PuSH - Publication Server of Helmholtz Zentrum München

Capitanchik, C.* ; Wilkins, O.G.* ; Wagner, N.* ; Gagneur, J. ; Ule, J.*

From computational models of the splicing code to regulatory mechanisms and therapeutic implications.

Nat. Rev. Genet., DOI: 10.1038/s41576-024-00774-2 (2024)
DOI PMC
Open Access Green as soon as Postprint is submitted to ZB.
Since the discovery of RNA splicing and its role in gene expression, researchers have sought a set of rules, an algorithm or a computational model that could predict the splice isoforms, and their frequencies, produced from any transcribed gene in a specific cellular context. Over the past 30 years, these models have evolved from simple position weight matrices to deep-learning models capable of integrating sequence data across vast genomic distances. Most recently, new model architectures are moving the field closer to context-specific alternative splicing predictions, and advances in sequencing technologies are expanding the type of data that can be used to inform and interpret such models. Together, these developments are driving improved understanding of splicing regulatory mechanisms and emerging applications of the splicing code to the rational design of RNA- and splicing-based therapeutics.
Altmetric
Additional Metrics?
[➜Log in]
Edit extra informations Login
Publication type Article: Journal article
Document type Review
Corresponding Author
Keywords Messenger-rna; High-throughput; Sequence; Binding; Variants; Introns; Reveals; Tdp-43; Gene; Polyadenylation
ISSN (print) / ISBN 1471-0056
e-ISSN 1471-0064
Journal Nature Reviews - Genetics
Publisher Nature Publishing Group
Publishing Place Heidelberger Platz 3, Berlin, 14197, Germany
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)