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Modelling phylogeny in 16S rRNA gene sequencing datasets using string-based kernels

Year: 2025
Type: article
Abstract: • The proposed family of phylogeny-aware kernels leverages string kernels from natural language processing to encode phylogenetic relationships in microbiome datasets. • Simulation studies demonstrate that a kernel two-sample test using one of the proposed string-based kernels is sensitive to the phylogenetic scale at which differences between microbial populations occur, addressing a key limitation of traditional abundance-based only kernels. • The proposed kernels can be used within Gaussian Process regression to infer the distribution of bacterial-host phenotype effects across the phylogenetic tree, with validation on both simulated data and a real dataset predicting vaginal pH from bacterial community composition. • The relative Gaussian Process training objective using the proposed kernel vs an abundance-only kernel can serve as an indicator of whether the factors controlling a host trait are related to the observed 16S rRNA gene sequence or if they might be driven by other factors. • Open-source code is provided, allowing researchers to replicate all findings and apply the proposed kernels to new microbiome datasets. The bacterial microbiome is increasingly being recognised as a key factor in human health, driven in large part by datasets collected using 16S rRNA (ribosomal ribonucleic acid) gene sequencing, which enable cost-effective quantification of the composition of an individual’s bacterial community. One of the defining characteristics of 16S rRNA datasets is the evolutionary relationships that exist between taxa (phylogeny). Here, we demonstrate the utility of modelling these phylogenetic relationships in two statistical tasks (the two sample test and host trait prediction) and propose a novel family of kernels for analysing microbiome datasets by leveraging string kernels from the natural language processing literature. We show via simulation studies that a kernel two-sample test using the proposed kernel is sensitive to the phylogenetic scale of the difference between the two populations. In a second set of simulations we also show how Gaussian process modelling with string kernels can infer the distribution of bacterial-host effects across the phylogenetic tree and apply this approach to a real host-trait prediction task. The results in the paper can be reproduced by running the code at https://github.com/jonathanishhorowicz/modelling_phylogeny_in_16srrna_using_string_kernels .
DOI: 10.1016/j.jtbi.2025.112249
Language: English
Open Access status: hybrid
Estimated APC paid: $2,550
OpenAlex ID: W4414167130
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Cites: 35
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