MOTIVATION: A bacterial polysaccharide utilization locus (PUL) is a set of physically linked genes that orchestrate the breakdown of a specific glycan. PULs are prevalent in the Bacteroidetes phylum and are key to the digestion of complex carbohydrates, notably by the human gut microbiota. A given Bacteroidetes genome can encode dozens of different PULs whose boundaries and precise gene content are difficult to predict. RESULTS: Here, we present a fully automated approach for PUL prediction using genomic context and domain annotation alone. By combining the detection of a pair of marker genes with operon prediction using intergenic distances, and queries to the carbohydrate-active enzymes database (www.cazy.org), our predictor achieved above 86% accuracy in two Bacteroides species with extensive experimental PUL characterization. AVAILABILITY AND IMPLEMENTATION: PUL predictions in 67 Bacteroidetes genomes from the human gut microbiota and two additional species, from the canine oral sphere and from the environment, are presented in our database accessible at www.cazy.org/PULDB/index.php.

Microbial communities carry out the majority of the biochemical activity on the planet, and they play integral roles in processes including metabolism and immune homeostasis in the human microbiome. Shotgun sequencing of such communities' metagenomes provides information complementary to organismal abundances from taxonomic markers, but the resulting data typically comprise short reads from hundreds of different organisms and are at best challenging to assemble comparably to single-organism genomes. Here, we describe an alternative approach to infer the functional and metabolic potential of a microbial community metagenome. We determined the gene families and pathways present or absent within a community, as well as their relative abundances, directly from short sequence reads. We validated this methodology using a collection of synthetic metagenomes, recovering the presence and abundance both of large pathways and of small functional modules with high accuracy. We subsequently applied this method, HUMAnN, to the microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals as part of the Human Microbiome Project (HMP). This provided a means to compare functional diversity and organismal ecology in the human microbiome, and we determined a core of 24 ubiquitously present modules. Core pathways were often implemented by different enzyme families within different body sites, and 168 functional modules and 196 metabolic pathways varied in metagenomic abundance specifically to one or more niches within the microbiome. These included glycosaminoglycan degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. An implementation of our methodology is available at http://huttenhower.sph.harvard.edu/humann. This provides a means to accurately and efficiently characterize microbial metabolic pathways and functional modules directly from high-throughput sequencing reads, enabling the determination of community roles in the HMP cohort and in future metagenomic studies.