Delineating yeast cleavage and polyadenylation signals using deep learning.


Journal

Genome research
ISSN: 1549-5469
Titre abrégé: Genome Res
Pays: United States
ID NLM: 9518021

Informations de publication

Date de publication:
24 Jun 2024
Historique:
received: 10 10 2023
accepted: 17 06 2024
medline: 25 6 2024
pubmed: 25 6 2024
entrez: 24 6 2024
Statut: aheadofprint

Résumé

3'-end cleavage and polyadenylation is an essential process for eukaryotic mRNA maturation. In yeast species, the polyadenylation signals that recruit the processing machinery are degenerate and remain poorly characterized compared to the well-defined regulatory elements in mammals. Here we address this question by developing deep learning models to deconvolute degenerate

Identifiants

pubmed: 38914436
pii: gr.278606.123
doi: 10.1101/gr.278606.123
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Published by Cold Spring Harbor Laboratory Press.

Auteurs

Emily Stroup (E)

Northwestern University.

Zhe Ji (Z)

Northwestern University zhe.ji@northwestern.edu.

Classifications MeSH