Department of Biotechnology, RV College of Engineering, Bengaluru, Karnataka 560059, India.
Department of Biotechnology, RV College of Engineering, Bengaluru, Karnataka 560059, India..
Infect Genet Evol. 2021 Jul;91:104805. doi: 10.1016/j.meegid.2021.104805. Epub 2021 Mar 6.
In recent years, mutational signature analysis has become a routine practice in cancer genomics for classification and diagnosis. Characterizing mutational signatures across species or within genomes of a bacteria helps in understanding their evolution and adaptation. However, an integrated framework for analysis and visualization of mutational signatures in bacterial genome is lacking. Hence, we aim to develop an integrated, automated, open-source and user-friendly framework called MutVis to analyze mutational signatures from bacterial whole genome next generation sequencing data. The current framework integrates various publicly available packages using Snakemake workflow management software, Python and R scripting. MutVis supports variant calling, transition (Ti) and transversion (Tv) graphical representation, generation of mutational count matrix, graphical visualization of base-pair substitution spectrum (BPSs) and mutation signatures extraction. TvTi plots provide the 6 base substitution classification for both genome and gene level. Further resolution of base pair substitution classification is provided as 96-profile BPSs plot. Mutation signatures is derived based on the characteristic pattern observed in BPSs using non-negative matrix factorization. Relative contribution of signatures is given as hierarchically clustered heatmap. This provides information on active signatures in the individual given sample and classify samples according to signature contributions. We demonstrated the MutVis framework using geographically different strains of Mycobacterium tuberculosis, downloaded from PATRIC TB-ARC Antibiotic Resistance Catalog (n = 963). The current framework can be used to study mutation biases and characteristic mutational signatures in bacterial genomes and is freely available at https://github.com/AkshathaPrasanna/MutVis.
近年来，突变特征分析已成为癌症基因组学中分类和诊断的常规实践。在不同物种或细菌基因组内对突变特征进行特征化有助于了解它们的进化和适应。然而，缺乏用于分析和可视化细菌基因组中突变特征的综合框架。因此，我们旨在开发一个集成的、自动化的、开源的和用户友好的框架，称为 MutVis，用于分析来自细菌全基因组下一代测序数据的突变特征。当前框架使用 Snakemake 工作流程管理软件、Python 和 R 脚本集成了各种可用的公共软件包。MutVis 支持变体调用、转换 (Ti) 和颠换 (Tv) 图形表示、突变计数矩阵的生成、碱基对替换谱 (BPS) 的图形可视化和突变特征提取。TvTi 图提供了基因组和基因水平的 6 种碱基替换分类。进一步提供了 96 个特征 BPS 图作为碱基对替换分类的分辨率。突变特征是根据 BPS 中观察到的特征模式使用非负矩阵分解得出的。特征的相对贡献以层次聚类热图表示。这提供了有关单个样本中活跃特征的信息，并根据特征贡献对样本进行分类。我们使用从 PATRIC TB-ARC 抗生素耐药目录下载的地理位置不同的结核分枝杆菌菌株 (n=963) 展示了 MutVis 框架。该框架可用于研究细菌基因组中的突变偏向和特征突变特征，并可在 https://github.com/AkshathaPrasanna/MutVis 上免费获得。
