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Protein_Project - runs with Ubuntu 16.04.2 LTS (GNU/Linux 4.4.0-66-generic x86_64), python 3 and modules of the versions from January 2017

by Ryno Lawson

Place protein seq to be predicted into the folder:~/Documents/Protein_Project/newproject/src/Predictor/

Read below

Predictor script:

  1. predictor.py - Is a working training file and predictor file(NB!FULL DATASET- see predictor_small_dataset.py for a smaller training dataset and functional 3fold hard coded predictor combo). This is hard coded to receive my mainset, train a linear svm model and call in a file to be predicted Run the predictor.py script this will take some time as the training of the model and the predictor is combined.

Main dataset: ~/Documents/Protein_Project/newproject/data/null_dataset/membrane-alpha.3line.txt

Script folder:~/Documents/Protein_Project/newproject/src/

Predictor script:

  1. predictor.py - Is a working training file and predictor file. This is hard coded to receive my mainset, train a linear svm model and call in a file to be predicted

Training FolderScripts:

  1. parse.py #Runs parse.py with hard coded dataset file - separates IDs, sequences and features.

  2. protein cross.py and cross_valid.py - held my protein level cross validation code.
    #Runs protein_cross function in the cross_valid.py makes 4 training and 1 test dataset file plus extra combo files

  3. training_exe_input.py # Is one of the better scripts for encoding(built on the encoding_file.py- which is where the dynamic windows and encoding was first coded), training svm linear and predicting on. Similiar scripts such as kfold_training_exe.py(tried to loop through varying window size but and new_rbf.py are modifications of the svm but gear towards rbf or automating my training of different window sizes for the linear_svm(kfold_training)

4.Other python or bash script served as to either create a pipeline between scripts or are templates of code which was write and didnt function. svm_learning.py and svm_functions.py held the sklearn code to be implemented

#Runs encoding function in encoding.py - training and test files will be looped through here

In Scripts in Psiblast:

Some of the psiblast scripts I wrote for parsing the fasta files, and running psiblast.

Data folder:

Sorted either by file format(fasta,txt,etc... or stage of processing(cross validating,encoding, etc...)

Result folder: ~/Documents/Protein_Project/newproject/results/

Some training and predictiing results from my svm linear with accuracies and confusion matrix. Some varying window sizes with the kfold files.

Ignore

#| bash training_file_list.sh #Runs training_file_list.sh to create a .txt list of file names for training and test function inputs

This pipeline shell script forms the backbone of the prediction model and runs through the training of the prediction model and quality testing

python cross_valid.py #| bash training_file_list.sh #Runs protein_cross function in the cross_valid.py makes 4 training and 1 test dataset file plus extra combo files #Runs training_file_list.sh to create a .txt list of file names for training and test function inputs

#bash cross_valid.sh #Convert dataset to a fasta file for psiblast

python encoding.py python encoding_linear.py # | python svm_learning.py < (X, y)#(out_sparse1) #, out_formatted