Prediction of alternative pre-mRNA splicing outcomes.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
15 11 2023
Historique:
received: 13 06 2023
accepted: 12 11 2023
medline: 17 11 2023
pubmed: 16 11 2023
entrez: 15 11 2023
Statut: epublish

Résumé

To understand the biological impact of alternative pre-mRNA splicing, it is vital to know which exons are involved, what protein domains they encode, and how the translated isoforms differ. Therefore, we developed a computational pipeline (RiboSplitter) focused on functional effect prediction. It builds on event-based alternative splicing detection with additional filtering steps leading to more efficient statistical testing, and with detection of isoform-specific protein changes. A key methodological advance is reading frame prediction by translating exonic DNA in all possible frames, then finding a single open reading frame, or a single frame with matches to known proteins of the gene. This allowed unambiguous translation in 93.9% of alternative splicing events when tested on RNA-sequencing data of B cells from Sjögren's syndrome patients. RiboSplitter does not depend on reference annotations and translates events even when one or both isoform(s) are novel (unannotated). RiboSplitter's visualizations illustrate each event with translation outcomes, show event location within the gene, and align exons to protein domains.

Identifiants

pubmed: 37968320
doi: 10.1038/s41598-023-47348-6
pii: 10.1038/s41598-023-47348-6
pmc: PMC10651857
doi:

Substances chimiques

RNA Precursors 0
RNA, Messenger 0
Protein Isoforms 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

20000

Informations de copyright

© 2023. The Author(s).

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Auteurs

Rayan Najjar (R)

Division of Rheumatology, Department of Medicine, University of Washington, 750 Republican Street, Seattle, WA, 98109, USA. najjar@uw.edu.

Tomas Mustelin (T)

Division of Rheumatology, Department of Medicine, University of Washington, 750 Republican Street, Seattle, WA, 98109, USA.

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Classifications MeSH