test_differential_expression_linear_model( - diff_expr_test_data, - show_figures, + diff_expr_test_data, + show_figures, ): test_intensity_df, test_metadata_df = diff_expr_test_data test_alpha = 0.05 @@ -117,8 +117,8 @@ def test_differential_expression_linear_model( assert p_values_rounded == corrected_p_values assert log2fc_rounded == log2_fc assert ( - list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) - == differentially_expressed_proteins + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == differentially_expressed_proteins ) assert current_out["corrected_alpha"] == test_alpha @@ -170,13 +170,13 @@ def test_differential_expression_student_t_test(diff_expr_test_data, show_figure assert p_values_rounded == corrected_p_values assert ( - list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) - == differentially_expressed_proteins + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == differentially_expressed_proteins ) assert current_out["corrected_alpha"] == test_alpha assert ( - list(current_out["significant_proteins_df"]["Protein ID"].unique()) - == significant_proteins + list(current_out["significant_proteins_df"]["Protein ID"].unique()) + == significant_proteins ) @@ -227,13 +227,13 @@ def test_differential_expression_welch_t_test(diff_expr_test_data, show_figures) assert p_values_rounded == corrected_p_values assert ( - list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) - == differentially_expressed_proteins + list(current_out["differentially_expressed_proteins_df"]["Protein ID"].unique()) + == differentially_expressed_proteins ) assert current_out["corrected_alpha"] == test_alpha assert ( - list(current_out["significant_proteins_df"]["Protein ID"].unique()) - == significant_proteins + list(current_out["significant_proteins_df"]["Protein ID"].unique()) + == significant_proteins ) @@ -535,27 +535,27 @@ def test_differential_expression_kruskal_wallis_on_intensity_group_handling( @pytest.fixture def ptm_test_data(): test_amount_list = ( - ["Sample1", 1, 1, 10, 1, 100], - ["Sample2", 2, 2, 10, 1, 100], - ["Sample3", 3, 3, 10, 1, 100], - ["Sample4", 4, 4, 10, 1, 100], - ["Sample5", 5, 5, 10, 1, 100], - ["Sample6", 6, 3, 11, 111, 100], - ["Sample7", 7, 4, 12, 222, 100], - ["Sample8", 8, 5, 13, 333, 100], - ["Sample9", 9, 6, 14, 444, 100], - ["Sample10", 10, 7, 15, 555, 100], - ["Sample11", 11, 5, 16, 1111, 100], - ["Sample12", 12, 6, 17, 2222, 100], - ["Sample13", 13, 7, 18, 3333, 100], - ["Sample14", 14, 8, 19, 4444, 100], - ["Sample15", 15, 9, 20, 5555, 100], + ["Sample1", 1, 1, 10, 1, 100, 100], + ["Sample2", 2, 2, 10, 1, 100, 100], + ["Sample3", 3, 3, 10, 1, 100, 100], + ["Sample4", 4, 4, 10, 1, 100, 100], + ["Sample5", 5, 5, 10, 1, 100, 100], + ["Sample6", 6, 3, 11, 111, 100, 100], + ["Sample7", 7, 4, 12, 222, 100, 100], + ["Sample8", 8, 5, 13, 333, 100, 100], + ["Sample9", 9, 6, 14, 444, 100, 100], + ["Sample10", 10, 7, 15, 555, 100, 100], + ["Sample11", 11, 5, 16, 1111, 100, 100], + ["Sample12", 12, 6, 17, 2222, 100, 100], + ["Sample13", 13, 7, 18, 3333, 100, 100], + ["Sample14", 14, 8, 19, 4444, 100, 100], + ["Sample15", 15, 9, 20, 5555, 100, 100], ) test_amount_df = pd.DataFrame( data=test_amount_list, - columns=["Sample", "Oxidation", "Acetyl", "GlyGly", "Phospho", "Unmodified"], + columns=["Sample", "Oxidation", "Acetyl", "GlyGly", "Phospho", "Unmodified", "Total Amount of Peptides"], ) test_metadata_list = ( diff --git a/tests/protzilla/data_analysis/test_differential_expression_helper.py b/tests/protzilla/data_analysis/test_differential_expression_helper.py new file mode 100644 index 00000000..d4726b8a --- /dev/null +++ b/tests/protzilla/data_analysis/test_differential_expression_helper.py @@ -0,0 +1,15 @@ +import pytest + +from protzilla.data_analysis.differential_expression_helper import normalize_ptm_df +from protzilla.data_analysis.ptm_analysis import ptms_per_sample + + +def test_normalize_ptm_df(evidence_peptide_df): + ptm_df = ptms_per_sample(evidence_peptide_df)["ptm_df"] + normalized_ptm_df = normalize_ptm_df(ptm_df) + + assert normalized_ptm_df.columns.tolist() == ["Sample", "Acetyl (Protein N-term)", "Oxidation (M)", "Unmodified"] + assert normalized_ptm_df["Sample"].tolist() == ["Sample1", "Sample2", "Sample3", "Sample4"] + assert normalized_ptm_df["Unmodified"].tolist() == [0.8, 0.8, 1.0, 1.0] + assert normalized_ptm_df["Acetyl (Protein N-term)"].tolist() == [0.2, 0.2, 0.0, 0.0] + assert normalized_ptm_df["Oxidation (M)"].tolist() == [0.1, 0.0, 0.0, 0.0] \ No newline at end of file diff --git a/tests/protzilla/data_analysis/test_filter_peptites_of_protein.py b/tests/protzilla/data_analysis/test_filter_peptites_of_protein.py index b191f335..23d739a2 100644 --- a/tests/protzilla/data_analysis/test_filter_peptites_of_protein.py +++ b/tests/protzilla/data_analysis/test_filter_peptites_of_protein.py @@ -1,10 +1,10 @@ import pytest -from protzilla.data_analysis.ptm_analysis import filter_peptides_of_protein +from protzilla.data_analysis.ptm_analysis import select_peptides_of_protein def test_filter_peptides_of_protein(peptides_df): - filtered_peptides_df = filter_peptides_of_protein(peptides_df, ["Protein2"])["peptide_df"] + filtered_peptides_df = select_peptides_of_protein(peptides_df, ["Protein2"])["peptide_df"] assert len(filtered_peptides_df) == 6 assert filtered_peptides_df["Sequence"].tolist() == [ diff --git a/tests/protzilla/data_analysis/test_peptide_analysis.py b/tests/protzilla/data_analysis/test_peptide_analysis.py index b2848a65..8e87de88 100644 --- a/tests/protzilla/data_analysis/test_peptide_analysis.py +++ b/tests/protzilla/data_analysis/test_peptide_analysis.py @@ -1,14 +1,14 @@ import pytest -from protzilla.data_analysis.ptm_analysis import filter_peptides_of_protein, ptms_per_sample, \ +from protzilla.data_analysis.ptm_analysis import select_peptides_of_protein, ptms_per_sample, \ ptms_per_protein_and_sample @pytest.mark.parametrize("df_num", [0, 1]) -def test_filter_peptides_of_protein(peptides_df, evidence_peptide_df, df_num): +def test_select_peptides_of_protein(peptides_df, evidence_peptide_df, df_num): peptide_df = [peptides_df, evidence_peptide_df][df_num] - filtered_peptides_df = filter_peptides_of_protein(peptide_df, ["Protein2"])["peptide_df"] + filtered_peptides_df = select_peptides_of_protein(peptide_df, ["Protein2"])["peptide_df"] assert len(filtered_peptides_df) == 6 assert filtered_peptides_df["Sequence"].tolist() == [ @@ -20,11 +20,12 @@ def test_filter_peptides_of_protein(peptides_df, evidence_peptide_df, df_num): def test_ptms_per_sampel(evidence_peptide_df): ptm_df = ptms_per_sample(evidence_peptide_df)["ptm_df"] - assert ptm_df.columns.tolist() == ["Sample", "Acetyl (Protein N-term)", "Oxidation (M)", "Unmodified"] + assert ptm_df.columns.tolist() == ["Sample", "Acetyl (Protein N-term)", "Oxidation (M)", "Unmodified", "Total Amount of Peptides"] assert ptm_df["Sample"].tolist() == ["Sample1", "Sample2", "Sample3", "Sample4"] - assert ptm_df["Unmodified"].tolist() == [7, 4, 5, 4] + assert ptm_df["Unmodified"].tolist() == [8, 4, 5, 4] assert ptm_df["Acetyl (Protein N-term)"].tolist() == [2, 1, 0, 0] assert ptm_df["Oxidation (M)"].tolist() == [1, 0, 0, 0] + assert ptm_df["Total Amount of Peptides"].tolist() == [10, 5, 5, 4] def test_ptms_per_protein_and_sample(evidence_peptide_df): @@ -37,7 +38,7 @@ def test_ptms_per_protein_and_sample(evidence_peptide_df): assert (ptm_df["Protein2"].tolist() == ["(2) Acetyl (Protein N-term), (1) Oxidation (M), (1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, "]) assert (ptm_df["Protein3"].tolist() == - ["(3) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, "]) + ["(4) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, "]) assert (ptm_df["Protein4"].tolist() == ["(1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, ", "(1) Unmodified, "]) assert (ptm_df["Protein5"].tolist() == diff --git a/ui/runs/forms/data_analysis.py b/ui/runs/forms/data_analysis.py index e575abe5..b890dd5b 100644 --- a/ui/runs/forms/data_analysis.py +++ b/ui/runs/forms/data_analysis.py @@ -1,5 +1,7 @@ +import logging from enum import Enum, StrEnum +from protzilla.methods.data_preprocessing import DataPreprocessingStep from protzilla.methods.data_analysis import ( DataAnalysisStep, DifferentialExpressionLinearModel, @@ -1145,9 +1147,7 @@ def fill_form(self, run: Run) -> None: if chosen_list == "all proteins": self.fields["protein_ids"].choices = fill_helper.to_choices( - run.steps.get_step_output( - Step, "protein_df" - )["Protein ID"].unique() + run.steps.get_step_output(Step, "protein_df")["Protein ID"].unique() ) else: if self.data.get("sort_proteins"): @@ -1179,9 +1179,7 @@ def fill_form(self, run: Run) -> None: ) self.fields["peptide_df"].choices = fill_helper.to_choices(single_protein_peptides) - self.fields["peptide_df"].choices = fill_helper.get_choices( - run, "peptide_df" - ) + self.fields["peptide_df"].choices = fill_helper.get_choices(run, "peptide_df")[::-1] single_protein_peptides = run.steps.get_instance_identifiers( SelectPeptidesForProtein, "peptide_df" From c4da33e77ed1bdce36f97488cd8c0dd80daf3b72 Mon Sep 17 00:00:00 2001 From: JanniRoebbecke <50395935+JanniRoebbecke@users.noreply.github.com> Date: Mon, 11 Nov 2024 16:55:02 +0100 Subject: [PATCH 22/22] Enable volcano plot with ptm data (#530) * tests for filter_proteins with peptide file * tests for filter_proteins with peptide file, better mock dataframe * output types * added testing that only filtered Proteins are removed from peptides_df * tests for filter_samples with peptide file * extract test that peptide filtering matches protein filtering into Method * refactor: shortened tests by integrating extracted method and mock data * exclude peptides * tests * minor fixes in test * minor fixes in test * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * fix test that peptide filtering matches protein filtering Method * match mock protein dataframe to mock peptide dataframe * extract test that peptide filtering matches protein filtering into Method * implementation * tests * tests improve error logging * tests git testing * peptide import added missing column * implements form, form_mapping, method and the actual functionality (untested) * merge peptide filtering and outlier_detection * merge peptide transformation * implement passing of peptide data between preprocessing steps * complete implement passing of peptide data between preprocessing steps * implement overhead for new step evidence import * enable selecting multiple proteins of interest and implements tests * Fix EvidenceImport class (now the right method is called) * implement option to choose protein from list of significant proteins * implement option to let the system choose the most expressed protein * include messages for peptide filtering * remove selecting multiple proteins and add substring search * add cleaning of protein groups to evidence import * implement suggested changes: remove default values and correct grammar mistake * make preprocessing work without peptide_df * clean up * add cleaning of protein groups to evidence import * rename * clean Protein IDs for peptide_import and name intensity column Intensity * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used * implement requested changes: make peptide_df input optional for all preprocessing steps, remove peptide_df input in preprocessing steps, where it is not used * unify into one method * cleanup old names * cleanup names * cleanup names * setup form and method * small refactor: utilize fill_helper better * implemented method * cleanup rename and docstring * tests and named sample column from "0" to "Sample" * implemented slow version * enable selecting multiple proteins * complete merge * rename file and fix selecting multiple proteins * improve efficiency of methods ptms_per_sample and ptms_per_protein_and_sample * added test for ptms_per_protein_and_sample * complete merge (fix testes after merge) * cleanup * implement differential expression with mann-whitney-test on ptm data * implement differential expression with mann-whitney-test on protein data * refactor input and output handling for Mann-Whitney-test on ptm data * add additional parameter form user and docstrings * fix typo * add test for mann whitney on intensity data * add test for mann whitney on ptm data * add mann whitney to volcano plot form * implement kruskal wallis test on ptm data * implement kruskal wallis test on protein data * adds tests for kruskal wallis test * implement normalization * adapt tests * add unit test for ptm_df normalization * change mock files as to not upset other tests * enable volcano plot with ptm data * make names of select peptides for Protein Step consistent * simplify form and make names more broad * Names of import section * Names of import section * Add description of Volcano Plot * complete merge --------- Co-authored-by: janni.roebbecke --- protzilla/data_analysis/plots.py | 36 ++++++++------- protzilla/methods/data_analysis.py | 44 ++++++++++++++++++- protzilla/methods/importing.py | 20 ++++----- protzilla/runner.py | 2 +- protzilla/steps.py | 9 ++-- .../data_analysis/test_analysis_plots.py | 2 +- ui/runs/forms/data_analysis.py | 27 ++++++++---- 7 files changed, 99 insertions(+), 41 deletions(-) diff --git a/protzilla/data_analysis/plots.py b/protzilla/data_analysis/plots.py index 439d458f..84ae2b59 100644 --- a/protzilla/data_analysis/plots.py +++ b/protzilla/data_analysis/plots.py @@ -93,7 +93,8 @@ def create_volcano_plot( alpha: float, group1: str, group2: str, - proteins_of_interest: list | None = None, + item_type: str = "Protein ID", + items_of_interest: list | None = None, ) -> dict: """ Function to create a volcano plot from p values and log2 fold change with the @@ -105,12 +106,13 @@ def create_volcano_plot( :param alpha: the alpha value for the significance line :param group1: the name of the first group :param group2: the name of the second group - :param proteins_of_interest: the proteins that should be annotated in the plot + :param item_type: in ["Protein", "PTM"] the type of the items in the data + :param items_of_interest: the items that should be annotated in the plot :return: returns a dictionary containing a list with a plotly figure and/or a list of messages """ - plot_df = p_values.join(log2_fc.set_index("Protein ID"), on="Protein ID") + plot_df = p_values.join(log2_fc.set_index(item_type), on=item_type) fig = dashbio.VolcanoPlot( dataframe=plot_df, effect_size="log2_fold_change", @@ -122,30 +124,30 @@ def create_volcano_plot( xlabel=f"log2(fc) ({group2} / {group1})", ylabel="-log10(p)", title="Volcano Plot", - annotation="Protein ID", + annotation=item_type, plot_bgcolor=colors["plot_bgcolor"], xaxis_gridcolor=colors["gridcolor"], yaxis_gridcolor=colors["gridcolor"], ) - if proteins_of_interest is None: - proteins_of_interest = [] - elif not isinstance(proteins_of_interest, list): - proteins_of_interest = [proteins_of_interest] + if items_of_interest is None: + items_of_interest = [] + elif not isinstance(items_of_interest, list): + items_of_interest = [items_of_interest] - # annotate the proteins of interest permanently in the plot - for protein in proteins_of_interest: + # annotate the items of interest permanently in the plot + for item in items_of_interest: fig.add_annotation( x=plot_df.loc[ - plot_df["Protein ID"] == protein, + plot_df[item_type] == item, "log2_fold_change", ].values[0], y=-np.log10( plot_df.loc[ - plot_df["Protein ID"] == protein, + plot_df[item_type] == item, "corrected_p_value", ].values[0] ), - text=protein, + text=item, showarrow=True, arrowhead=1, font=dict(color=colors["annotation_text_color"]), @@ -158,8 +160,8 @@ def create_volcano_plot( ) new_names = { - "Point(s) of interest": "Significant Proteins", - "Dataset": "Not Significant Proteins", + "Point(s) of interest": f"Significant {item_type}s", + "Dataset": f"Not Significant {item_type}s", } fig.for_each_trace( @@ -170,11 +172,11 @@ def create_volcano_plot( ) fig.update_traces( marker=dict(color=PROTZILLA_DISCRETE_COLOR_SEQUENCE[2]), - selector=dict(name="Significant Proteins"), + selector=dict(name=f"Significant {item_type}s"), ) fig.update_traces( marker=dict(color=PROTZILLA_DISCRETE_COLOR_SEQUENCE[0]), - selector=dict(name="Not Significant Proteins"), + selector=dict(name=f"Not Significant {item_type}s"), ) return dict(plots=[fig]) diff --git a/protzilla/methods/data_analysis.py b/protzilla/methods/data_analysis.py index 0d10a78b..511629f5 100644 --- a/protzilla/methods/data_analysis.py +++ b/protzilla/methods/data_analysis.py @@ -14,6 +14,8 @@ mann_whitney_test_on_intensity_data, mann_whitney_test_on_ptm_data) from protzilla.data_analysis.differential_expression_t_test import t_test from protzilla.data_analysis.dimension_reduction import t_sne, umap +from protzilla.data_analysis.ptm_analysis import ptms_per_sample, \ + ptms_per_protein_and_sample, select_peptides_of_protein from protzilla.data_analysis.model_evaluation import evaluate_classification_model from protzilla.data_analysis.plots import ( clustergram_plot, @@ -229,6 +231,37 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: return inputs +class DifferentialExpressionKruskalWallisOnIntensity(DataAnalysisStep): + display_name = "Kruskal-Wallis Test" + operation = "differential_expression" + method_description = ("A function to conduct a Kruskal-Wallis test between groups defined in the clinical data." + "The p-values are corrected for multiple testing.") + + input_keys = [ + "protein_df", + "metadata_df", + "grouping", + "selected_groups", + "alpha", + "log_base", + "multiple_testing_correction_method", + ] + output_keys = [ + "differentially_expressed_proteins_df", + "significant_proteins_df", + "corrected_p_values_df", + "corrected_alpha", + ] + + def method(self, inputs: dict) -> dict: + return kruskal_wallis_test_on_intensity_data(**inputs) + + def insert_dataframes(self, steps: StepManager, inputs) -> dict: + inputs["ptm_df"] = steps.get_step_output(Step, "ptm_df", inputs["ptm_df"]) + inputs["metadata_df"] = steps.metadata_df + return inputs + + class DifferentialExpressionKruskalWallisOnIntensity(DataAnalysisStep): display_name = "Kruskal-Wallis Test" operation = "differential_expression" @@ -294,13 +327,17 