ArticleBioinformatics (Oxford, England)2022
Integrating phylogenetic and functional data in microbiome studies.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Phylogenize2: robust phylogenetic methods link genes to phenotypes across host-associated and environmental microbiomes.bioRxiv : the preprint server for biology · 2026Article
- A robust, sensitive phylogenetic method enables gene-level metagenomic analyses.bioRxiv : the preprint server for biology · 2026Article
- PhyloFunc: phylogeny-informed functional distance as a new ecological metric for metaproteomic data analysis.Microbiome · 2025Article
- Influence of Saccharomyces cerevisiae CNCM I-1077 on the fecal pH, markers of gut permeability, fecal microbiota, and markers of systemic inflammation in sedentary horses fed a high-starch diet.Journal of animal science · 2025Article
- Efficient computation of contributional diversity metrics from microbiome data with FuncDiv.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
4 authors.
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Abstract
motivationMicrobiome functional data are frequently analyzed to identify associations between microbial functions (e.g. genes) and sample groups of interest. However, it is challenging to distinguish between different possible explanations for variation in community-wide functional profiles by considering functions alone. To help address this problem, we have developed POMS, a package that implements multiple phylogeny-aware frameworks to more robustly identify enriched functions.
resultsThe key contribution is an extended balance-tree workflow that incorporates functional and taxonomic information to identify functions that are consistently enriched in sample groups across independent taxonomic lineages. Our package also includes a workflow for running phylogenetic regression. Based on simulated data we demonstrate that these approaches more accurately identify gene families that confer a selective advantage compared with commonly used tools. We also show that POMS in particular can identify enriched functions in real-world metagenomics datasets that are potential targets of strong selection on multiple members of the microbiome. AVAILABILITY AND IMPLEMENTATION: These workflows are freely available in the POMS R package at https://github.com/gavinmdouglas/POMS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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