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Gerry Tonkin-Hill

@gerrythill.bsky.social

954 followers 561 following 20 posts

Group leader at Peter Mac & the Doherty Institute in Melbourne, Australia microbial genomics, statistics, machine learning

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Gerry Tonkin-Hill @gerrythill.bsky.social · 02/09/2026

StrainSpy’s GLMM framework enables straightforward integration of large, multi-cohort microbiome studies. Re-analysis of a large colorectal cancer dataset identified both known and novel strain-level associations, including in E. coli, with an accompanying enrichment of adhesin-encoding genes.

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Gerry Tonkin-Hill @gerrythill.bsky.social · 02/09/2026

Inspired by @jimshaw.bsky.social's Sylph paper, StrainSpy makes it easier to apply association tests to ANI estimates. Applying it to a well-characterised study of antibiotic exposure identified sub-species perturbations that would have been obscured by species-level abundance methods.

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Gerry Tonkin-Hill @gerrythill.bsky.social · 30/06/2026

Using MxSure, we estimated within-host substitution rates and transmission SNP thresholds for multiple commensal bacterial species, including Bifidobacterium longum and Bifidobacterium bifidum.

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Gerry Tonkin-Hill @gerrythill.bsky.social · 30/06/2026

In addition to accurately identifying strain replacement events in S. pneumoniae, we show that phylogenetically informed thresholds outperform methods that do not model the underlying evolutionary process, such as Youden's J statistic, which is often used in metagenomic transmission studies.

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Gerry Tonkin-Hill @gerrythill.bsky.social · 30/06/2026

Critically, unlike most existing approaches, MxSure distinguishes strain replacement from persistence.

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Gerry Tonkin-Hill @gerrythill.bsky.social · 22/08/2024

Applying TRACS to metagenomic data from FMT triads and the UK babybiome also revealed cryptic transmission of minority strains which were frequently missed by alternative tools. 4/4

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Gerry Tonkin-Hill @gerrythill.bsky.social · 22/08/2024

TRACS is designed to accurately estimate a lower bound on the genetic distances between strains at the SNP level from single isolate, population, and metagenomic sequencing data. Importantly, it effectively handles intra-species diversity within hosts. 2/4

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Gerry Tonkin-Hill @gerrythill.bsky.social · 09/08/2023

Although this was a conservative approach, there was not an obvious cancer specific microbial signature in the re-normalised data of 1,809 cancer samples.

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Gerry Tonkin-Hill @gerrythill.bsky.social · 09/08/2023

The Cancer Genome Atlas was not initially intended to investigate the microbiome and batch effects including hospital and sequencing centre correlate strongly with specific cancer types.

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