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Determining Streptococcus suis serotype from short-read whole-genome sequencing data

Overview of attention for article published in BMC Microbiology, July 2016
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Title
Determining Streptococcus suis serotype from short-read whole-genome sequencing data
Published in
BMC Microbiology, July 2016
DOI 10.1186/s12866-016-0782-8
Pubmed ID
Authors

Taryn B. T. Athey, Sarah Teatero, Sonia Lacouture, Daisuke Takamatsu, Marcelo Gottschalk, Nahuel Fittipaldi

Abstract

Streptococcus suis is divided into 29 serotypes based on a serological reaction against the capsular polysaccharide (CPS). Multiplex PCR tests targeting the cps locus are also used to determine S. suis serotypes, but they cannot differentiate between serotypes 1 and 14, and between serotypes 2 and 1/2. Here, we developed a pipeline permitting in silico serotype determination from whole-genome sequencing (WGS) short-read data that can readily identify all 29 S. suis serotypes. We sequenced the genomes of 121 strains representing all 29 known S. suis serotypes. We next combined available software into an automated pipeline permitting in silico serotyping of strains by differential alignment of short-read sequencing data to a custom S. suis cps loci database. Strains of serotype pairs 1 and 14, and 2 and 1/2 could be differentiated by a missense mutation in the cpsK gene. We report a 99 % match between coagglutination- and pipeline-determined serotypes for strains in our collection. We used 375 additional S. suis genomes downloaded from the NCBI's Sequence Read Archive (SRA) to validate the pipeline. Validation with SRA WGS data resulted in a 92 % match. Included pipeline subroutines permitted us to assess strain virulence marker content and obtain multilocus sequence typing directly from WGS data. Our pipeline permits rapid and accurate determination of S. suis serotype, and other lineage information, directly from WGS data. By discriminating between serotypes 1 and 14, and between serotypes 2 and 1/2, our approach solves a three-decade longstanding S. suis typing issue.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 2%
Unknown 57 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 16%
Researcher 8 14%
Student > Master 8 14%
Student > Bachelor 7 12%
Student > Postgraduate 3 5%
Other 7 12%
Unknown 16 28%
Readers by discipline Count As %
Veterinary Science and Veterinary Medicine 13 22%
Agricultural and Biological Sciences 10 17%
Immunology and Microbiology 6 10%
Biochemistry, Genetics and Molecular Biology 5 9%
Nursing and Health Professions 2 3%
Other 4 7%
Unknown 18 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 September 2017.
All research outputs
#18,466,238
of 22,881,154 outputs
Outputs from BMC Microbiology
#2,248
of 3,195 outputs
Outputs of similar age
#280,286
of 364,027 outputs
Outputs of similar age from BMC Microbiology
#71
of 96 outputs
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