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Volume 9, Issue 3

ShigaPass: an tool predicting serotypes from whole-genome sequencing assemblies Open Access

Abstract

is one of the commonest causes of diarrhoea worldwide and a major public health problem. serotyping is based on a standardized scheme that splits strains into four serogroups and 60 serotypes on the basis of biochemical tests and O-antigen structures. This conventional serotyping method is laborious, time-consuming, impossible to automate, and requires a high level of expertise. Whole-genome sequencing (WGS) is becoming more affordable and is now used for routine surveillance, opening up possibilities for the development of much-needed accurate rapid typing methods. Here, we describe ShigaPass, a new tool for predicting serotypes from WGS assemblies on the basis of gene cluster DNA sequences, phage and plasmid-encoded O-antigen modification genes, seven housekeeping genes (EnteroBase’s MLST scheme), alleles and clustered regularly interspaced short palindromic repeats (CRISPR) spacers. Using 4879 genomes, including 4716 reference strains and clinical isolates of characterized with a panel of biochemical tests and serotyped by slide agglutination, we show here that ShigaPass outperforms all existing tools, particularly for the identification of and serotypes, with a correct serotype assignment rate of 98.5 % and a sensitivity rate (i.e. ability to make any prediction) of 100 %.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): in silico serotyping , ShigaPass , Shigella and whole-genome sequencing
Funding
This study was supported by the:
  • Fulbright Association
    • Principle Award Recipient: ElisabethE Hansen
  • Fondation Le Roch-Les Mousquetaires
    • Principle Award Recipient: François-XavierWeill
  • Santé publique France
    • Principle Award Recipient: François-XavierWeill
  • Institut Pasteur
    • Principle Award Recipient: François-XavierWeill
© 2023 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution NonCommercial License.
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2023-03-23
2024-04-26
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ShigaPass: an in silico tool predicting Shigella serotypes from whole-genome sequencing assemblies
M Gen 9, 000961 (2023); https://doi.org/10.1099/mgen.0.000961
/content/journal/mgen/10.1099/mgen.0.000961
/content/journal/mgen/10.1099/mgen.0.000961
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