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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-09-16

AUTHORS

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

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. More... »

PAGES

12797

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/ncomms12797

DOI

http://dx.doi.org/10.1038/ncomms12797

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1047980738

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/27633831


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