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Treffer: motifbreakR v2: expanded variant analysis including indels and integrated evidence from transcription factor binding databases.

Title:
motifbreakR v2: expanded variant analysis including indels and integrated evidence from transcription factor binding databases.
Authors:
Coetzee SG; Department of Computational Biomedicine at Cedars-Sinai Medical Center, West Hollywood, CA 90069, United States., Hazelett DJ; Department of Computational Biomedicine at Cedars-Sinai Medical Center, West Hollywood, CA 90069, United States.
Source:
Bioinformatics advances [Bioinform Adv] 2024 Oct 23; Vol. 4 (1), pp. vbae162. Date of Electronic Publication: 2024 Oct 23 (Print Publication: 2024).
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9918282081306676 Publication Model: eCollection Cited Medium: Internet ISSN: 2635-0041 (Electronic) Linking ISSN: 26350041 NLM ISO Abbreviation: Bioinform Adv Subsets: PubMed not MEDLINE
Imprint Name(s):
Original Publication: [Oxford] : Oxford University Press : International Society for Computational Biology, [2021]-
References:
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Nucleic Acids Res. 2015 Jan;43(Database issue):D97-102. (PMID: 25361979)
Entry Date(s):
Date Created: 20241030 Latest Revision: 20241030
Update Code:
20250114
PubMed Central ID:
PMC11520234
DOI:
10.1093/bioadv/vbae162
PMID:
39474623
Database:
MEDLINE

Weitere Informationen

Motivation: motifbreakR scans genetic variants against position weight matrices of transcription factors (TFs) to determine the potential for the disruption of binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to query a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single-nucleotide variants on TF binding sites, in motifbreakR v2, we have updated the functionality.
Results: New features include the ability to query other types of complex genetic variants, such as short insertions and deletions. This capability allows modeling a more extensive array of variants that may have significant effects on TF binding. Additionally, predictions based on sequence preference alone can indicate many more potential binding events than observed. Adding information from DNA-binding sequencing datasets lends confidence to motif disruption prediction by demonstrating TF binding in cell lines and tissue types. Therefore, motifbreakR can directly query the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in motifbreakR , in addition to the existing interface, we implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.
Availability and Implementation: motifbreakR is implemented in R. Source code, documentation, and tutorials are available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/motifbreakR.html and GitHub at https://github.com/Simon-Coetzee/motifBreakR.
(© The Author(s) 2024. Published by Oxford University Press.)

None declared.