MotifbreakR v2：扩展能力与数据库整合
MotifbreakR v2: extended capability and database integration
AI总结：
MotifbreakR v2 扩展了遗传变异对转录因子结合影响的分析能力，新增对插入缺失等复杂变异的支持，并整合 ReMap2022 数据库和 R/Shiny 图形界面，提升预测可信度与易用性。
摘要：
MotifbreakR 是一款软件工具，它根据转录因子（TF）的位置权重矩阵扫描遗传变异，以确定在变异位点破坏 TF 结合的潜力。它利用 Bioconductor 软件包和注释套件，在多种基因组和 motif 数据库上运行。最初开发该工具是为了研究单核苷酸变异（常见和罕见 SNV）对潜在 TF 结合位点的影响，在 motifbreakR v2 中，我们更新了功能。新功能包括能够查询其他类型的更复杂遗传变异，例如短插入和缺失（indels）。该功能允许对可能对 TF 结合产生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，但仅基于序列偏好的 TF 结合预测可能表明比观察到的更多的潜在结合事件。添加来自 DNA 结合测序数据集的信息，通过展示细胞系和组织类型中的 TF 结合，为 motif 破坏预测增加了可信度。因此，motifbreakR 实现了查询 ReMap2022 数据库，以获取与破坏的 motif 匹配的 TF 在破坏性变异上结合的证据。最后，在 motifbreakR 中，除了现有接口外，我们还实现了一个 R/Shiny 图形用户界面，以简化并增强不同技能水平研究人员的访问。
英文摘要：
MotifbreakR 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 motifbreakR 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, motifbreakR implements querying 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 have implemented an R/Shiny graphical user interface to simplify and enhance access to researchers with different skill sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
