diff --git a/protzilla/data_analysis/protein_coverage.py b/protzilla/data_analysis/protein_coverage.py
index 35bc0516..6e4fb04c 100644
--- a/protzilla/data_analysis/protein_coverage.py
+++ b/protzilla/data_analysis/protein_coverage.py
@@ -1,9 +1,24 @@
+from colour import color_scale
 from docutils.nodes import title
 from tqdm import tqdm
 from protzilla.constants.paths import EXTERNAL_DATA_PATH
 from protzilla.disk_operator import PickleOperator
+from dataclasses import dataclass
 import pandas as pd
+from numpy import log2
 import plotly.graph_objects as go
+
+@dataclass(unsafe_hash=True)
+class PeptideMatch:
+
+    peptide_sequence: str
+    start_location_on_protein: int
+    end_location_on_protein: int
+    intensity: float = 0.0
+    sample: str = ""
+
+
+
 def build_kmer_dictionary(protein_dictionary: dict[str, str], k: int = 5) -> dict[str, list[tuple[str, int]]]:
     """
     Builds a dictionary of all kmers in a list of protein sequences. The kmers are generated by sliding a window of size
@@ -16,9 +31,10 @@ def build_kmer_dictionary(protein_dictionary: dict[str, str], k: int = 5) -> dic
         kmer_dict = PickleOperator.read(kmer_dict_path)
         return kmer_dict
     kmer_dict = {}
-    for protein_id, protein_sequence in tqdm(protein_dictionary.items(), desc="Building kmer dictionary", unit_scale=True, unit="protein"):
+    for protein_id, protein_sequence in tqdm(protein_dictionary.items(), desc="Building kmer dictionary",
+                                             unit_scale=True, unit="protein"):
         for i in range(len(protein_sequence) - k + 1):
-            kmer = protein_sequence[i : i + k]
+            kmer = protein_sequence[i: i + k]
             if kmer in kmer_dict:
                 kmer_dict[kmer].append((protein_id, i))
             else:
@@ -28,7 +44,8 @@ def build_kmer_dictionary(protein_dictionary: dict[str, str], k: int = 5) -> dic
 
 
 def match_peptide_to_protein_ids(
-    peptide_sequence: str, protein_kmer_dictionary : dict[str, list[tuple[str, int]]], protein_dictionary: dict[str, str]
+        peptide_sequence: str, protein_kmer_dictionary: dict[str, list[tuple[str, int]]],
+        protein_dictionary: dict[str, str]
 ) -> list[tuple[str, int, int]]:
     """
     Matches a peptide sequence to a dictionary of kmers in protein sequences.
@@ -36,7 +53,7 @@ def match_peptide_to_protein_ids(
     """
     # convert the peptide sequence to a list of kmers
     k = 5
-    kmers = [peptide_sequence[i : i + k] for i in range(len(peptide_sequence) - k + 1)]
+    kmers = [peptide_sequence[i: i + k] for i in range(len(peptide_sequence) - k + 1)]
     # for now, we only do exact matches, so only the first and last kmer of the peptide are needed
     first_kmer = kmers[0]
     last_kmer = kmers[-1]
@@ -58,90 +75,170 @@ def match_peptide_to_protein_ids(
             subsequence = protein_sequence[start_first_kmer:end_last_kmer]
             if subsequence != peptide_sequence:
                 continue
-            assert len(subsequence) == len(peptide_sequence), f"Lengths do not match: {len(subsequence)} != {len(peptide_sequence)}"
+            assert len(subsequence) == len(
+                peptide_sequence), f"Lengths do not match: {len(subsequence)} != {len(peptide_sequence)}"
             assert subsequence in peptide_sequence, f"Subsequence not in peptide sequence:\nA: {subsequence}\nB: {peptide_sequence}"
             assert peptide_sequence in protein_sequence, f"Peptide not in protein sequence: {peptide_sequence} not in {protein_sequence}"
             hits.append((protein_id, start_first_kmer, end_last_kmer))
     return hits
 
