Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, United States.
Quantitative and Computational Biosciences Graduate Program, Baylor College of Medicine, Houston, TX 77030, United States.
Bioinformatics. 2023 Aug 1;39(8). doi: 10.1093/bioinformatics/btad467.
In any population under selective pressure, a central challenge is to distinguish the genes that drive adaptation from others which, subject to population variation, harbor many neutral mutations de novo. We recently showed that such genes could be identified by supplementing information on mutational frequency with an evolutionary analysis of the likely functional impact of coding variants. This approach improved the discovery of driver genes in both lab-evolved and environmental Escherichia coli strains. To facilitate general adoption, we now developed ShinyBioHEAT, an R Shiny web-based application that enables identification of phenotype driving gene in two commonly used model bacteria, E.coli and Bacillus subtilis, with no specific computational skill requirements. ShinyBioHEAT not only supports transparent and interactive analysis of lab evolution data in E.coli and B.subtilis, but it also creates dynamic visualizations of mutational impact on protein structures, which add orthogonal checks on predicted drivers.
Code for ShinyBioHEAT is available at https://github.com/LichtargeLab/ShinyBioHEAT. The Shiny application is additionally hosted at http://bioheat.lichtargelab.org/.
在任何受到选择压力的群体中,一个核心挑战是区分那些驱动适应的基因与其他基因,这些基因在受到群体变异影响时会产生许多新的中性突变。我们最近表明,可以通过补充有关突变频率的信息,并对编码变异的潜在功能影响进行进化分析,从而识别出这些基因。这种方法提高了在实验室进化和环境大肠杆菌菌株中发现驱动基因的能力。为了方便广泛采用,我们现在开发了 ShinyBioHEAT,这是一个基于 R Shiny 的网络应用程序,可用于鉴定两种常用模型细菌大肠杆菌和枯草芽孢杆菌中的表型驱动基因,而无需特定的计算技能要求。ShinyBioHEAT 不仅支持对大肠杆菌和枯草芽孢杆菌的实验室进化数据进行透明和交互式分析,而且还创建了对蛋白质结构突变影响的动态可视化,这为预测的驱动因素提供了正交检查。
ShinyBioHEAT 的代码可在 https://github.com/LichtargeLab/ShinyBioHEAT 上获得。Shiny 应用程序还可在 http://bioheat.lichtargelab.org/ 上访问。