期刊
NATURE COMMUNICATIONS
卷 7, 期 -, 页码 -出版社
NATURE PUBLISHING GROUP
DOI: 10.1038/ncomms12797
关键词
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资金
- Wellcome Trust [098051, 107376/Z/15/Z]
- MRC grant [1365620]
- ERC grant [239784]
- Academy of Finland [287665]
- COIN Centre of Excellence
- Medical Research Council [1365620, MR/K010174/1B] Funding Source: researchfish
- Wellcome Trust [107376/Z/15/Z] Funding Source: researchfish
- Academy of Finland (AKA) [287665, 287665] Funding Source: Academy of Finland (AKA)
- Wellcome Trust [107376/Z/15/Z] Funding Source: Wellcome Trust
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.
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