ArticleCommunications biology2025
TBtypeR: Sensitive detection and sublineage classification of Mycobacterium tuberculosis complex mixed-strain infections.
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Within-host pathogen population diversity predicts treatment response in Tuberculosis.medRxiv : the preprint server for health sciences · 2026Article
- PET-CT benchmarked detection and 5-year progression of asymptomatic tuberculosis: a longitudinal, prospective cohort study.The Lancet. Respiratory medicine · 2026Article
- Mixed-strain infection and heteroresistance in drug-resistant tuberculosis: insights from a whole-genome sequencing pilot.Frontiers in epidemiology · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Tuberculosis (TB) is typically attributed to a single infecting strain of the Mycobacterium tuberculosis Complex (MTBC), however mixed-strain infections are more common than reported due to the limitations of conventional diagnostics. While whole genome sequencing (WGS) methods have improved the detection of mixed-strain infections, existing tools struggle to reliably identify mixed-strain infections with frequencies below 10%. TBtypeR, a new tool, addresses this challenge by comparing WGS data to a phylogenetic SNP panel of over 10,000 sites and 164 MTBC phylotypes and using a model based on the binomial distribution to classify sublineage mixtures at frequencies as low as 1%. Extensive benchmarking shows TBtypeR outperforms current methods. Application to a published dataset of 5000 WGS samples identified 305 mixed-strain infections, six-fold higher than previously reported. The TBtypeR R package and a Nextflow pipeline are available at github.com/bahlolab/TBtypeR, providing a powerful tool for studying TB epidemiology and mixed-strain infections.
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Registered trials
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