A computational approach to predict, scan, and design the host-specific IFN-γ inducing epitopes using the sequence information of the peptides.
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.
PIP version is also available for easy installation and usage of this tool. The following command is required to install the package
pip install ifnepitope2
To know about the available option for the pip package, type the following command:
ifnepitope2 -h
The Standalone version of transfacpred is written in python3 and following libraries are necessary for the successful run:
- scikit-learn
- Pandas
- Numpy
- blastp
For hyperparameter tuning, we implemented grid search with 5-fold stratified cross-validation across different classifiers, including Decision Trees (DT), Random Forest (RF), Logistic Regression (LR), XGBoost (XGB), K-Nearest Neighbors (KNN), Extra Trees (ET), and Support Vector Classifiers (SVC). Each model was optimized using a tailored parameter grid. The parameter grids were dynamically adjusted based on the data to ensure valid configurations during cross-validation. The best hyperparameters were selected based on the highest AUROC scores achieved during the tuning process.
To know about the available option for the parameter optimization, type the following command:
python3 param_opt.py -h
To run the example, type the following command:
python3 param_opt.py --file example_param_opt_input_file.csv --classifier ALL --output example_param_opt_output_file.txt
To run the code with feature file, type the following command:
python3 param_opt.py --file <feature file> --classifer <Classifier Options>
To know about the available option for the stanadlone, type the following command:
python ifnepitope2.py -h
To run the example, type the following command:
python3 ifnepitope2.py -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).
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.
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
