RT Journal Article SR Electronic T1 Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes JF bioRxiv FD Cold Spring Harbor Laboratory SP 038463 DO 10.1101/038463 A1 John A. Lees A1 Minna Vehkala A1 Niko Välimäki A1 Simon R. Harris A1 Claire Chewapreecha A1 Nicholas J. Croucher A1 Pekka Marttinen A1 Mark R. Davies A1 Andrew C. Steer A1 Stephen Y. C. Tong A1 Antti Honkela A1 Julian Parkhill A1 Stephen D. Bentley A1 Jukka Corander YR 2016 UL http://biorxiv.org/content/early/2016/03/02/038463.abstract AB 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 even 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 characterised 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.