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Maast: genotyping thousands of microbial strains efficiently.
Shi, Zhou Jason; Nayfach, Stephen; Pollard, Katherine S.
Affiliation
  • Shi ZJ; Chan Zuckerberg Biohub, San Francisco, CA, USA.
  • Nayfach S; Gladstone Institutes of Data Science and Biotechnology, San Francisco, CA, USA.
  • Pollard KS; Joint Genome Institute, Department of Energy, Walnut Creek, CA, USA.
Genome Biol ; 24(1): 186, 2023 08 10.
Article in En | MEDLINE | ID: mdl-37563669
ABSTRACT
Existing single nucleotide polymorphism (SNP) genotyping algorithms do not scale for species with thousands of sequenced strains, nor do they account for conspecific redundancy. Here we present a bioinformatics tool, Maast, which empowers population genetic meta-analysis of microbes at an unrivaled scale. Maast implements a novel algorithm to heuristically identify a minimal set of diverse conspecific genomes, then constructs a reliable SNP panel for each species, and enables rapid and accurate genotyping using a hybrid of whole-genome alignment and k-mer exact matching. We demonstrate Maast's utility by genotyping thousands of Helicobacter pylori strains and tracking SARS-CoV-2 diversification.
Subject(s)

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Systematic_reviews Limits: Humans Language: En Journal: Genome Biol Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2023 Type: Article Affiliation country: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Systematic_reviews Limits: Humans Language: En Journal: Genome Biol Journal subject: BIOLOGIA MOLECULAR / GENETICA Year: 2023 Type: Article Affiliation country: United States