A tool to predict toxic and non-toxic proteins
Project description
Toxinpred2.0
Introduction
ToxinPred2.0 is developed for predicting, mapping and scanning toxic proteins. More information on ToxinPred2.0 is available from its web server http://webs.iiitd.edu.in/raghava/toxinpred2. This page provide information about standalone version of ToxinPred2.0.
Installation
To install the package, type the following command:
pip install toxinpred2
Minimum USAGE
To know about the available option for the package, type the following command:
toxinpred2.py -h
To run the example, type the following command:
toxinpred2.py -i peptide.fa
Full Usage:
Following is complete list of all options, you may get these options by "toxinpred2.py -h"
usage: toxinpred2.py [-h] -i INPUT [-o OUTPUT] [-t THRESHOLD] [-m {1,2}] [-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 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
-t THRESHOLD, --threshold THRESHOLD
Threshold: Value between 0 to 1 by default 0.6
-m {1,2}, -- model Model
Model: 1: AAC based RF, 2: Hybrid, by default 1
-d {1,2}, --display {1,2}
Display: 1:Toxin peptide, 2: All peptides, by
default 1
Input File: It allow users to provide input in two format; i) FASTA format (standard) (e.g. peptide.fa) and ii) Simple Format. In case of simple format, file should have one peptide sequence in a single line in single letter code (eg. peptide.seq).
Output File: Program will save result in 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, please note score is proportional to toxic potential of peptide.
Models: In this program, two models have been incorporated; i) Model1 for predicting given input peptide/protein sequence as toxic and non-toxic peptide/proteins using Random Forest based on amino-acid composition of the peptide/proteins;
ii) Model2 for predicting given input peptide/protein sequence as toxic and non-toxic peptide/proteins using Hybrid approach, which is the ensemble of Random Forest+ BLAST+ MERCI. It combines the scores generated from machine learning (RF), MERCI, and BLAST as Hybrid Score, and the prediction is based on Hybrid Score.
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