yascoma / paprec_pipeline

Python pipeline to prepare epitopes and protein sequence datasets, extract numerical features from sequence with alignment-free methods, perform model evaluation and test model performance upon feature selection.

PAPreC - Pipeline for Antigenicity Predictor Comparison

Python pipeline to prepare epitopes and protein sequence datasets, extract numerical features from sequence with alignment-free methods, perform model evaluation and test model performance upon feature selection.

Summary

We have developed a comprehensive pipeline for comparing models used in antigenicity prediction. This pipeline encompasses a range of experiment configurations that systematically modify four key parameters: (1) the source dataset, encompassing datasets Bcipep, hla and Protegen (Yang et al. 2011); (2) the alignment-free method employed for generating numerical features; and (3) the utilization of nine distinct classifiers.

Requirements:

• Python packages needed:
  • pip3 install numpy
  • pip3 install sklearn
  • pip3 install pandas
  • pip3 install matplotlib
  • pip3 install statistics
  • pip3 install boruta
  • pip3 install joblib

Usage Instructions

Preparation:

1. git clone https://github.com/YasCoMa/paprec_pipeline.git
2. cd paprec_pipeline
3. pip3 install -r requirements.txt

Run Screening:

1. python3 multiple_method_dataset.py
2. Check the results obtained with those found in our article:
   • Bcipep dataset: https://www.dropbox.com/s/8ezeup4xiwb9p7n/bcipep_dataset.zip?dl=0
   • HLA dataset: https://www.dropbox.com/s/6vpfgvmsz9vd5r0/hla_dataset.zip?dl=0
   • Gram+ dataset: https://www.dropbox.com/s/l5wqpcsp4qc6ret/gram%2B_dataset.zip?dl=0
   • Gram- dataset: https://www.dropbox.com/s/cvzrhlselxj9sp5/gram-_dataset.zip?dl=0

Run Comparison in Gram positive and negative bacteria (Optional) :

1. Download and uncompress the following folder: https://www.dropbox.com/s/27nnwhh1spl2038/gram_comparison.zip?dl=0
2. python3 comparison_gram.py

Reference

Bug Report

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