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Related Experiment Video

Updated: Jun 30, 2026

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dTOURS: Dense-region tagging for outbreak detection using ratio statistics.

Lukas Wagner1, Richa Agarwala2

  • 1Emeritus, National Center for Biotechnology Information, Bethesda, Maryland, United States of America.

Plos One
|May 13, 2025
PubMed
Summary

A new tool, dTOURS, improves foodborne outbreak detection by accurately identifying closely related bacterial isolates using single nucleotide polymorphism (SNP) analysis. This enhances public health surveillance for safer food.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Genomics

Background:

  • Food safety surveillance relies on collaborative public health efforts and global sequence data sharing.
  • Automated pipelines analyze pathogen genomes to detect outbreaks by identifying single nucleotide polymorphisms (SNPs).
  • Accurate identification of closely related isolates is crucial for effective outbreak analysis.

Purpose of the Study:

  • To develop a robust method for identifying horizontally transferred regions in fragmented bacterial genome assemblies.
  • To improve the accuracy of detecting closely related isolates for foodborne outbreak surveillance.
  • To compare the performance of the new method against existing tools.

Main Methods:

  • Developed dTOURS, adapting a ratio statistic to find regions of high SNP density in fragmented genomes.
  • Utilized simulations to determine optimal dTOURS parameters.
  • Validated dTOURS using five published bacterial outbreak datasets.

Main Results:

  • dTOURS effectively identifies horizontally transferred regions, crucial for accurate SNP-based isolate comparison.
  • Comparison with Gubbins indicated dTOURS is more robust for close isolate detection in outbreak analysis.
  • The Food and Drug Administration's method, while fast, demonstrated lower sensitivity compared to dTOURS.

Conclusions:

  • dTOURS offers a more sensitive and robust approach for identifying closely related bacterial isolates in foodborne outbreak investigations.
  • The method enhances the accuracy of public health surveillance for foodborne pathogens.
  • dTOURS improves upon existing methods for genomic epidemiology of foodborne diseases.