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Related Experiment Video

Updated: Oct 25, 2025

Subtyping of Campylobacter jejuni ssp. doylei Isolates Using Mass Spectrometry-based PhyloProteomics MSPP
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Centre-specific bacterial pathogen typing affects infection-control decision making.

Jordy P M Coolen1, Casper Jamin2, Paul H M Savelkoul2,3

  • 1Department of Medical Microbiology and Radboudumc Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands.

Microbial Genomics
|August 6, 2021
PubMed
Summary
This summary is machine-generated.

Whole-genome sequencing for bacterial surveillance shows significant discrepancies in outbreak detection among different bioinformatics workflows. Standardized analysis did not resolve inconsistencies in identifying bacterial clusters and resistance genes.

Keywords:
BioinformaticsInfection Prevention ControlOutbreak analysisProficiency testWhole genome sequencingbacterial typing

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Area of Science:

  • Microbiology and Infectious Diseases
  • Bioinformatics and Computational Biology
  • Genomics and Molecular Epidemiology

Background:

  • Whole-genome sequencing (WGS) is the standard for bacterial outbreak surveillance and infection prevention.
  • Implementation requires specialized bioinformatics tools and expertise.
  • Concordance of outbreak reports using diverse bioinformatics workflows is largely unknown.

Purpose of the Study:

  • To assess the concordance of bacterial whole-genome outbreak analysis across multiple Dutch healthcare centers.
  • To evaluate the impact of different bioinformatics workflows on outbreak detection and characterization.
  • To determine if standardized analysis methods can improve uniformity in outbreak cluster reporting.

Main Methods:

  • Multi-center proficiency testing involving 13 Dutch healthcare-affiliated centers.
  • Participants analyzed two randomized bacterial datasets (Klebsiella pneumoniae and Vancomycin-resistant Enterococcus faecium) using their own bioinformatics workflows.
  • Analysis included antimicrobial resistance, multi-locus sequence typing (MLST), and outbreak cluster identification; phylogenetic tree comparison (Kendall-Colijn distances) and SNP analysis were employed.

Main Results:

  • Significant discrepancies in reported outbreak clusters were observed between centers, even with similar bioinformatics workflows.
  • Stringent data filtering by some centers led to the failure in detecting key genes (e.g., extended-spectrum beta-lactamase) and MLST loci.
  • A standardized method for cluster determination on de novo assemblies did not achieve uniformity in outbreak-cluster composition.

Conclusions:

  • Current bioinformatics workflows exhibit substantial variability in bacterial outbreak analysis, impacting surveillance reliability.
  • Standardization efforts are needed to address discrepancies in WGS-based outbreak detection and characterization.
  • Further research is required to harmonize bioinformatics pipelines for consistent and accurate public health surveillance.