Modelling phylogeny in 16S rRNA gene sequencing datasets using string-based kernels
File(s)
Published version
Author(s)
Ish-Horowicz, Jonathan
Filippi, Sarah
Type
Journal Article
Abstract
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.
Date Issued
2026-01-07
Date Acceptance
2025-09-01
Citation
Journal of Theoretical Biology, 2026, 616
ISSN
0022-5193
Publisher
Elsevier
Journal / Book Title
Journal of Theoretical Biology
Volume
616
Copyright Statement
© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/40947007
PII: S0022-5193(25)00215-2
Subjects
Biology
HUMAN MICROBIOME
Kernel methods
Life Sciences & Biomedicine
Life Sciences & Biomedicine - Other Topics
Mathematical & Computational Biology
Microbiome data analysis
Non-parametric statistics
REGRESSION
Science & Technology
Publication Status
Published
Coverage Spatial
England
Article Number
112249
Date Publish Online
2025-09-12