Coetzee Simon G, Hazelett Dennis J
Department of Computational Biomedicine at Cedars-Sinai Medical Center, West Hollywood, CA 90069, United States.
Bioinform Adv. 2024 Oct 23;4(1):vbae162. doi: 10.1093/bioadv/vbae162. eCollection 2024.
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 v2, we have updated the functionality.
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, the ReMap2022 database for evidence that a TF matching the disrupted motif binds over the disrupting variant. Finally, in , 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.
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.
将基因变异与转录因子（TFs）的位置权重矩阵进行比对，以确定变异位点处结合被破坏的可能性。它利用Bioconductor软件包和注释套件来查询各种基因组和基序数据库。最初开发用于研究单核苷酸变异对TF结合位点的影响，在v2版本中，我们更新了功能。
新功能包括查询其他类型复杂基因变异的能力，如短插入和缺失。这种能力允许对可能对TF结合有重大影响的更广泛变异进行建模。此外，仅基于序列偏好的预测可能表明比观察到的更多潜在结合事件。通过展示细胞系和组织类型中的TF结合，添加来自DNA结合测序数据集的信息为基序破坏预测提供了信心。因此，ReMap2022数据库提供了与被破坏基序匹配的TF在破坏变异上结合的证据。最后，在……中，除了现有的界面，我们还实现了一个R/Shiny图形用户界面，以简化并增强对不同技能水平研究人员的访问。
在R中实现。源代码、文档和教程可在Bioconductor上获取，网址为https://bioconductor.org/packages/release/bioc/html/motifbreakR.html ，在GitHub上获取，网址为https://github.com/Simon-Coetzee/motifBreakR 。
