MetaGeneHunt for protein domain annotation in short-read metagenomes.

The annotation of short-reads metagenomes is an essential process to understand the functional potential of sequenced microbial communities. Annotation techniques based solely on the identification of local matches tend to confound local sequence similarity and overall protein homology and thus don't mirror the complex multidomain architecture and the shuffling of functional domains in many protein families. Here, we present MetaGeneHunt to identify specific protein domains and to normalize the hit-counts based on the domain length. We used MetaGeneHunt to investigate the potential for carbohydrate processing in the mouse gastrointestinal tract. We sampled, sequenced, and analyzed the microbial communities associated with the bolus in the stomach, intestine, cecum, and colon of five captive mice. Focusing on Glycoside Hydrolases (GHs) we found that, across samples, 58.3% of the 4,726,023 short-read sequences matching with a GH domain-containing protein were located outside the domain of interest. Next, before comparing the samples, the counts of localized hits matching the domains of interest were normalized to account for the corresponding domain length. Microbial communities in the intestine and cecum displayed characteristic GH profiles matching distinct microbial assemblages. Conversely, the stomach and colon were associated with structurally and functionally more diverse and variable microbial communities. Across samples, despite fluctuations, changes in the functional potential for carbohydrate processing correlated with changes in community composition. Overall MetaGeneHunt is a new way to quickly and precisely identify discrete protein domains in sequenced metagenomes processed with MG-RAST. In addition, using the sister program "GeneHunt" to create custom Reference Annotation Table, MetaGeneHunt provides an unprecedented way to (re)investigate the precise distribution of any protein domain in short-reads metagenomes.

GeneHunt for rapid domain-specific annotation of glycoside hydrolases.

The identification of glycoside hydrolases (GHs) for efficient polysaccharide deconstruction is essential for the development of biofuels. Here, we investigate the potential of sequential HMM-profile identification for the rapid and precise identification of the multi-domain architecture of GHs from various datasets. First, as a validation, we successfully reannotated >98% of the biochemically characterized enzymes listed on the CAZy database. Next, we analyzed the 43 million non-redundant sequences from the M5nr data and identified 322,068 unique GHs. Finally, we searched 129 assembled metagenomes retrieved from MG-RAST for environmental GHs and identified 160,790 additional enzymes. Although most identified sequences corresponded to single domain enzymes, many contained several domains, including known accessory domains and some domains never identified in association with GH. Several sequences displayed multiple catalytic domains and few of these potential multi-activity proteins combined potentially synergistic domains. Finally, we produced and confirmed the biochemical activities of a GH5-GH10 cellulase-xylanase and a GH11-CE4 xylanase-esterase. Globally, this "gene to enzyme pipeline" provides a rationale for mining large datasets in order to identify new catalysts combining unique properties for the efficient deconstruction of polysaccharides.