Dataset Information
HierCC: a multi-level clustering scheme for population assignments based on core genome MLST.
Ontology highlight
ABSTRACT: Motivation
Routine infectious disease surveillance is increasingly based on large-scale whole-genome sequencing databases. Real-time surveillance would benefit from immediate assignments of each genome assembly to hierarchical population structures. Here we present pHierCC, a pipeline that defines a scalable clustering scheme, HierCC, based on core genome multi-locus typing that allows incremental, static, multi-level cluster assignments of genomes. We also present HCCeval, which identifies optimal thresholds for assigning genomes to cohesive HierCC clusters. HierCC was implemented in EnteroBase in 2018 and has since genotyped >530 000 genomes from Salmonella, Escherichia/Shigella, Streptococcus, Clostridioides, Vibrio and Yersinia.Availability and implementation
https://enterobase.warwick.ac.uk/ and Source code and instructions: https://github.com/zheminzhou/pHierCC.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Zhou Z
PROVIDER: S-EPMC8545296 | biostudies-literature | 2021 Oct
REPOSITORIES: biostudies-literature
Publications
HierCC: a multi-level clustering scheme for population assignments based on core genome MLST.
Bioinformatics (Oxford, England) 20211001 20
<h4>Motivation</h4>Routine infectious disease surveillance is increasingly based on large-scale whole-genome sequencing databases. Real-time surveillance would benefit from immediate assignments of each genome assembly to hierarchical population structures. Here we present pHierCC, a pipeline that defines a scalable clustering scheme, HierCC, based on core genome multi-locus typing that allows incremental, static, multi-level cluster assignments of genomes. We also present HCCeval, which identif ...[more]
