EmiRPred

A computational tool for predicting exosomal and non-exosomal miRNA

Introduction

EmiRPred is a tool for the classifcation of miRNA into exosomal and non-exosomal. It uses similarity-based methods (BLAST and MERCI for motif-search) combined with Extra Tree Classifier built on the best performing compoisition-based features extracted using One hot encoding, Term Frequency - Inverse Document Frequency, and Reverse Complement RNA strand compositions. EmiRPred is also available as a web-server at https://webs.iiitd.edu.in/raghava/emirpred. Please read/cite the content about EmiRPred for complete information including algorithm behind the approach.

Keywords: Exosome, miRNA, sub-cellular location, Subcellular, Machine Learning, Deep Learning, Biology

Reference

Arora, A., Raghava, G.P.S. Prediction of exosomal miRNA-based biomarkers for liquid biopsy. Sci Rep 15, 31191 (2025). https://doi.org/10.1038/s41598-025-15814-y

dataset folder

Contain all the dataset

Zenodo

https://doi.org/10.5281/zenodo.19917635

Python Package

pip install emirpred

import emirpred

It can also be downloaded from - https://pypi.org/project/emirpred/

Standalone

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

• scikit-learn=1.2.2
• Pandas
• Numpy
• Joblib
• Argparse

No additional package/tool is required for model = 1 (default model), however for model = 2, please download blast (version - blast: 2.12.0+) from https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html

Minimum USAGE

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

emirpred -h

To run the example, type the following command:

emirpred -f example_seq.fa -o output

Here, -f argument is to enter the input file in Fasta format and -o argument is for giving the path to the output directory. By default, the package uses model (-m) = 1 which employs only ML algorithm (Extra Tree Classifier) to classify the miRNA sequences, which generates a prediction file "classification_ML.csv" in the specified output directory. If model (-m) = 2 is selected, then the hybrid model is employed (ML + MERCI + BLAST) to classify the miRNA sequences, which generates a prediction file "classification_hybrid.csv" in the specified output directory.

Full Usage

usage: emirpred [-h] --file FILE --output OUTPUT [--model MODEL] [--threshold THRESHOLD]

Please provide following arguments for successful run
required arguments:
  --file FILE, -f FILE                   Path to fasta file
  --output OUTPUT, -o OUTPUT             Path to output

optional arguments:

  --model MODEL, -m MODEL                Model selection: 1 for ML only, 2 for ML + BLAST + MERCI (By default model = 1)
  --threshold THRESHOLD, -t THRESHOLD    Threshold for classification (can be any value between 0-1 for model = 1 (by default = 0.5) and 0-2 for model = 2 (by default = 0.52))

For help:
  -h, --help            show this help message and exit

Standalone minimum usage

python3 emirpred.py -f example_seq.fa -o output

Arguments description

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

Output File: Program will save the results to this folder

Model: User can pick which model to run, model = 1 runs only ML model (ET classifier), whereas model = 2 runs hybrid model (ML + BLAST + MERCI), by default the tool runs model = 1

Threshold: User can provide threshold for classification (can be any value between 0-1 for model = 1 (by default = 0.5) and 0-2 for model = 2 (by default = 0.52))

EmiRPred 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

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

emirpred.py : Main python program

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

blast_db : Database for BLAST search

MERCI_motif_locator.pl : To locate exosomal motifs within the query sequences