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Updated: Jun 17, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Pan-genome analysis provides much higher strain typing resolution than multi-locus sequence typing.

Barry G Hall^1,2, Garth D Ehrlich^3,1, Fen Z Hu^3,1

¹Center for Genomic Sciences, Allegheny-Singer Research Institute, 320 East North Ave, Pittsburgh, PA 15212, USA.

Microbiology (Reading, England)

|December 19, 2009

Summary

A new bacterial strain typing method using distributed genes offers higher resolution than multi-locus sequence typing (MLST). This cost-effective approach provides better diagnostic and prognostic information for bacterial pathogens.

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Last Updated: Jun 17, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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Genotyping of Staphylococcus aureus by Ribosomal Spacer PCR (RS-PCR)

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Published on: November 4, 2016

Area of Science:

Microbiology
Genomics
Bioinformatics

Background:

Multi-locus sequence typing (MLST) is a common DNA-based method for bacterial strain identification.
MLST often lacks the resolution needed to differentiate closely related bacterial strains within a species.

Purpose of the Study:

To develop a high-resolution bacterial strain typing method.
To provide a cost-effective alternative to MLST for bacterial diagnostics.

Main Methods:

Developed a novel clustering method called neighbour grouping.
Utilized the presence or absence of distributed genes for strain typing.
Applied the method to six completely sequenced bacterial species genomes.

Main Results:

The distributed gene presence/absence method successfully resolved all strains within the six bacterial species.
Neighbour grouping demonstrated consistency with MLST but offered significantly higher resolution.
This method is compatible with low-cost microarray analyses.

Conclusions:

Strain typing based on distributed gene profiles is a high-resolution, practical alternative to MLST.
This approach can yield valuable diagnostic and prognostic data for pathogenic bacteria.
The method offers improved bacterial strain discrimination for epidemiological and clinical applications.