MRSLpred

A computational tool for multilabel mRNA subcellular localization prediction

Introduction

MRSLpred is a tool for multilabel mRNA subcellular localization prediction using a XGBoost classifier. It uses only composition based features for predicting the subcellular locations. The final model also deploys a motif-based module which has been implemented using MERCI. MRSLpred is also available as web-server at https://webs.iiitd.edu.in/raghava/mrslpred. Please read/cite the content about MRSLpred for complete information including algorithm behind the approach.

PIP Installation

PIP version is also available for easy installation and usage of this tool. The following command is required to install the package

pip install mrslpred

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

mrslpred -h

Standalone

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

• scikit-learn
• xgboost=0.90
• Pandas
• Numpy

Minimum USAGE

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

python3 mrslpred_motif.py -h

To run the example, type the following command:

python3 mrslpred_motif.py -i example_input.fa

This will predict where the submitted sequences are going to localize. It will use other parameters by default. It will save the output in "final_prediction.csv" and "final_prob_prediction.csv" in CSV format (comma separated variables).

Full Usage

usage: mrslpred_motif.py [-h] --file FILE [--th1 TH1] [--th2 TH2] [--th3 TH3]
                         [--th4 TH4] [--th5 TH5] [--th6 TH6] --output OUTPUT

Please provide following arguments for successful run

optional arguments:
  -h, --help            show this help message and exit
  --file FILE, -f FILE  Path to fasta file
  --th1 TH1, -t1 TH1    Threshold for Ribosome
  --th2 TH2, -t2 TH2    Threshold for Cytosol
  --th3 TH3, -t3 TH3    Threshold for Endoplasmic Reticulum
  --th4 TH4, -t4 TH4    Threshold for Membrane
  --th5 TH5, -t5 TH5    Threshold for Nucleus
  --th6 TH6, -t6 TH6    Threshold for Exosome
  --output OUTPUT, -o OUTPUT    Path to output

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

Output File: Program will save the results to this folder

Threshold 1: User should provide threshold for Ribosome between 0 and 1, by default it is 0.3079.

Threshold 2: User should provide threshold for Cytosol between 0 and 1, by default it is 0.1468.

Threshold 3: User should provide threshold for Endoplasmic Reticulum between 0 and 1, by default it is 0.1156.

Threshold 4: User should provide threshold for Membrane between 0 and 1, by default it is 0.1956.

Threshold 5: User should provide threshold for Nucleus between 0 and 1, by default it is 0.7028.

Threshold 6: User should provide threshold for Exosome between 0 and 1, by default it is 0.9961.

MRSLpred Package Files

It contantain following files, brief description of these files given below

INSTALLATION : Installations instructions

LICENSE : License information

README.md : This file provide information about this package

xgboost_final.pkl : This file contains the pickled version of model

mrslpred_motif.py : Main python program

dataset.zip : Compressed zip file containing two files - training_dataset.fasta and validation_dataset.fasta

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

example_predict_prob_output.csv : Example output file containing probabilities for each location

example_predict_output.csv : Example output file containing labels for each location