This repository contains the official implementation of Deepred-Mt, along with instructions for reproducing results presented in "Deepred-Mt: Deep Representation Learning for Predicting C-to-U RNA Editing in Plant Mitochondria", by A. A. Edera, I. Small, D. H. Milone, and M. V. Sanchez-Puerta. Download PDF.

In land plants, the editosome is a highly sophisticated molecular machine able to convert post-transcriptionally cytidines into uridines (C-to-U) at highly specific RNA positions called editing sites. This RNA editing is governed by cis elements, which still remain recalcitrant to characterization.

Deepred-Mt is a novel neural network able to predict C-to-U editing sites in angiosperm mitochondria. Given an RNA sequence, consisting of a central cytidine flanked by 20 nucleotides on each side, Deepred-Mt scores how probable its editing is.

The score is computed from complex cis elements or motifs automatically extracted from the flanking bases by a multi-layer convolutional neural network, whose full architecture is schematically shown below.

To submit RNA/DNA sequences for predicting their C-to-U editing sites with Deepred-Mt, use the following link:

Submit sequences

Note 1: To be able to submit, you must be logged in with a Google Account (e.g., Gmail).

Note 2: If difficulties are experienced when submitting sequences, try to use Google Chrome as the web browser.

To install Deepred-Mt on your computer, the following dependencies must be installed:

• Python 3.7
• pip
• Git
• Conda

First, create and activate a new Conda environment

conda create -n deepredmt python=3.7
conda activate deepredmt

Next, install Deepred-Mt from the sources

pip install -U "deepredmt @ git+https://github.com/aedera/deepredmt.git"

Once installed, Deepred-Mt can be executed on the command line to predict C-to-U editing sites from a desired FASTA file. Here is an example FASTA file called seqs.fas:

deepredmt seqs.fas

This command extracts cytidines from the FASTA file to make predictions based on their surrounding nucleotides.

The following notebooks reproduce experiments in the article.

Description	Notebook
Use Deepred-Mt on the command line to predict C-to-U editing sites from a given FASTA file
Compare the predictive performance of Deepred-Mt and state-of-the art methods for predicting editing sites
Train Deepred-Mt from scratch

Experiments used three datasets built from FASTA files containing mitochondrial protein sequences from 21 plant species. These files have 'E' nucleotides to indicate C-to-U editing sites, which were previously identified by using RNAseq data publicly available in the European Nucleotide Archive.

More information on the data format is provided here.

Deepred-Mt was compared to two state-of-the-art methods for predicting editing sites: PREP-Mt and PREPACT. The following figure shows precision-recall curves obtained from the predictions of each method. Deepred-Mt achieves the highest F1 scores and the best areas under the curves (AUPRC) for two predictive scenarios: one excluding synonymous sites (dashed lines) and other including them (solid lines).

Contributions from anyone are welcome. You can start by adding a new entry here.

Deepred-Mt is licensed under the MIT license. See LICENSE for more details.