Machine learning identifies signatures of host adaptation in the bacterial pathogen Salmonella enterica.

ABSTRACT: Emerging pathogens are a major threat to public health, however understanding how pathogens adapt to new niches remains a challenge. New methods are urgently required to provide functional insights into pathogens from the massive genomic data sets now being generated from routine pathogen surveillance for epidemiological purposes. Here, we measure the burden of atypical mutations in protein coding genes across independently evolved Salmonella enterica lineages, and use these as input to train a random forest classifier to identify strains associated with extraintestinal disease. Members of the species fall along a continuum, from pathovars which cause gastrointestinal infection and low mortality, associated with a broad host-range, to those that cause invasive infection and high mortality,

SUBMITTER: Wheeler NE 

PROVIDER: S-EPMC5940178 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature