Strain-level transmission inference across multi-kingdom metagenomic data using TRACS

doi:10.1038/s41564-026-02339-x

Strain-level transmission inference across multi-kingdom metagenomic data using TRACS

2026-04-01

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Alexander E. Zarebski

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Sudaraka Mallawaarachchi

DOI：10.1038/s41564-026-02339-x

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Coexisting strains of the same species within metagenomic data pose a substantial challenge to inferring transmission of pathogenic and commensal microbes. Here we present TRAnsmision Clustering of Strains (TRACS), a highly accurate algorithm for estimating genetic distances between strains at the level of individual single nucleotide polymorphisms, which is robust to intra-species diversity within the host. Analysis of faecal microbiota transplantation datasets and extensive simulations demonstrates that TRACS outperforms existing methods. We use TRACS to infer transmission networks in patients colonized with multiple strains, including severe acute respiratory syndrome coronavirus 2 amplicon sequencing data, deep population sequencing data of Streptococcus pneumoniae and single-cell genome sequencing data from patients infected with Plasmodium falciparum. Applying TRACS to gut metagenomic samples from a mother-infant cohort revealed species-specific transmission rates and identified increased the persistence of Bifidobacterium breve in infants, a finding previously missed owing to the presence of multiple strains. Our study shows that TRACS can be used across microbial kingdoms to uncover strain dynamics.

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Strain-level transmission inference across multi-kingdom metagenomic data using TRACS

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