Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Article number12797
Number of pages8
JournalNature Communications
Volume7
Publication statusPublished - 16 Sep 2016
MoE publication typeA1 Journal article-refereed

Researchers

  • John A. Lees
  • Minna Vehkala
  • Niko Välimäki
  • Simon R. Harris
  • Claire Chewapreecha
  • Nicholas J. Croucher
  • Pekka Marttinen

  • Mark R. Davies
  • Andrew C. Steer
  • Steven Y C Tong
  • Antti Honkela
  • Julian Parkhill
  • Stephen D. Bentley
  • Jukka Corander

Research units

  • University of Helsinki
  • University of Cambridge
  • Imperial College London
  • University of Melbourne
  • Murdoch Children's Research Institute
  • Charles Darwin University
  • University of Oslo
  • Wellcome Trust Sanger Institute

Abstract

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.

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