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: class PlotVolcano(PlotStep): display_name = "Volcano Plot" operation = "plot" + method_description = ("Plots the results of a differential expression analysis in a volcano plot. The x-axis shows " + "the log2 fold change and the y-axis shows the -log10 of the corrected p-values. The user " + "can define a fold change threshold and an alpha level to highlight significant items.") input_keys = [ "p_values", "fc_threshold", "alpha", "group1", "group2", - "proteins_of_interest", + "item_type", + "items_of_interest", "log2_fc", ] output_keys = [] @@ -323,6 +360,11 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: Step, "log2_fold_change_df", inputs["input_dict"] ) + if step.operation == "differential_expression": + inputs["item_type"] = "Protein ID" + elif step.operation == "Peptide analysis": + inputs["item_type"] = "PTM" + return inputs diff --git a/protzilla/methods/importing.py b/protzilla/methods/importing.py index 7cde1ba0..c91b40fc 100644 --- a/protzilla/methods/importing.py +++ b/protzilla/methods/importing.py @@ -25,9 +25,9 @@ def insert_dataframes(self, steps: StepManager, inputs) -> dict: class MaxQuantImport(ImportingStep): - display_name = "MaxQuant" - operation = "msdataimport" - method_description = "Import MaxQuant data" + display_name = "MaxQuant Protein Groups Import" + operation = "Protein Data Import" + method_description = "Import the protein groups file form output of MaxQuant" input_keys = ["file_path", "map_to_uniprot", "intensity_name", "aggregation_method"] output_keys = ["protein_df"] @@ -37,8 +37,8 @@ def method(self, inputs): class DiannImport(ImportingStep): - display_name = "DIA-NN" - operation = "msdataimport" + display_name = "DIA-NN Import" + operation = "Protein Data Import" method_description = "DIA-NN data import" input_keys = ["file_path", "map_to_uniprot", "aggregation_method"] @@ -49,9 +49,9 @@ def method(self, inputs): class MsFraggerImport(ImportingStep): - display_name = "MS Fragger" - operation = "msdataimport" - method_description = "MS Fragger data import" + display_name = "MS Fragger Combined Protein Import" + operation = "Protein Data Import" + method_description = "Import the combined_protein.tsv file form output of MS Fragger" input_keys = ["file_path", "intensity_name", "map_to_uniprot", "aggregation_method"] output_keys = ["protein_df"] @@ -119,7 +119,7 @@ def insert_dataframes(self, steps: StepManager, inputs: dict) -> dict: class PeptideImport(ImportingStep): - display_name = "Peptide import" + display_name = "MaxQuant Peptide Import" operation = "peptide_import" method_description = "Import peptide data" @@ -131,7 +131,7 @@ def method(self, inputs): class EvidenceImport(ImportingStep): - display_name = "Evidence import" + display_name = "MaxQuant Evidence Import" operation = "peptide_import" method_description = "Import an evidence file" diff --git a/protzilla/runner.py b/protzilla/runner.py index 50cbf127..fa206a19 100644 --- a/protzilla/runner.py +++ b/protzilla/runner.py @@ -96,7 +96,7 @@ def compute_workflow(self): self.run._run_write() def _insert_commandline_inputs(self, step): - if step.operation == "msdataimport": + if step.operation == "Protein Data Import": step.form_inputs["file_path"] = self.ms_data_path elif step.operation == "metadataimport": diff --git a/protzilla/steps.py b/protzilla/steps.py index d5fb124e..eecf0d04 100644 --- a/protzilla/steps.py +++ b/protzilla/steps.py @@ -353,17 +353,20 @@ def all_steps(self) -> list[Step]: ) def get_instance_identifiers( - self, step_type: type[Step], output_key: str = None + self, step_type: type[Step], output_key: str | list[str] = None ) -> list[str]: + if isinstance(output_key, str): + output_key = [output_key] + instance_identifiers = [ step.instance_identifier for step in self.all_steps if isinstance(step, step_type) - and (output_key is None or output_key in step.output) + and (output_key is None or all(k in step.output for k in output_key)) ] if not instance_identifiers: logging.warning( - f"No instance identifiers found for {step_type} and output_key {output_key}" + f"No instance identifiers found with step type {step_type} and output_key{'s' if len(output_key) > 1 else ''} {output_key}" ) return instance_identifiers diff --git a/tests/protzilla/data_analysis/test_analysis_plots.py b/tests/protzilla/data_analysis/test_analysis_plots.py index 3b665b0b..eca37a85 100644 --- a/tests/protzilla/data_analysis/test_analysis_plots.py +++ b/tests/protzilla/data_analysis/test_analysis_plots.py @@ -89,7 +89,7 @@ def test_plots_volcano_plot_multiple_annotations(ttest_input, ttest_output, show log2_fc=ttest_output["log2_fold_change_df"], fc_threshold=0, alpha=0, - proteins_of_interest=["Protein1", "Protein2"], + items_of_interest=["Protein1", "Protein2"], group1=ttest_input["group1"], group2=ttest_input["group2"], ) diff --git a/ui/runs/forms/data_analysis.py b/ui/runs/forms/data_analysis.py index b890dd5b..d16145b4 100644 --- a/ui/runs/forms/data_analysis.py +++ b/ui/runs/forms/data_analysis.py @@ -7,8 +7,10 @@ DifferentialExpressionLinearModel, DifferentialExpressionTTest, DimensionReductionUMAP, + DataAnalysisStep, PTMsPerSample, SelectPeptidesForProtein, + DifferentialExpressionMannWhitneyOnPTM, ) from protzilla.methods.data_preprocessing import DataPreprocessingStep from protzilla.run import Run @@ -456,16 +458,15 @@ class PlotVolcanoForm(MethodForm): fc_threshold = CustomNumberField( label="Log2 fold change threshold", min_value=0, initial=0 ) - proteins_of_interest = CustomMultipleChoiceField( + items_of_interest = CustomMultipleChoiceField( choices=[], - label="Proteins of interest (will be highlighted)", + label="Items of interest (will be highlighted)", ) def fill_form(self, run: Run) -> None: self.fields["input_dict"].choices = fill_helper.to_choices( run.steps.get_instance_identifiers( - DifferentialExpressionTTest | DifferentialExpressionLinearModel | DifferentialExpressionMannWhitneyOnIntensityForm, - "differentially_expressed_proteins_df", + Step, ["corrected_p_values_df", "log2_fold_change_df"], ) ) @@ -473,11 +474,19 @@ def fill_form(self, run: Run) -> None: "input_dict", self.fields["input_dict"].choices[0][0] ) - proteins = run.steps.get_step_output( + items_of_interest = [] + step_output = run.steps.get_step_output( Step, "differentially_expressed_proteins_df", input_dict_instance_id - )["Protein ID"].unique() + ) + if step_output is not None: + items_of_interest = step_output["Protein ID"].unique() + step_output = run.steps.get_step_output( + Step, "differentially_expressed_ptm_df", input_dict_instance_id + ) + if step_output is not None: + items_of_interest = step_output["PTM"].unique() - self.fields["proteins_of_interest"].choices = fill_helper.to_choices(proteins) + self.fields["items_of_interest"].choices = fill_helper.to_choices(items_of_interest) class PlotScatterPlotForm(MethodForm): @@ -1179,7 +1188,9 @@ def fill_form(self, run: Run) -> None: ) self.fields["peptide_df"].choices = fill_helper.to_choices(single_protein_peptides) - self.fields["peptide_df"].choices = fill_helper.get_choices(run, "peptide_df")[::-1] + self.fields["peptide_df"].choices = fill_helper.get_choices( + run, "peptide_df" + )[::-1] single_protein_peptides = run.steps.get_instance_identifiers( SelectPeptidesForProtein, "peptide_df"