IFNepitope2

A computational approach to predict, scan, and design the host-specific IFN-γ inducing epitopes using the sequence information of the peptides.

Introduction

IFNepitope2 is an update of IFNepitope published by our group in 2013. It is developed to predict, scan, and, design the IFN-γ inducing peptides for human and mouse host, seperately, using sequence information only. In the standalone version, DPC based extra-tree classifier model is implemented alongwith the BLAST search, named it as hybrid approach. IFNepitope2 is also available as web-server at https://webs.iiitd.edu.in/raghava/ifnepitope2. Please read/cite the content about the IFNepitope2 for complete information including algorithm behind the approach.

Standalone

The Standalone version of transfacpred is written in python3 and following libraries are necessary for the successful run:

• scikit-learn(1.2.1)
• Pandas(1.4.0)
• Numpy
• blastp

Minimum USAGE

To know about the available option for the stanadlone, type the following command:

ifnepitope2 -h

To run the example, type the following command:

ifnepitope2 -i example_input_human.fa

This will predict if the submitted sequences are IFN-γ inducers or Non-inducer. It will use other parameters by default. It will save the output in "outfile.csv" in CSV (comma seperated variables).

Full Usage

usage: ifnepitope2.py [-h]
		      -i INPUT
		      [-o OUTPUT]
		      [-s {1,2}]
		      [-j {1,2,3}]
                      [-t THRESHOLD]
                      [-w {8,9,10,11,12,13,14,15,16,17,18,19,20}]
		      [-d {1,2}]

Please provide following arguments

optional arguments:
  -h, --help            show this help message and exit
  -i INPUT, --input INPUT
                        Input: protein or peptide sequence(s) in FASTA format
                        or single sequence per line in single letter code
  -o OUTPUT, --output OUTPUT
                        Output: File for saving results by default outfile.csv
  -s {1,2}, --host {1,2}
                        Host: 1: Human, 2: Mouse, by default 1
  -j {1,2,3}, --job {1,2,3}
                        Job Type: 1:Predict, 2: Design, 3:Scan, by default 1
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold: Value between 0 to 1 by default 0.49
  -w {8,9,10,11,12,13,14,15,16,17,18,19,20}, --winleng {8,9,10,11,12,13,14,15,16,17,18,19,20}
                        Window Length: 8 to 20 (scan mode only), by default 8
  -d {1,2}, --display {1,2}
                        Display: 1:IFN-γ inducers, 2: All peptides, by default 1

Input File: It allow users to provide input in the FASTA format.

Output File: Program will save the results in the CSV format, in case user do not provide output file name, it will be stored in "outfile.csv".

Threshold: User should provide threshold between 0 and 1, by default its 0.49.

Host: User is allowed to choose the host organism, such as, 1 for Human, and 2 for Mouse.

Job: User is allowed to choose between three different modules, such as, 1 for prediction, 2 for Designing and 3 for scanning, by default its 1.

Window length: User can choose any pattern length between 8 and 20 in long sequences. This option is available for only scanning module.

Display type: This option allow users to fetch either only HLA-DRB1-04:01 binding peptides by choosing option 1 or prediction against all peptides by choosing option 2.

IFNepitope2 Package Files

It contantain following files, brief descript of these files given below

INSTALLATION : Installations instructions

LICENSE : License information

README.md : This file provide information about this package

model.zip : This zipped file contains the compressed version of models

envfile : This file compeises of paths for the database and blastp executable

ifnepitope2.py : Main python program

example_input_human.fa : Example file contain peptide sequences for human host in FASTA format

example_input_mouse.fa : Example file contain peptide sequenaces for mouse host in FASTA format

example_predict_human_output.csv : Example output file for predict module for human host

example_predict_mouse_output.csv : Example output file for predict module for mouse host

example_scan_human_output.csv : Example output file for scan module for human host

example_scan_mouse_output.csv : Example output file for scan module for mouse host

example_design_human_output.csv : Example output file for design module for human host

example_design_mouse_output.csv : Example output file for design module for mouse host