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EnteroBase: Hierarchical clustering of 100,000s of bacterial genomes into species/sub-species and populations

View ORCID ProfileMark Achtman, View ORCID ProfileZhemin Zhou, View ORCID ProfileJane Charlesworth, View ORCID ProfileLaura Baxter
doi: https://doi.org/10.1101/2022.01.11.475882
Mark Achtman
University of Warwick, Coventry CV4 7AL, UK
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  • For correspondence: m.achtman@warwick.ac.uk zheminzhou@qq.com
Zhemin Zhou
University of Warwick, Coventry CV4 7AL, UK
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  • For correspondence: m.achtman@warwick.ac.uk zheminzhou@qq.com
Jane Charlesworth
University of Warwick, Coventry CV4 7AL, UK
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Laura Baxter
University of Warwick, Coventry CV4 7AL, UK
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Abstract

The definition of bacterial species is traditionally a taxonomic issue while defining bacterial populations is done with population genetics. These assignments are species specific, and depend on the practitioner. Legacy multilocus sequence typing is commonly used to identify sequence types (STs) and clusters (ST Complexes). However, these approaches are not adequate for the millions of genomic sequences from bacterial pathogens that have been generated since 2012. EnteroBase (http://enterobase.warwick.ac.uk) automatically clusters core genome MLST alleles into hierarchical clusters (HierCC) after assembling annotated draft genomes from short read sequences. HierCC clusters span core sequence diversity from the species level down to individual transmission chains. Here we evaluate HierCC’s ability to correctly assign 100,000s of genomes to the species/subspecies and population levels for Salmonella, Clostridoides, Yersinia, Vibrio and Streptococcus. HierCC assignments were more consistent with maximum-likelihood super-trees of core SNPs or presence/absence of accessory genes than classical taxonomic assignments or 95% ANI. However, neither HierCC nor ANI were uniformly consistent with classical taxonomy of Streptococcus. HierCC was also consistent with legacy eBGs/ST Complexes in Salmonella or Escherichia and revealed differences in vertical inheritance of O serogroups. Thus, EnteroBase HierCC supports the automated identification of and assignment to species/subspecies and populations for multiple genera.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/zheminzhou/cgMLSA

  • http://enterobase.warwick.ac.uk

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 11, 2022.
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EnteroBase: Hierarchical clustering of 100,000s of bacterial genomes into species/sub-species and populations
Mark Achtman, Zhemin Zhou, Jane Charlesworth, Laura Baxter
bioRxiv 2022.01.11.475882; doi: https://doi.org/10.1101/2022.01.11.475882
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EnteroBase: Hierarchical clustering of 100,000s of bacterial genomes into species/sub-species and populations
Mark Achtman, Zhemin Zhou, Jane Charlesworth, Laura Baxter
bioRxiv 2022.01.11.475882; doi: https://doi.org/10.1101/2022.01.11.475882

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