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Zawisza-Álvarez M, Peñuela-Melero J, Vegas E, Reverter F, Garcia-Fernàndez J, Herrera-Úbeda C. Exploring functional conservation in silico: a new machine learning approach to RNA-editing. Brief Bioinform 2024; 25:bbae332. [PMID: 38980372 PMCID: PMC11232462 DOI: 10.1093/bib/bbae332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 06/09/2024] [Accepted: 06/25/2024] [Indexed: 07/10/2024] Open
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.
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Affiliation(s)
- Michał Zawisza-Álvarez
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Digonal 643, 08028 Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain
| | - Jesús Peñuela-Melero
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Digonal 643, 08028 Barcelona, Spain
| | - Esteban Vegas
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Digonal 643, 08028 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, Calle Sinesio Delgado 4, 28029 Madrid, Spain
| | - Ferran Reverter
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Digonal 643, 08028 Barcelona, Spain
| | - Jordi Garcia-Fernàndez
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Digonal 643, 08028 Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain
| | - Carlos Herrera-Úbeda
- Departament de Genètica, Microbiologia i Estadística, Facultat de Biologia, Universitat de Barcelona, Av. Digonal 643, 08028 Barcelona, Spain
- Institut de Biomedicina (IBUB), Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain
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El Allali A, Elhamraoui Z, Daoud R. Machine learning applications in RNA modification sites prediction. Comput Struct Biotechnol J 2021; 19:5510-5524. [PMID: 34712397 PMCID: PMC8517552 DOI: 10.1016/j.csbj.2021.09.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 09/24/2021] [Accepted: 09/25/2021] [Indexed: 12/15/2022] Open
Abstract
Ribonucleic acid (RNA) modifications are post-transcriptional chemical composition changes that have a fundamental role in regulating the main aspect of RNA function. Recently, large datasets have become available thanks to the recent development in deep sequencing and large-scale profiling. This availability of transcriptomic datasets has led to increased use of machine learning based approaches in epitranscriptomics, particularly in identifying RNA modifications. In this review, we comprehensively explore machine learning based approaches used for the prediction of 11 RNA modification types, namely,m 1 A ,m 6 A ,m 5 C , 5 hmC , ψ , 2 ' - O - Me , ac 4 C ,m 7 G , A - to - I ,m 2 G , and D . This review covers the life cycle of machine learning methods to predict RNA modification sites including available benchmark datasets, feature extraction, and classification algorithms. We compare available methods in terms of datasets, target species, approach, and accuracy for each RNA modification type. Finally, we discuss the advantages and limitations of the reviewed approaches and suggest future perspectives.
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Affiliation(s)
- A. El Allali
- African Genome Center, University Mohamed VI Polytechnic, Morocco
| | - Zahra Elhamraoui
- African Genome Center, University Mohamed VI Polytechnic, Morocco
| | - Rachid Daoud
- African Genome Center, University Mohamed VI Polytechnic, Morocco
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