Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning

Trends Microbiol. 2021 Jul;29(7):621-633. doi: 10.1016/j.tim.2020.12.002. Epub 2021 Jan 14.

Abstract

The advent of inexpensive and rapid sequencing technologies has allowed bacterial whole-genome sequences to be generated at an unprecedented pace. This wealth of information has revealed an unanticipated degree of strain-to-strain genetic diversity within many bacterial species. Awareness of this genetic heterogeneity has corresponded with a greater appreciation of intraspecies variation in virulence. A number of comparative genomic strategies have been developed to link these genotypic and pathogenic differences with the aim of discovering novel virulence factors. Here, we review recent advances in comparative genomic approaches to identify bacterial virulence determinants, with a focus on genome-wide association studies and machine learning.

Keywords: bacteria; genomics; virulence.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Bacteria / classification
  • Bacteria / genetics*
  • Bacteria / pathogenicity*
  • Genetic Variation
  • Genome, Bacterial*
  • Genome-Wide Association Study / methods*
  • Genomics
  • Genotype
  • Machine Learning*
  • Phylogeny
  • Virulence / genetics
  • Virulence Factors / genetics*

Substances

  • Virulence Factors