Intraspecies associations from strain-rich metagenome samples.

Area of Science:

• Microbiome research
• Metagenomics
• Computational biology

Background:

• Genetically distinct microbial strains within a species exhibit significant phenotypic variation.
• This variation limits the utility of species-level microbiome analysis for health and disease association studies.
• Current strain-resolved metagenomic methods present a trade-off between detecting novel strains and robustness in complex samples.

Purpose of the Study:

• To introduce PHLAME, a novel computational method that integrates reference-based analysis with novelty detection for strain-resolved metagenomics.
• To address the limitations of existing methods by combining robustness with the ability to identify previously unknown microbial diversity.
• To enhance the characterization of intraspecies dynamics within complex microbial communities.

Main Methods:

• PHLAME defines microbial clades at multiple phylogenetic levels.
• It employs a probabilistic, mutation-based framework to quantify strain novelty relative to the nearest reference.
• The method was validated using publicly available human skin and vaginal metagenomic datasets.

Main Results:

• PHLAME successfully identified previously undetected associations between microbial clades and factors such as coexisting species, geographic location, and host age.
• The method demonstrated improved accuracy in quantifying strain novelty compared to existing approaches.
• Application to human microbiome data revealed new insights into intraspecies diversity and dynamics.

Conclusions:

• PHLAME offers a powerful approach to bridge the gap between reference-based and *de novo* strain analysis in metagenomics.
• The method enhances the ability to detect and characterize novel microbial strains and their ecological associations.
• PHLAME will facilitate deeper mechanistic insights from the growing volume of metagenomic data, particularly in previously inaccessible environments.