Deepred-Mt: Deep Representation Learning for Predicting C-to-U RNA Editing in Plant Mitochondria

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 seems to be partially 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.

Submit RNA sequences for predictions

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.

If you encounter problems when submitting sequences please report an issue.

Installation

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"

Usage

Command line

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.

Demo notebooks

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

Data

The experiments reported in the manuscript used three datasets built from these FASTA files, extracted from nucleotide sequences encoding mitochondrial proteins from 21 plant species. In these files, 'E' nucleotides indicate C-to-U editing sites identified by using published RNAseq data, obtained from the European Nucleotide Archive.

Dataset	Description
Training data	41-bp nucleotide windows whose center positions are either unedited (C) or edited (E) cytidines. Nucleotide windows are labeled according to both the nucleotide in their central positions (0/C, 1/E) and their corresponding editing extents (a value ranging from 0 to 1)
Task-related sequences	Sequences used for the augmentation strategy proposed in the article. These sequences are 41-bp nucleotide windows whose center positions are thymidines homologous to one of the editing sites in the training data
Control data	Control data containing fake editing signal "GGCG" within the downstream regions of nucleotide windows that are labeled as 1 (edited)

More information on the data format is provided here.

Results

In our experiments, 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).

Method	Excluded		Included
	AUPRC	F1	AUPRC	F1
PREPACT	0.91	0.89	0.79	0.82
PREP-Mt	0.88	0.91	0.76	0.84
Deepred-Mt	0.96	0.92	0.91	0.86

Contributing

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

License

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