WEHI Bioinformatics and Computational Biology Special Seminar hosted by Mengbo Li, Jinjin Chen & Saskia Freytag

Dr Gerry Tonkin-Hill

Research Group Leader – Peter MacCallum Cancer Centre

Microbial diversity during transmission, treatment and disease

 

Davis Auditorium

Join via ZOOM

Including Q&A session

Differences in the genomic makeup of microbial strains can profoundly alter their behaviour, ecological roles, and consequences for human health. Traditionally, a species’ relative abundance has been used to identify associations between the microbiome and disease. However, this approach overlooks intra-species genetic variation and is susceptible to spurious correlations arising from the compositional nature of abundance data. Recently developed algorithms now enable rapid and accurate estimation of strain-level Average Nucleotide Identity (containment ANI) between metagenomic samples and reference databases. Despite its value as an orthogonal metric for strain-level microbiome analysis, methods for conducting such association studies remain limited.

To address this, we developed StrainSpy, an algorithm that links containment ANI to phenotypes of interest across a range of study designs. Using extensive simulations, we demonstrate that StrainSpy is more accurate than alternative approaches. Re-analysis of a study examining gut microbiota recovery in 12 healthy adults following antibiotic exposure revealed novel strain-level associations, including a reduction in intraspecies diversity despite species persistence. Notably, using StrainSpy, we found that certain previously reported associations were confounded by microbial load. We further applied StrainSpy to consider a pooled analysis of 3,414 stool metagenomes from 17 cohorts to identify strain-level associations with colorectal cancer. In addition to uncovering both known and novel associations, we show that a machine learning model incorporating containment ANI and strain presence–absence achieves similar accuracy in distinguishing colorectal cancer patients from healthy individuals as models based on abundance data.

Gerry’s group at the Peter MacCallum Cancer Centre and the Peter Doherty Institute for Infection and Immunity focuses on the development and application of statistical and computational methods to study the interactions between humans and microbes. Specifically, the group is interested in how microbes evolve within and transmit between people and the impacts of pharmaceutical and surgical interventions. Gerry has developed several popular computational tools to infer pathogen population structure, transmission networks and bacterial pangenomes. He completed his PhD in Mathematical Genomics and Medicine at the University of Cambridge and the Wellcome Sanger Institute. Prior to this, he completed a Masters of statistics at the University of Melbourne and WEHI.

All welcome!