Revised computational metagenomic processing uncovers hidden and biologically meaningful functional variation in the human microbiome

Background: Recent metagenomic analyses of the human gut microbiome identified striking variability in its taxonomic composition across individuals. Notably, however, these studies often reported marked functional uniformity, with relatively little variation in the microbiome's gene composition or in its overall metabolic capacity. Results: Here, we address this surprising discrepancy between taxonomic and functional variations and set out to track its origins. Specifically, we demonstrate that the functional uniformity observed in microbiome studies can be attributed, at least partly, to common computational metagenomic processing procedures that mask true functional variation across microbiome samples. We identify several such procedures, including commonly used practices for gene abundance normalization, mapping of gene families to functional pathways, and gene family aggregation. We show that accounting for these factors and using revised metagenomic processing procedures uncovers such hidden functional variation, significantly increasing observed variation in the abundance of functional elements across samples. Importantly, we find that this uncovered variation is biologically meaningful and that it is associated with both host identity and health. Conclusions: Accurate characterization of functional variation in the microbiome is essential for comparative metagenomic analyses in health and disease. Our finding that metagenomic processing procedures mask underlying and biologically meaningful functional variation therefore highlights an important challenge such studies may face. Alternative schemes for metagenomic processing that uncover this hidden functional variation can facilitate improved metagenomic analysis and help pinpoint disease-and host-associated shifts in the microbiome's functional capacity.