 
-
 def plot_protein_coverage(
-    fasta_df: pd.DataFrame, peptide_df: pd.DataFrame, protein_id: str
+        fasta_df: pd.DataFrame, peptide_df: pd.DataFrame, protein_id: str, samples: list[str] = None
 ) -> None:
     """
     Plots the coverage of a protein sequence by peptides.
     """
     # generate the protein kmer dictionary
+    if samples is None:
+        samples = []
+    reduced_peptide_df = peptide_df[peptide_df["Sample"].isin(samples) & peptide_df['Intensity'] > 0]
+
     protein_dict = dict(zip(fasta_df["Protein ID"], fasta_df["Protein Sequence"]))
     kmer_dict = build_kmer_dictionary(protein_dict, k=5)
 
     protein_sequence = protein_dict[protein_id]
     protein_sequence_length = len(protein_sequence)
     peptide_matches = []
+    coverage = [0] * protein_sequence_length
 
-    for peptide_sequence in tqdm(peptide_df['Sequence'].unique(), desc="Matching peptides to protein", unit_scale=True, unit="peptide"):
-        protein_hits = match_peptide_to_protein_ids(peptide_sequence=peptide_sequence, protein_kmer_dictionary=kmer_dict, protein_dictionary=protein_dict)
+    for sample, peptide_sequence, intensity in tqdm(
+            reduced_peptide_df[['Sample', 'Sequence', 'Intensity']].drop_duplicates().itertuples(index=False),
+            desc="Matching peptides to protein", unit_scale=True,
+            unit="peptide"):
+        protein_hits = match_peptide_to_protein_ids(peptide_sequence=peptide_sequence,
+                                                    protein_kmer_dictionary=kmer_dict, protein_dictionary=protein_dict)
         for prot_id, start_location_on_protein, end_location_on_protein in protein_hits:
             # we are only interested in the provided protein id, everything else is discarded
             if prot_id != protein_id:
                 continue
             # add the peptide sequence and location to the peptide matches pertaining to the protein id
-            peptide_matches.append((peptide_sequence, start_location_on_protein, end_location_on_protein))
+            peptide_matches.append(
+                PeptideMatch(peptide_sequence=peptide_sequence,
+                             start_location_on_protein=start_location_on_protein,
+                             end_location_on_protein=end_location_on_protein,
+                             intensity=log2(intensity),
+                             sample=sample)
+            )
+            # update the coverage of the protein sequence
+            coverage[start_location_on_protein:end_location_on_protein] = \
+                [coverage + 1 for coverage in coverage[start_location_on_protein:end_location_on_protein]]
 
