Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.
Skip to main content

Bacterial genomes vary extensively in terms of both gene content and gene sequence. This plasticity hampers the use of traditional SNP-based methods for identifying all genetic associations with phenotypic variation. Here we introduce a computationally scalable and widely applicable statistical method (SEER) for the identification of sequence elements that are significantly enriched in a phenotype of interest. SEER is applicable to tens of thousands of genomes by counting variable-length k-mers using a distributed string-mining algorithm. Robust options are provided for association analysis that also correct for the clonal population structure of bacteria. Using large collections of genomes of the major human pathogens Streptococcus pneumoniae and Streptococcus pyogenes, SEER identifies relevant previously characterized resistance determinants for several antibiotics and discovers potential novel factors related to the invasiveness of S. pyogenes. We thus demonstrate that our method can answer important biologically and medically relevant questions.

Original publication

DOI

10.1038/ncomms12797

Type

Journal article

Journal

Nature communications

Publication Date

16/09/2016

Volume

7

Addresses

Pathogen Genomics, Wellcome Trust Sanger Institute, Cambridge CB10 1SA, UK.

Keywords

Streptococcus pneumoniae, Streptococcus pyogenes, DNA, Bacterial, Nucleic Acid Amplification Techniques, Genome, Bacterial, Models, Genetic, Computer Simulation, Genome-Wide Association Study