4.4 Article

MutVis: Automated framework for analysis and visualization of mutational signatures in pathogenic bacterial strains

Journal

INFECTION GENETICS AND EVOLUTION
Volume 91, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.meegid.2021.104805

Keywords

Mutational signatures; Pathogens; BPS; Transition and transversion; Mutation count matrix

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MutVis is an integrated, automated, open-source, and user-friendly framework for analyzing mutational signatures from bacterial whole genome next generation sequencing data. It supports variant calling, Ti and Tv graphical representation, generation of mutational count matrix, and graphical visualization of BPSs, among other features.
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/Mut Vis.

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