+    if len(peptide_matches) == 0:
+        raise ValueError(f"No peptides matched for protein {protein_id}")
     # now that all the matches have been determined with their start and end on the protein sequence, we need to find
     # an optimal solution for the location of their rectangles in the plot without overlap while minimizing the
     # required number of rows (vertical space)
-    rows_of_peptide_matches = distribute_to_rows(peptide_matches)
-
-    x_labels = [f"{i} ({protein_sequence[i - 1]})" for i in range(1, protein_sequence_length + 1)]
-    fig = go.Figure(data=go.Bar(x=x_labels, y=[1] * protein_sequence_length,
-                                name="Protein Sequence", marker=dict(color="lightgray")), )
-    for row_index, row in enumerate(rows_of_peptide_matches):
-        for start_location_on_protein, end_location_on_protein, peptide_sequence in row:
-            print(f"Peptide: {peptide_sequence}, start: {start_location_on_protein}, end: {end_location_on_protein}")
-            # add a rectangle for each peptide
+    rows = distribute_to_rows(peptide_matches)
+
+    x_labels = [f"{amino_acid_index} ({amino_acid})" for amino_acid_index, amino_acid in enumerate(protein_sequence)]
+    max_coverage_in_sequence = max(coverage)
+    max_intensity = max([peptide_match.intensity for peptide_match in peptide_matches])
+    normalized_coverage = [value / max_coverage_in_sequence for value in coverage]
+    hover_text = [f"Position: {i}<br>Amino acid: {amino_acid}<br>Coverage: {coverage}"
+                  for i, (coverage, amino_acid) in
+                  enumerate(zip(normalized_coverage, protein_sequence))]
+
+    fig = go.Figure(data=go.Bar(x=x_labels,
+                                y=normalized_coverage,
+                                marker=dict(
+                                    color=normalized_coverage,
+                                    colorscale='Bluered',
+                                    colorbar=dict(title="Coverage<br>(normalized)"),
+                                    line=dict(width=0)
+                                ),
+                                hovertext=hover_text,
+                                hoverinfo="text",
+                                showlegend=False,
+                                name="Coverage (normalized to max in Protein Sequence)"))
+
+    # Keep track of the current row position across all samples
+    current_row = 1
+
+    # Iterate through samples and their rows in order
+    for sample_idx, (sample, sample_rows) in enumerate(rows.items()):
+        # Add a sample label
+        fig.add_annotation(
+            x=-0.1,  # slightly to the left of the plot
+            y=current_row + len(sample_rows) / 2,
+            text=sample,
+            showarrow=False,
+            textangle=-90,
+            xref='paper',
+            yref='y'
+        )
+
+        for row_index, row in enumerate(sample_rows):
+            for peptide_match in row:
+                # add a rectangle for each peptide
+                x0, x1 = peptide_match.start_location_on_protein, peptide_match.end_location_on_protein
+                y0, y1 = current_row, current_row + 1
+                normalized_intensity = (peptide_match.intensity) / max_intensity
+                fig.add_shape(
+                    type="rect",
+                    # the coordinates need to be a bit offset, as otherwise the rectangles would start in the middle of the bars
+                    x0=x0 - 0.5, y0=y0,
+                    x1=x1 - 0.5, y1=y1,
+                    fillcolor=f"rgba({int(255 * (1 - normalized_intensity))}, 0, {int(255 * normalized_intensity)}, 0.5)",
+                    opacity=0.5,
+                    layer="above",
+                )
+                # add invisible plotly object to the rectangle to show the peptide sequence when hovered over
+                fig.add_trace(go.Scatter(
+                    x=x_labels[peptide_match.start_location_on_protein:peptide_match.end_location_on_protein + 1],
+                    y=[(y0 + y1) / 2] * len(peptide_match.peptide_sequence),
+                    text=[f"Sample: {sample}<br>Peptide: {peptide_match.peptide_sequence}<br>"
+                          f"({peptide_match.start_location_on_protein}-{peptide_match.end_location_on_protein})<br>"
+                          f"Intensity: {peptide_match.intensity}"] * len(peptide_match.peptide_sequence),
+                    mode="markers",
+                    marker=dict(opacity=0, size=30),  # make marker invisible
+                    hoverinfo="text",
+                    hovertemplate="%{text}<extra></extra>",
+                    showlegend=False,
+                ))
+
+            # Move to next row
+            current_row += 1
+
+        # Add a horizontal line separator between samples (except after the last sample)
+        if sample_idx < len(rows) - 1:
             fig.add_shape(
-                type="rect",
-                x0=start_location_on_protein,
-                y0=row_index+1 + 0.1,
-                x1=end_location_on_protein,
-                y1=row_index+ 2 - 0.1,
-                fillcolor="blue",
-                opacity=0.5,
-                layer="above",
+                type="line",
+                x0=-0.5,
+                y0=current_row,
+                x1=len(protein_sequence) - 0.5,
+                y1=current_row,
+                line=dict(
+                    color="Gray",
+                    width=2,
+                    dash="solid",
+                )
             )
-            # add invisible plotly object to the rectangle to show the peptide sequence when hovered over
-            fig.add_trace(go.Scatter(
-                x=[x_labels[(start_location_on_protein + end_location_on_protein) // 2]],
-                y=[row_index + 1.5],
-                text=[f"{peptide_sequence}<br>({start_location_on_protein}-{end_location_on_protein})"],
-                mode="markers",
-                marker=dict(opacity=0, size=20),  # make marker invisible
-                hoverinfo="text",
-                hovertemplate="%{text}<extra></extra>",
-                showlegend=False,
-            ))
 
