Computational tools for inferring transcription factor activity.
bioinformatic tools
gene regulation
gene regulatory networks
transcription factor activity
Journal
Proteomics
ISSN: 1615-9861
Titre abrégé: Proteomics
Pays: Germany
ID NLM: 101092707
Informations de publication
Date de publication:
14 Sep 2023
14 Sep 2023
Historique:
revised:
11
08
2023
received:
17
05
2023
accepted:
22
08
2023
pubmed:
14
9
2023
medline:
14
9
2023
entrez:
14
9
2023
Statut:
aheadofprint
Résumé
Transcription factors (TFs) are essential players in orchestrating the regulatory landscape in cells. Still, their exact modes of action and dependencies on other regulatory aspects remain elusive. Since TFs act cell type-specific and each TF has its own characteristics, untangling their regulatory interactions from an experimental point of view is laborious and convoluted. Thus, there is an ongoing development of computational tools that estimate transcription factor activity (TFA) from a variety of data modalities, either based on a mapping of TFs to their putative target genes or in a genome-wide, gene-unspecific fashion. These tools can help to gain insights into TF regulation and to prioritize candidates for experimental validation. We want to give an overview of available computational tools that estimate TFA, illustrate examples of their application, debate common result validation strategies, and discuss assumptions and concomitant limitations.
Identifiants
pubmed: 37706624
doi: 10.1002/pmic.202200462
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2200462Subventions
Organisme : NIDDK NIH HHS
Pays : United States
Organisme : NIDDK NIH HHS
Pays : United States
Informations de copyright
© 2023 The Authors. Proteomics published by Wiley-VCH GmbH.
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