Yersinia
Yersinia performs genomic identification and characterization of isolates within the genus Yersinia by applying a 500-gene core-genome multilocus sequence typing (cgMLST) scheme for taxonomic assignment and phylogenetic analysis.
Key Features:
- Core-Genome Multilocus Sequence Typing (cgMLST): Utilizes a core subset of 500 shared genes across Yersinia species to enable precise phylogenetic analysis that distinguishes existing species and identifies novel taxa.
- Automated Taxonomic Assignment: Applies species-specific thresholds derived from cgMLST data to automate species-level and infra-specific (biotype and serotype) classification.
- Comprehensive Database Access: Contains cgMLST profiles and reference taxonomic information in BigsDB (https://bigsdb.pasteur.fr/yersinia).
- Validation and Phenotypic Resolution: Validated against phenotypic reference methods with 98.4% agreement for species identification and subtyping and resolves discrepancies caused by atypical biochemical characteristics or limited phenotypic resolution.
Scientific Applications:
- Species Identification: Identifies Yersinia species from genomic sequences, addressing limitations of phenotypic methods.
- Strain Characterization: Enables detailed subtyping at biotype and serotype levels to assess pathogenic potential and support epidemiological investigations.
- Surveillance and Research: Supports public health surveillance and research, demonstrated on 1843 isolates from the French National Surveillance System.
Methodology:
Genome sequencing of Yersinia isolates followed by cgMLST analysis using a 500-gene core scheme, with validation against phenotypic reference methods (98.4% agreement).
Topics
Details
- Added:
- 1/9/2020
- Last Updated:
- 1/17/2021
Operations
Publications
Savin C, Criscuolo A, Guglielmini J, Le Guern A, Carniel E, Pizarro-Cerdá J, Brisse S. Genus-wide Yersinia core-genome multilocus sequence typing for species identification and strain characterization. Microbial Genomics. 2019;5(10). doi:10.1099/mgen.0.000301. PMID:31580794. PMCID:PMC6861861.