Studies of the human microbiome have revealed that even healthy individuals differ remarkably in the microbes that occupy habitats such as the gut, skin, and vagina. Much of this diversity remains unexplained, although diet, environment, host genetics, and early microbial exposure have all been implicated. Accordingly, to characterize the ecology of human-associated microbial communities, the Human Microbiome Project has analyzed the largest cohort and set of distinct, clinically relevant body habitats to date. We found the diversity and abundance of each habitat’s signature microbes to vary widely even among healthy subjects, with strong niche specialization both within and among individuals. The project encountered an estimated 81–99% of the genera, enzyme families, and community configurations occupied by the healthy Western microbiome. Metagenomic carriage of metabolic pathways was stable among individuals despite variation in community structure, and ethnic/racial background proved to be one of the strongest associations of both pathways and microbes with clinical metadata. These results thus delineate the range of structural and functional configurations normal in the microbial communities of a healthy population, enabling future characterization of the epidemiology, ecology, and translational applications of the human microbiome.

A variety of microbial communities and their genes (microbiome) exist throughout the human body, playing fundamental roles in human health and disease. The NIH funded Human Microbiome Project (HMP) Consortium has established a population-scale framework which catalyzed significant development of metagenomic protocols resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 to 18 body sites up to three times, which to date, have generated 5,177 microbial taxonomic profiles from 16S rRNA genes and over 3.5 Tb of metagenomic sequence. In parallel, approximately 800 human-associated reference genomes have been sequenced. Collectively, these data represent the largest resource to date describing the abundance and variety of the human microbiome, while providing a platform for current and future studies.

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations.

Summary The characterization of baseline microbial and functional diversity in the human microbiome has enabled studies of microbiome-related disease, microbial population diversity, biogeography, and molecular function. The NIH Human Microbiome Project (HMP) has provided one of the broadest such characterizations to date. Here, we introduce an expanded second phase of the study, abbreviated HMP1-II, comprising 1,631 new metagenomic samples (2,355 total) targeting diverse body sites with multiple time points in 265 individuals. We applied updated profiling and assembly methods to these data to provide new characterizations of microbiome personalization. Strain identification revealed distinct subspecies clades specific to body sites; it also quantified species with phylogenetic diversity under-represented in isolate genomes. Body-wide functional profiling classified pathways into universal, human-enriched, and body site-enriched subsets. Finally, temporal analysis decomposed microbial variation into rapidly variable, moderately variable, and stable subsets. This study furthers our knowledge of baseline human microbial diversity, thus enabling an understanding of personalized microbiome function and dynamics.

The human microbiome refers to the community of microorganisms including prokaryotes, viruses and microbial eukaryotes that populate the human body. The National Institutes of Health launched an initiative that focuses describing the diversity of microbial species associated with health and disease. The first phase of this initiative includes the sequencing of hundreds of microbial reference genomes, coupled to metagenomic sequencing from multiple body sites. Here we present results from an initial reference genome sequencing of 178 microbial genomes. From 547,968 predicted polypeptides that correspond to the gene complement of these strains “novel” polypeptides that had both unmasked sequence length > 100 amino acids and no BLASTP match to any non-reference entry in the nr subset were defined. This analysis resulted in a set of 30,867 polypeptides, of which 29,987 (~97%) were unique. In addition, this set of microbial genomes allows for ~ 40% of random sequences from the microbiome of the gastrointestinal tract to be associated with organisms based on the match criteria used. Insights into pan-genome analysis suggest that we are still far from saturating microbial species genetic datasets. In addition, the associated metrics and standards used by the group for quality assurance are presented.

The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification includes specific formal semantic rules to express meaningful disease models and has expanded from a single asserted classification to include multiple-inferred mechanistic disease classifications, thus providing novel perspectives on related diseases. Expansion of disease terms, alternative anatomy, cell type and genetic disease classifications and workflow automation highlight the updates for the DO since 2015. The enhanced breadth and depth of the DO’s knowledgebase has expanded the DO’s utility for exploring the multi-etiology of human disease, thus improving the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources and demonstrated by a 6.6× growth in DO’s user community since 2015. The DO’s continual integration of human disease knowledge, evidenced by the more than 200 SVN/GitHub releases/revisions, since previously reported in our DO 2015 NAR paper, includes the addition of 2650 new disease terms, a 30% increase of textual definitions, and an expanding suite of disease classification hierarchies constructed through defined logical axioms.

The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bioinformatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new ‘phylogenetic annotation’ process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources.

Pseudomonas syringae pv. phaseolicola, a gram-negative bacterial plant pathogen, is the causal agent of halo blight of bean. In this study, we report on the genome sequence of P. syringae pv. phaseolicola isolate 1448A, which encodes 5,353 open reading frames (ORFs) on one circular chromosome (5,928,787 bp) and two plasmids (131,950 bp and 51,711 bp). Comparative analyses with a phylogenetically divergent pathovar, P. syringae pv. tomato DC3000, revealed a strong degree of conservation at the gene and genome levels. In total, 4,133 ORFs were identified as putative orthologs in these two pathovars using a reciprocal best-hit method, with 3,941 ORFs present in conserved, syntenic blocks. Although these two pathovars are highly similar at the physiological level, they have distinct host ranges; 1448A causes disease in beans, and DC3000 is pathogenic on tomato and Arabidopsis. Examination of the complement of ORFs encoding virulence, fitness, and survival factors revealed a substantial, but not complete, overlap between these two pathovars. Another distinguishing feature between the two pathovars is their distinctive sets of transposable elements. With access to a fifth complete pseudomonad genome sequence, we were able to identify 3,567 ORFs that likely comprise the core Pseudomonas genome and 365 ORFs that are P. syringae specific.The gram-negative plant-pathogenic species Pseudomonas syringae is comprised of at least 50 pathovars that can be distinguished by their host ranges (30). Many P. syringae pathovars also contain several races characterized by their avirulence on different host cultivars. Genetic control of host specificity at the race-cultivar level, and possibly the pathovar-host species level, is conditioned by "gene-for-gene" interactions between avirulence genes in the pathogen and the corresponding resistance genes in the plant (35). In the last 2 decades, a number of pathogen avirulence genes, as well as the corresponding host resistance genes, have been cloned and identified (9, 13). Resistance gene products, regardless of whether they encode resistance to viral, bacterial, fungal, nematode, or insect pathogens, share similar structures and with few exceptions contain a leucine-rich repeat region (reviewed in reference 37), suggesting a conserved mechanism(s) for pathogen recognition and signal transduction events. In contrast, avirulence gene products share little sequence similarity, although it is well known that bacterial avirulence gene products, along with other virulence factors collectively termed effectors, are injected into the host cell via the specialized type III secretion system (TTSS) that is conserved among plant and animal pathogens (15). The P. syringae effectors are designated Avr (avirulence) or Hop (Hrp outer protein) according to a recently adopted nomenclature system (38). P. syringae effectors collectively are important to virulence, and increasing evidence suggests that these proteins are involved in suppression of host defense responses in compatible interactions with hos...