Sequence element enrichment analysis to determine the genetic basis of bacterial phenotypes
Author(s)
Type
Journal Article
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.
Date Issued
2016-09-16
Date Acceptance
2016-07-28
Citation
Nature Communications, 2016, 7 (1)
ISSN
2041-1723
Publisher
Nature Publishing Group
Journal / Book Title
Nature Communications
Volume
7
Issue
1
Copyright Statement
This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
License URL
Sponsor
Wellcome Trust
Medical Research Council (MRC)
Identifier
https://www.nature.com/articles/ncomms12797
Grant Number
104169/Z/14/Z
MR/K010174/1B
Subjects
Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
GENOME-WIDE ASSOCIATION
POSITIVE SELECTION
EXPRESSION
EVOLUTION
INFECTION
DISEASE
STREPTOCOCCI
VIRULENCE
FRAMEWORK
Computer Simulation
DNA, Bacterial
Genome, Bacterial
Genome-Wide Association Study
Models, Genetic
Nucleic Acid Amplification Techniques
Streptococcus pneumoniae
Streptococcus pyogenes
Streptococcus pneumoniae
Streptococcus pyogenes
DNA, Bacterial
Nucleic Acid Amplification Techniques
Genome, Bacterial
Models, Genetic
Computer Simulation
Genome-Wide Association Study
Publication Status
Published
Article Number
12797