Skip to main content

A tool to predict il5 inducing peptides

Project description

IL5pred

A computational method to predict and scan IL-5 inducing peptides.

Introduction

IL5pred is developed to predict, scan, and design IL-5 inducing peptides. More information on IL5pred web server is available at https://webs.iiitd.edu.in/raghava/il5pred. Please read/cite the content about the IL5pred for complete information including the algorithm behind the approach.

Reference

Devi NL, Sharma N and Raghava GPS (2023) A web server for predicting and scanning of IL-5 inducing peptides using alignment-free and alignment-based method. Computers in Biology and Medicine, DOI:10.1016/j.compbiomed.2023.106864.

Model

The best Random Forest-based hybrid model (RF(DPC)+BLAST) is implemented in the webserver.

Standalone

Standalone version of IL5pred is written in python3 and the following libraries are necessary for a successful run:

  • scikit-learn
  • Pandas
  • Numpy
  • blastp

Important Note

  • Due to large size of the model file, we have not included it in the zipped folder or GitHub repository, thus to run standalone successfully you need to download model file and then unzip them.
  • Make sure you extract the download zip files in the directory where main execution file i.e. il5pred.py is available.
  • To download the model folder click [here].(https://webs.iiitd.edu.in/raghava/il5pred/model.zip)

Minimum usage

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

il5pred -h

To run the example, type the following command:

il5pred -i example_input.fa

This will predict if the submitted sequences are IL-5 inducers or Non-inducers. It will use other parameters by default. It will save the output in "outfile.csv" in CSV (comma-separated variables).

Full Usage:

Following is complete list of all options, you may get these options
usage: il5pred [-h] 
                       [-i INPUT]
                       [-o OUTPUT]
		       [-j {1,2,3}]
		       [-t THRESHOLD]
                       [-w {9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25}]
		       [-d {1,2}]
Please provide the 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 a 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 0.21
  -w {9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25}, --winleng {9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25}
                        Window Length: 9 to 20 (scan mode only), by default 9
  -d {1,2}, --display {1,2}
                        Display: 1:IL-5 inducing peptides only, 2: All peptides, by default 1

Input File: Allows user to provide input in FASTA format.

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

Threshold: User should provide a threshold between 0 and 1, by default it's 0.21.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

il5pred-1.2.tar.gz (104.6 MB view details)

Uploaded Source

File details

Details for the file il5pred-1.2.tar.gz.

File metadata

  • Download URL: il5pred-1.2.tar.gz
  • Upload date:
  • Size: 104.6 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.17

File hashes

Hashes for il5pred-1.2.tar.gz
Algorithm Hash digest
SHA256 a0df4b0e5390a8114ee8d3c119df56c268ba160bd8f51d60e24e6cae8deff0db
MD5 1eca156c8da32cb5b77fafe2ca937823
BLAKE2b-256 af133bc115148d0758191738685bc082605f3a2563547769a918efb930757e9b

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page