dmppred 1.0.0

Dmppred: A tool for predicting, designing, and scanning Type 1 associated peptides

DMPPred

A computational approach to predict the Type 1 diabetes mellitus causing peptides using the sequence information.

Introduction

DMPPred is developed to predict, scan, and, design the Type 1 diabetes mellitus causing peptides using sequence information only. In the standalone version, extra-tree classifier based model is implemented alongwith the BLAST search, named it as hybrid approach. DMPPred is also available as web-server at https://webs.iiitd.edu.in/raghava/dmppred. Please read/cite the content about the DMPPred 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
• Pandas
• Numpy
• blastp

Minimum USAGE

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

python dmppred.py -h

To run the example, type the following command:

python3 dmppred.py -i example_input.fa

This will predict if the submitted sequences can cause diabetes or not. It will use other parameters by default. It will save the output in "outfile.csv" in CSV (comma seperated variables).

Full Usage

usage: transfacpred.py [-h] 
                       [-i INPUT 
                       [-o OUTPUT]
		       [-j {1,2,3}]
		       [-t THRESHOLD]
                       [-w {8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30}]
		       [-d {1,2}]

Please provide following arguments for successful run

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
  -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 0.16
  -w {8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30}, --winleng {8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30}
                        Window Length: 8 to 30 (scan mode only), by default 9
  -d {1,2}, --display {1,2}
                        Display: 1:Diabetic peptides only, 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 0.58.

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 30 in long sequences. This option is available for only scanning module.

Display type: This option allow users to fetch either only diabetis causing peptides by choosing option 1 or prediction against all peptides by choosing option 2.

DMPPred 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 model

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

dmppred.py : Main python program

example_input.fa : Example file contain peptide sequenaces in FASTA format

example_predict_output.csv : Example output file for predict module

example_scan_output.csv : Example output file for scan module

example_design_output.csv : Example output file for design module

Reference:

Kumar N, Patiyal S, Choudhury S, Tomer R, Dhall A, Raghava GPS. DMPPred: a tool for identification of antigenic regions responsible for inducing type 1 diabetes mellitus. Brief Bioinform. 2022;bbac525. doi:10.1093/bib/bbac525