Reproducibly sampling SARS-CoV-2 genomes across time, geography, and viral diversity

• DOI: 10.12688/f1000research.24751.1
• Publication Date: 2020-06-29T16:25:08Z
• Authors: Evan Bolyen; Matthew R. Dillon; Nicholas A. Bokulich; Jason T. Ladner; Brendan B. Larsen; Crystal M. Hepp; Darrin Lemmer; Jason W. Sahl; Andrew Sanchez; Chris Holdgraf; Chris Sewell; Aakash G. Choudhury; John Stachurski; Matthew McKay; David M. Engelthaler; Michael Worobey; Paul Keim; J. Gregory Caporaso
• Keywords: Ensembl

<ns3:p>The COVID-19 pandemic has led to a rapid accumulation of SARS-CoV-2 genomes, enabling genomic epidemiology on local and global scales. Collections genomes from resources such as GISAID must be subsampled enable computationally feasible phylogenetic other analyses. We present genome-sampler, software package that supports sampling collections viral across multiple axes including time genome isolation, location diversity. The is modular in design so these or future approaches can applied independently combined (or replaced with random approach) facilitate custom workflows benchmarking. genome-sampler written QIIME 2 plugin, ensuring its application fully reproducible through 2’s unique retrospective data provenance tracking system. installed conda environment macOS Linux systems. A complete default pipeline available Snakemake workflow, subsampling achieved using single command. open source, free for all use, at <ns3:ext-link xmlns:ns4="http://www.w3.org/1999/xlink" ext-link-type="uri" ns4:href="https://caporasolab.us/genome-sampler">https://caporasolab.us/genome-sampler</ns3:ext-link>. hope this will research support evaluation epidemiology.</ns3:p>