Integrating phylogenetic and functional data in microbiome studies.

Area of Science:

• Microbiome Research
• Bioinformatics
• Computational Biology

Background:

• Analyzing microbiome functional data is crucial for understanding microbial communities.
• Distinguishing explanations for functional profile variations solely from function data is challenging.
• Existing methods may not robustly identify functions under selection.

Purpose of the Study:

• To develop a robust computational package, POMS, for identifying enriched microbial functions.
• To incorporate phylogenetic information to improve the identification of functions associated with sample groups.
• To provide tools for analyzing functional and taxonomic data simultaneously.

Main Methods:

• Developed POMS R package implementing multiple phylogeny-aware frameworks.
• Utilized an extended balance-tree workflow integrating functional and taxonomic information.
• Included a workflow for phylogenetic regression analysis.

Main Results:

• POMS accurately identifies gene families conferring selective advantages compared to existing tools, demonstrated with simulated data.
• The package successfully identifies functions under strong selection in real-world metagenomics datasets.
• Phylogeny-aware approaches enhance the robustness of identifying enriched functions across taxonomic lineages.

Conclusions:

• The POMS package offers a more robust method for identifying enriched microbial functions.
• Integrating functional, taxonomic, and phylogenetic data improves the detection of selection pressures in microbiomes.
• POMS is a valuable tool for microbiome research, facilitating the discovery of functionally important genes.