     fig.update_layout(
         title=f"Protein Coverage of {protein_id}",
         xaxis_title="Protein Sequence",
-        yaxis=dict(visible=False, range=[0, 1+len(rows_of_peptide_matches)+1], title=""),
+        yaxis=dict(visible=False, range=[0, 1 + current_row], title=""),
         showlegend=False,
+        bargap=0
     )
 
     return dict(plots=[fig])
 
 
-def distribute_to_rows(coverage):
-    coverage.sort(key=lambda x: x[1])
-    rows = []
-    for peptide_sequence, start_location, end_location in coverage:
+def distribute_to_rows(peptide_matches: list[PeptideMatch]) -> dict[list[list[PeptideMatch]]]:
+    """
+    Distributes peptide matches to rows such that no two peptides overlap in the same row.
+    Greedy algorithm (see Interval Scheduling Problem).
+    """
+    peptide_matches.sort(key=lambda peptide_match: peptide_match.start_location_on_protein)
+    rows = {sample : [] for sample in set([peptide_match.sample for peptide_match in peptide_matches])}
+    for peptide_match in peptide_matches:
         # find the first row that does not overlap with the current peptide
-        for row in rows:
-            if row[-1][1] <= start_location:
-                row.append((start_location, end_location, peptide_sequence))
+        for row in rows[peptide_match.sample]:
+            if row[-1].end_location_on_protein < peptide_match.start_location_on_protein:
+                row.append((peptide_match))
                 break
         else:
-            rows.append([(start_location, end_location, peptide_sequence)])
+            rows[peptide_match.sample].append([(peptide_match)])
     return rows
diff --git a/protzilla/methods/data_analysis.py b/protzilla/methods/data_analysis.py
index ab8bdc27..765b3047 100644
--- a/protzilla/methods/data_analysis.py
+++ b/protzilla/methods/data_analysis.py
@@ -792,6 +792,7 @@ class PlotProteinCoverage(PlotStep):
         "protein_id",
         "fasta_df",
         "peptide_df",
+        "samples"
     ]
     output_keys = []
 
diff --git a/ui/runs/forms/data_analysis.py b/ui/runs/forms/data_analysis.py
index be9ea58f..38c8668e 100644
--- a/ui/runs/forms/data_analysis.py
+++ b/ui/runs/forms/data_analysis.py
@@ -1103,6 +1103,11 @@ class PlotProteinCoverageForm(MethodForm):
         choices=[],
         label="Protein ID",
     )
+    samples = CustomMultipleChoiceField(
+        choices=[],
+        label="Samples",
+    )
+
 
     def fill_form(self, run: Run) -> None:
         self.fields["peptide_df_instance"].choices = fill_helper.get_choices(
@@ -1124,9 +1129,12 @@ def fill_form(self, run: Run) -> None:
             Step, "fasta_df", fasta_df_instance_id
         )["Protein ID"].unique()
         peptide_df_protein_ids = peptide_df["Protein ID"].unique()
-        common_protein_ids = list(set(fasta_protein_ids) & set(peptide_df_protein_ids))
+        common_protein_ids = sorted(list(set(fasta_protein_ids) & set(peptide_df_protein_ids)))
         self.fields["protein_id"].choices = fill_helper.to_choices(common_protein_ids)
 
+        peptide_df_samples = peptide_df["Sample"].unique()
+        self.fields["samples"].choices = fill_helper.to_choices(peptide_df_samples)
+
 
 class FLEXIQuantLFForm(MethodForm):
     is_dynamic = True