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

Identifieur interne : 000F65 ( PubMed/Curation ); précédent : 000F64; suivant : 000F66

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

Auteurs : John A. Lees [Royaume-Uni] ; Minna Vehkala [Finlande] ; Niko V Lim Ki [Finlande] ; Simon R. Harris [Royaume-Uni] ; Claire Chewapreecha [Royaume-Uni] ; Nicholas J. Croucher [Royaume-Uni] ; Pekka Marttinen [Finlande] ; Mark R. Davies [Australie] ; Andrew C. Steer [Australie] ; Steven Y C. Tong [Australie] ; Antti Honkela [Finlande] ; Julian Parkhill [Royaume-Uni] ; Stephen D. Bentley [Royaume-Uni] ; Jukka Corander [Royaume-Uni]

Source :

Nature communications [ 2041-1723 ] ; 2016.

RBID : pubmed:27633831

Descripteurs français

English descriptors

KwdEn :
- Computer Simulation, DNA, Bacterial (genetics), Genome, Bacterial, Genome-Wide Association Study, Models, Genetic, Nucleic Acid Amplification Techniques, Streptococcus pneumoniae (genetics), Streptococcus pyogenes (genetics).
MESH :
- chemical , genetics : DNA, Bacterial.
- genetics : Streptococcus pneumoniae, Streptococcus pyogenes.
- Computer Simulation, Genome, Bacterial, Genome-Wide Association Study, Models, Genetic, Nucleic Acid Amplification Techniques.

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.

DOI: 10.1038/ncomms12797
PubMed: 27633831

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<front><div type="abstract" xml:lang="en">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.</div>
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<Abstract><AbstractText>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.</AbstractText>
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<AffiliationInfo><Affiliation>Group A Streptococcal Research Group, Murdoch Children's Research Institute, Parkville, Victoria 3052, Australia.</Affiliation>
</AffiliationInfo>
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<ForeName>Antti</ForeName>
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<AffiliationInfo><Affiliation>Helsinki Institute for Information Technology HIIT, Department of Computer Science, University of Helsinki, Helsinki FI-00014, Finland.</Affiliation>
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Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes.

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

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