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

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Authors: Allen, JP; Snitkin, E; Pincus, NB; Hauser, AR
Article Title: Forest and Trees: Exploring Bacterial Virulence with Genome-wide Association Studies and Machine Learning.
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.
Journal Title: Trends in microbiology
Publisher: Unknown  
Date Published: 2021