Rapid identification of enteric bacteria from whole genome sequences using average nucleotide identity metrics.

ABSTRACT: Identification of enteric bacteria species by whole genome sequence (WGS) analysis requires a rapid and an easily standardized approach. We leveraged the principles of average nucleotide identity using MUMmer (ANIm) software, which calculates the percent bases aligned between two bacterial genomes and their corresponding ANI values, to set threshold values for determining species consistent with the conventional identification methods of known species. The performance of species identification was evaluated using two datasets: the Reference Genome Dataset v2 (RGDv2), consisting of 43 enteric genome assemblies representing 32 species, and the Test Genome Dataset (TGDv1), comprising 454 genome assemblies which is designed to represent all species needed to query for identification, as well a

SUBMITTER: Lindsey RL 

PROVIDER: S-EPMC10752928 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature