TransFacPred

A highly accurate method to predict the transcription factors using protein sequences.

Introduction

TransFacPred is developed for predicting the transcription factors (TFs) using the protein primary sequence information. In this approach, Hybrid model was implemented in which is a combination of ET-based model and BLAST Search.

Webserver and Standalone

Available as web-server at https://webs.iiitd.edu.in/raghava/transfacpred

Available as standalone at https://github.com/raghavagps/transfacpred/

Installation

To install the Transfacpred package, use the following command:

pip install transfacpred

Minimum USAGE

To explore the available options for the command-line tool, use:

transfacpred -h

Full Usage

usage: transfacpred [-h] 
                       [-i INPUT 
                       [-o OUTPUT]
                       [-t THRESHOLD]
                       [-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: File name containing protein or peptide sequence in FASTA format.
  -o OUTPUT, --output OUTPUT
                        Output: File for saving results by default outfile.csv
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold: Value between -0.5 to 1.5 by default -0.38
  -d {1,2}, --display {1,2}
                        Display: 1:Transcription Factors, 2: All Sequences, 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 -0.5 and 1.5.

Example usage details:

transfacpred.py -i protein.fa

This will predict if the submitted sequences are TFs or Non-TFs and display only the TFs. It will use other parameters by default. It will save the output in "outfile.csv"

transfacpred -i protein.fa -o output.csv -t 0.55 -d 2

This will predict if the submitted sequences are TFs or Non-TFs and display all. It will save the output in "output.csv" in CSV (comma seperated variables).

Reference

Patiyal et al. (2022) A hybrid approach for predicting transcription factors. Bioxriv doi: https://doi.org/10.1101/2022.07.13.499865