A computational tool for predicting exosomal and non-exosomal miRNA
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
pip install emirpred
import emirpred
It can also be downloaded from - https://pypi.org/project/emirpred/
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
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.
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
python3 emirpred.py -f example_seq.fa -o output
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))
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