Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/217652
Title: Exploring functional conservation in silico: a new machine learning approach to RNA-editing
Author: Zawisza-Álvarez, Michał 
Peñuela Melero, Jesús
Vegas Lozano, Esteban
Reverter Comes, Ferran
Garcia Fernández, Jordi
Herrera Úbeda, Carlos
Keywords: Aprenentatge automàtic
Evolució (Biologia)
RNA
Machine learning
Evolution (Biology)
RNA
Issue Date: 9-Jul-2024
Publisher: H. Stewart Publications
Abstract: Around 50 years ago, molecular biology opened the path to understand changes in forms, adaptations, complexity, or the basis of human diseases through myriads of reports on gene birth, gene duplication, gene expression regulation, and splicing regulation, among other relevant mechanisms behind gene function. Here, with the advent of big data and artificial intelligence (AI), we focus on an elusive and intriguing mechanism of gene function regulation, RNA editing, in which a single nucleotide from an RNA molecule is changed, with a remarkable impact in the increase of the complexity of the transcriptome and proteome. We present a new generation approach to assess the functional conservation of the RNA-editing targeting mechanism using two AI learning algorithms, random forest (RF) and bidirectional long short-term memory (biLSTM) neural networks with an attention layer. These algorithms, combined with RNA-editing data coming from databases and variant calling from same-individual RNA and DNA-seq experiments from different species, allowed us to predict RNA-editing events using both primary sequence and secondary structure. Then, we devised a method for assessing conservation or divergence in the molecular mechanisms of editing completely in silico: the cross-testing analysis. This novel method not only helps to understand the conservation of the editing mechanism through evolution but could set the basis for achieving a better understanding of the adenosine-targeting mechanism in other fields.
Note: Reproducció del document publicat a: https://doi.org/10.1093/bib/bbae332
It is part of: Briefings In Bioinformatics, 2024, vol. 25, num.4, p. 1-12
URI: https://hdl.handle.net/2445/217652
Related resource: https://doi.org/10.1093/bib/bbae332
ISSN: 1467-5463
Appears in Collections:Articles publicats en revistes (Genètica, Microbiologia i Estadística)

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