Computational approaches to understand transcription regulation in development.
developmental biology
functional genomics
gene expression and regulation
gene regulatory networks
Journal
Biochemical Society transactions
ISSN: 1470-8752
Titre abrégé: Biochem Soc Trans
Pays: England
ID NLM: 7506897
Informations de publication
Date de publication:
27 02 2023
27 02 2023
Historique:
received:
28
10
2022
revised:
07
01
2023
accepted:
13
01
2023
pubmed:
26
1
2023
medline:
3
3
2023
entrez:
25
1
2023
Statut:
ppublish
Résumé
Gene regulatory networks (GRNs) serve as useful abstractions to understand transcriptional dynamics in developmental systems. Computational prediction of GRNs has been successfully applied to genome-wide gene expression measurements with the advent of microarrays and RNA-sequencing. However, these inferred networks are inaccurate and mostly based on correlative rather than causative interactions. In this review, we highlight three approaches that significantly impact GRN inference: (1) moving from one genome-wide functional modality, gene expression, to multi-omics, (2) single cell sequencing, to measure cell type-specific signals and predict context-specific GRNs, and (3) neural networks as flexible models. Together, these experimental and computational developments have the potential to significantly impact the quality of inferred GRNs. Ultimately, accurately modeling the regulatory interactions between transcription factors and their target genes will be essential to understand the role of transcription factors in driving developmental gene expression programs and to derive testable hypotheses for validation.
Identifiants
pubmed: 36695505
pii: 232497
doi: 10.1042/BST20210145
pmc: PMC9988001
doi:
Substances chimiques
Transcription Factors
0
Types de publication
Review
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1-12Informations de copyright
© 2023 The Author(s).
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