exopropred 1.1

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.

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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