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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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HierCC: a multi-level clustering scheme for population assignments based on core genome MLST
Zhemin Zhou1, Jane Charlesworth1, Mark Achtman1
1Warwick Medical School, University of Warwick, Coventry CV4 7AL, UK.
Bioinformatics (Oxford, England)
|April 6, 2021
Summary
pHierCC provides Hierarchical Clustering (HierCC) for rapid, multi-level genome assignments in infectious disease surveillance. This scalable core genome typing method aids real-time tracking of bacterial populations using whole-genome sequencing data.
Area of Science:
- Microbiology
- Bioinformatics
- Genomics
Background:
- Infectious disease surveillance relies on whole-genome sequencing (WGS) databases.
- Real-time surveillance requires immediate hierarchical population structure assignments for new genome assemblies.
- Existing methods may lack scalability or real-time assignment capabilities.
Purpose of the Study:
- To present pHierCC, a computational pipeline for scalable, hierarchical genome clustering.
- To introduce HierCC, a clustering scheme based on core genome multi-locus typing.
- To develop HCCeval for optimizing cluster assignment thresholds.
Main Methods:
- pHierCC pipeline utilizing core genome multi-locus typing.
- HierCC scheme for incremental, static, multi-level cluster assignments.
- HCCeval tool for threshold identification.
Main Results:
- HierCC enables scalable, multi-level genome assignments.
- pHierCC and HierCC implemented in EnteroBase since 2018.
- Over 530,000 genomes from key bacterial genera (Salmonella, Escherichia/Shigella, Streptococcus, Clostridioides, Vibrio, Yersinia) have been genotyped.
Conclusions:
- pHierCC offers an efficient solution for real-time genomic surveillance.
- HierCC provides a robust framework for bacterial population structure analysis.
- The implemented system supports large-scale genomic epidemiology.
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