Coetzee Simon G, Hazelett Dennis J
Department of Computational Biomedicine at Cedars-Sinai Medical Center.
Cancer Prevention and Control - Samuel Oschin Cancer Center, Cedars-Sinai.
ArXiv. 2024 Jul 3:arXiv:2407.03441v1.
is a software tool that scans genetic variants against position weight matrices of transcription factors (TF) to determine the potential for the disruption of TF binding at the site of the variant. It leverages the Bioconductor suite of software packages and annotations to operate across a diverse array of genomes and motif databases. Initially developed to interrogate the effect of single nucleotide variants (common and rare SNVs) on potential TF binding sites, in v2, we have updated the functionality. New features include the ability to query other types of more complex genetic variants, such as short insertions and deletions (indels). This function allows modeling a more extensive array of variants that may have more significant effects on TF binding. Additionally, while TF binding is based partly on sequence preference, predictions of TF binding 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, implements querying 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 have implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.
是一种软件工具,它针对转录因子(TF)的位置权重矩阵扫描遗传变异,以确定变异位点处TF结合被破坏的可能性。它利用Bioconductor软件包和注释套件在各种基因组和基序数据库中运行。最初开发用于研究单核苷酸变异(常见和罕见的SNV)对潜在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更复杂遗传变异的能力,例如短插入和缺失(indels)。此功能允许对可能对TF结合有更显著影响的更广泛变异阵列进行建模。此外,虽然TF结合部分基于序列偏好,但仅基于序列偏好预测TF结合可能会显示出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,实现了查询ReMap2022数据库,以获取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,除了现有的界面外,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。