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Updated: Dec 31, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Hash-Based Core Genome Multilocus Sequence Typing for Clostridium difficile.

David W Eyre1,2, Tim E A Peto2,3,4, Derrick W Crook2,3,4

  • 1Big Data Institute, University of Oxford, Oxford, United Kingdom david.eyre@bdi.ox.ac.uk.

Journal of Clinical Microbiology
|November 1, 2019
PubMed
Summary
This summary is machine-generated.

A new method, hash-core genome multilocus sequence typing (hash-cgMLST), rapidly identifies closely related pathogen genomes for infection surveillance. While efficient, it generates more false variants than mapping, requiring follow-up analysis for precise genetic relationships.

Keywords:
Clostridium difficilecore genome MLSTquality assurancewhole-genome sequencing

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Area of Science:

  • Microbiology
  • Genomics
  • Epidemiology

Background:

  • Pathogen whole-genome sequencing aids infection transmission studies.
  • Rapidly identifying closely related genomes is challenging.
  • Current methods require resource-intensive centralized databases.

Purpose of the Study:

  • To introduce and evaluate hash-cgMLST, a refined core genome multilocus sequence typing (cgMLST) method.
  • To compare hash-cgMLST performance against standard cgMLST and mapping-based approaches.
  • To assess hash-cgMLST's utility as a rapid surveillance tool for identifying infectious disease clusters.

Main Methods:

  • Developed hash-cgMLST by converting gene alleles to unique hashes (short strings).
  • Tested reproducibility and discriminatory power using Clostridium difficile isolates and hospital infection data.
  • Compared hash-cgMLST with reference-based mapping and different assemblers (SPAdes, SKESA).

Main Results:

  • Hash-cgMLST demonstrated comparable results to standard cgMLST with minimal performance loss.
  • Comparing replicate sequences, hash-cgMLST showed slightly more gene differences than SNPs from mapping.
  • Hash-cgMLST rapidly compared genomes (<1 min for 100,000), identifying potential transmission clusters.
  • False gene differences were reduced using the SKESA assembler.

Conclusions:

  • Hash-cgMLST is an effective tool for rapid identification of related pathogen genomes and surveillance.
  • The method offers a decentralized alternative to traditional cgMLST databases.
  • Mapping-based analysis may still be necessary for precise genetic relationship definition due to false variants.