Identifying Differentially Abundant Metabolic Pathways in Metagenomic Datasets

Enabled by rapid advances in sequencing technology, metagenomic studies aim to characterize entire communities of microbes bypassing the need for culturing individual bacterial members. One major goal of such studies is to identify specific functional adaptations of microbial communities to their habitats. Here we describe a powerful analytical method (MetaPath) that can identify differentially abundant pathways in metagenomic data-sets, relying on a combination of metagenomic sequence data and prior metabolic pathway knowledge. We show that MetaPath outperforms other common approaches when evaluated on simulated datasets. We also demonstrate the power of our methods in analyzing two, publicly available, metagenomic datasets: a comparison of the gut microbiome of obese and lean twins; and a comparison of the gut microbiome of infant and adult subjects. We demonstrate that the subpathways identified by our method provide valuable insights into the biological activities of the microbiome.