exopropred 1.0

A tool to predict the subcellular localisation of exosomal proteins

ExoProPred

A computational approach to predict the subcellular localisation of exosomal proteins using the sequence information of the proteins.

Introduction

ExoProPred is a web server to predict exosomal proteins based on a hybrid model that combines a machine learning model with a motif-search approach. The models are trained on a dataset comprising 2831 exosomal proteins and 2831 non-exosomal proteins. The performance of the models was evaluated using 5-fold cross-validation. The models were trained on the top 70 best features comprising of composition-based and evolutionary information-based features as well as on hybrid features(Top 70 features + Motif-search) by implementing a random-forest classifier from the scikit library of Python. In the standalone version, a random-forest classifier-based model is implemented along with the motif search using the MERCI tool, named as hybrid approach. ExoProPred is also available as a web server at https://webs.iiitd.edu.in/raghava/exopropred. Please read/cite the content about the ExoProPred for complete information, including the algorithm behind the approach.

Standalone

The Standalone version of transfacpred is written in python3, and the following libraries are necessary for the successful run:

• scikit-learn
• Pandas
• Numpy

Minimum USAGE

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

python3 exopropred.py -h

To run the example, type the following command:

python3 exopropred.py -i example_input.fa

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

Full Usage

usage: exopropred.py [-h] -i INPUT [-o OUTPUT] [-m {1,2}] [-t THRESHOLD]
                     [-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 single letter code
  -o OUTPUT, --output OUTPUT
                        Output: File for saving results by default outfile.csv
  -m {1,2}, --model {1,2}
                        Model Type: 1: Composition-based model, 2: Hybrid
                        Model, by default 1
  -t THRESHOLD, --threshold THRESHOLD
                        Threshold: Value between 0 to 1 by default 0.51
  -d {1,2}, --display {1,2}
                        Display: 1:Exosomal Proteins only, 2: All Proteins, by
                        default 1

Input File: It allows users to provide input in the FASTA format.

Output File: Program will save the results in the CSV format; in case the user do 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 its 0.51.

Model: User is allowed to choose between two different models, such as 1 for the composition-based model, and 2 for the hybrid model; by default its 1.

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

ExoProPred Package Files

It contain the following files, brief descript of these files is given below

INSTALLATION : Installations instructions

LICENSE : License information

README.md : This file provides information about this package

model.zip : This zipped file contains the compressed version of the model

envfile : This file comprises paths for the PSI-BLAST, MERCI_motif_locator.pl, Motifs, and Swiss-Prot database.

exopropred.py : Main Python program

MERCI_motif_locator.pl : Perl script for locating motifs using MERCI

swissprot : Swiss-Prot database for calculating PSSM profile

motifs : Folder containing the motif files

extra : Folder containing the Python scripts for PSSM-based composition features

Data : Folder containing the files to calculate the features using Pfeature

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

example_composition_model_output.csv : Example output file for composition-based model

example_hybrid_model_output.csv : Example output file for the hybrid model