Using metagenomics data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. BiomeNet (Bayesian inference of metabolic Networks) is a novel hierarchical Bayesian model that models a sample as a unique mixture of complex metabolic systems (metabosystems). The metabosystems are composed of mixtures of tightly connected metabolic subnetworks. BiomeNet differs from previous methods by allowing researchers to discriminate groups of samples through metabolic patterns it discovers in the data, and by providing a built-in framework for interpreting them. Application of BiomeNet to human gut metagenomes revealed a metabosystem with greater prevalence among IBD patients. We inferred that this metabosystem is likely to be closely associated with the human gut epithelium, resistant to dietary interventions, and interfere with human uptake of an antioxidant connected to IBD.
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