Quantifying splice-site usage: a simple yet powerful approach to analyze splicing.


Journal

NAR genomics and bioinformatics
ISSN: 2631-9268
Titre abrégé: NAR Genom Bioinform
Pays: England
ID NLM: 101756213

Informations de publication

Date de publication:
Jun 2021
Historique:
received: 18 12 2020
revised: 24 03 2021
accepted: 28 04 2021
entrez: 21 5 2021
pubmed: 22 5 2021
medline: 22 5 2021
Statut: epublish

Résumé

RNA splicing, and variations in this process referred to as alternative splicing, are critical aspects of gene regulation in eukaryotes. From environmental responses in plants to being a primary link between genetic variation and disease in humans, splicing differences confer extensive phenotypic changes across diverse organisms (1-3). Regulation of splicing occurs through differential selection of splice sites in a splicing reaction, which results in variation in the abundance of isoforms and/or splicing events. However, genomic determinants that influence splice-site selection remain largely unknown. While traditional approaches for analyzing splicing rely on quantifying variant transcripts (i.e. isoforms) or splicing events (i.e. intron retention, exon skipping etc.) (4), recent approaches focus on analyzing complex/mutually exclusive splicing patterns (5-8). However, none of these approaches explicitly measure individual splice-site usage, which can provide valuable information about splice-site choice and its regulation. Here, we present a simple approach to quantify the empirical usage of individual splice sites reflecting their strength, which determines their selection in a splicing reaction. Splice-site strength/usage, as a quantitative phenotype, allows us to directly link genetic variation with usage of individual splice-sites. We demonstrate the power of this approach in defining the genomic determinants of splice-site choice through GWAS. Our pilot analysis with more than a thousand splice sites hints that sequence divergence in

Identifiants

pubmed: 34017946
doi: 10.1093/nargab/lqab041
pii: lqab041
pmc: PMC8121094
doi:

Types de publication

Journal Article

Langues

eng

Pagination

lqab041

Informations de copyright

© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.

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Auteurs

Craig I Dent (CI)

School of Biological Sciences, Monash University, VIC 3800, Australia.

Shilpi Singh (S)

School of Biological Sciences, Monash University, VIC 3800, Australia.

Sourav Mukherjee (S)

Independent Scholar, India.

Shikhar Mishra (S)

School of Biological Sciences, Monash University, VIC 3800, Australia.

Rucha D Sarwade (RD)

School of Biological Sciences, Monash University, VIC 3800, Australia.

Nawar Shamaya (N)

School of Biological Sciences, Monash University, VIC 3800, Australia.

Kok Ping Loo (KP)

School of Biological Sciences, Monash University, VIC 3800, Australia.

Paul Harrison (P)

Monash Bioinformatics Platform, Monash University, VIC 3800, Australia.

Sridevi Sureshkumar (S)

School of Biological Sciences, Monash University, VIC 3800, Australia.

David Powell (D)

Monash Bioinformatics Platform, Monash University, VIC 3800, Australia.

Sureshkumar Balasubramanian (S)

School of Biological Sciences, Monash University, VIC 3800, Australia.

Classifications MeSH