Created on 2nd March 2016
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 pathogen Streptococcus pneumoniae, SEER identifies relevant previously characterised resistance determinants for several antibiotics. We thus demonstrate that our method can answer important biologically and medically relevant questions.Show more
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