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Updated: Apr 26, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Strain-level transmission inference across multi-kingdom metagenomic data using TRACS.

Gerry Tonkin-Hill1,2,3,4, Yan Shao5, Alexander E Zarebski6

  • 1Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia. gtonkin@unimelb.edu.au.

Nature Microbiology
|April 24, 2026
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Summary

TRAnsmision Clustering of Strains (TRACS) accurately estimates genetic distances between microbial strains, improving pathogen transmission inference. This new algorithm reveals previously hidden strain dynamics in complex microbial communities.

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Area of Science:

  • Microbiology
  • Genomics
  • Bioinformatics

Background:

  • Metagenomic data analysis is challenged by coexisting strains of the same species.
  • Accurate inference of microbial transmission is crucial for understanding disease spread and community dynamics.

Purpose of the Study:

  • To introduce TRAnsmision Clustering of Strains (TRACS), a novel algorithm for precise strain-level genetic distance estimation.
  • To demonstrate TRACS's superior performance compared to existing methods in analyzing intra-species diversity.

Main Methods:

  • Developed TRACS algorithm for estimating genetic distances at the single nucleotide polymorphism level.
  • Validated TRACS using faecal microbiota transplantation datasets and extensive simulations.
  • Applied TRACS to diverse datasets including SARS-CoV-2, Streptococcus pneumoniae, Plasmodium falciparum, and mother-infant gut metagenomes.

Main Results:

  • TRACS significantly outperforms existing methods in accuracy and robustness to intra-species diversity.
  • Inferred transmission networks in patients with multiple coexisting strains across different microbial kingdoms.
  • Identified species-specific transmission rates and increased Bifidobacterium breve persistence in infants.

Conclusions:

  • TRACS provides a powerful tool for uncovering microbial strain dynamics in complex communities.
  • The algorithm enhances our ability to track pathogen and commensal microbe transmission.
  • TRACS has broad applicability across various microbial sequencing datasets and research areas.