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Genome-wide discovery of pre-miRNAs

This repository contains the source code to reproduce the results shown in

L.A. Bugnon, C. Yones, D.H. Milone and G. Stegmayer, "Genome-wide discovery of pre-miRNAs: comparison of recent approaches based on machine learning", Briefings in Bioinformatics, (in press), 2020

The genome-wide discovery of microRNAs (miRNAs) involves identifying sequences having the highest chance of being a novel miRNA precursor (pre-miRNA), within all sequences in a complete genome. The known pre-miRNAs are usually just a few in comparison to the millions of candidates to pre-miRNAs that have to be analyzed. In this work, we review six recent methods for tackling this problem with machine learning, comparing the models in genome-wide datasets. They have been designed for the pre-miRNAs prediction task, where there is a class of interest that is significantly underrepresented (the known pre-miRNAs) with respect to a very large number of unlabeled samples.

Running the experiments

The methods reviewed here were published under different frameworks. Several installation steps would be needed for each case. Most of the methods run on Python, except for miRNAss that runs on R and deepBN that runs on Matlab/Octave. The code was tested with:

Unzip the source code from "gwmirna-X.Y.tar.gz". Sequences and features to be analyzed should be in the "genomes/" folder. The C. elegans (CEL) dataset with its corresponding training and testing partitions is provided in "celdata.tar.gz".

OC-SVM

Install the required packages with:

python -m pip install --user -r src/OC-SVM/requirements.txt

The model is trained and tested in a cross-validation scheme with:

python train_OC-SVM.py

Scores are saved in "results/OC-SVM/"

deeSOM

In a similar way, install the requirements with:

python -m pip install --user -r src/deeSOM/requirements.txt

Then train and test the model with

python train_deeSOM.py

miRNAss

A python wrapper is used to train and test the model. Install the requirements with:

python -m pip install --user -r src/miRNAss/requirements.txt

If R is already installed, just run:

python train_miRNAss.py

The script also will install the miRNAss package.

deepBN

This model was built in a Matlab/Octave framework. Open an Octave terminal in this root folder and run

octave:1> train_deepBN

bb-DeepMir

Install the requirements with:

python -m pip install -r src/bbDeepMir/requirements.txt

Train and test the model with:

KERAS_BACKEND=tensorflow python train_bb-DeepMir.py

bb-deepMiRGene

This method uses libraries that are not available in PiPy. The fastest way to install dependencies is using an Anaconda distribution:

conda install -c bioconda --file src/bbdeepMiRGene/requirements.txt

Train and test the model with:

KERAS_BACKEND=theano python train_bb-deepMiRGene.py