open access publication

Article, 2024

Deep Learning for Elucidating Modifications to RNA—Status and Challenges Ahead

Genes, ISSN 2073-4425, Volume 15, 5, Page 629, 10.3390/genes15050629

Contributors

Rennie, Sarah 0000-0001-9731-6957 [1]

Affiliations

  1. [1] University of Copenhagen
  2. [NORA names: KU University of Copenhagen; University; Denmark; Europe, EU; Nordic; OECD]

Abstract

RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.

Keywords

AHEAD, CO, Deep, RNA, RNA status, RNA-binding proteins, absence, absence of modifications, approach, area, article, binding, biological role, challenges, chemical, chemical modifications to RNA, context, deep learning, deep learning approach, determination, diverse features, features, field, genes, genomic regions, input, learning, learning approach, location, method, model, model training, modification, modifications to RNA, negative region, overview, patterns, post-transcriptional regulation, post-transcriptional regulation of genes, preferences, presence, problem, process, process of model training, protein, recommendations, region, regulation of genes, relevant model, role, secondary structure, sequence, sequence patterns, sequence-based, site preference, sites, structure, target, target location, test, training

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