exopropred 1.1
A tool to predict the subcellular localisation of exosomal proteins
Requires Python: >=3.6

###ExoProPred

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

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###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 a 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.

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###Standalone

The Standalone version of exopropred is written in python3, and the following libraries are necessary for the successful run:
- scikit-learn
- Pandas
- Numpy

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###nstallation

To install the package, type the following command:

pip install exopropred

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###Minimum usage

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

exopropred -h

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###Getting started

To run the example, type the following command:

exopropred. -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).

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###Full Usage

usage: exopropred [-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

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###File descriptions

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

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

3. Threshold: User should provide a threshold between 0 and 1; by default, its 0.51.

4. 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 it's 1.

5. 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.

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###ExoProPred Package Files

It contains the following files; a brief description 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

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