Cryptococcus, a major cause of disseminated infections in immunocompromised patients, kills over 600,000 people per year worldwide. Genes involved in the virulence of the meningitis-causing fungus are being characterized at an increasing rate, and to date, at least 648 Cryptococcus gene names have been published. However, these data are scattered throughout the literature and are challenging to find. Furthermore, conflicts in locus identification exist, so that named genes have been subsequently published under new names or names associated with one locus have been used for another locus. To avoid these conflicts and to provide a central source of Cryptococcus gene information, we have collected all published Cryptococcus gene names from the scientific literature and associated them with standard Cryptococcus locus identifiers and have incorporated them into FungiDB (www.fungidb.org). FungiDB is a panfungal genome database that collects gene information and functional data and provides search tools for 61 species of fungi and oomycetes. We applied these published names to a manually curated ortholog set of all Cryptococcus species currently in FungiDB, including Cryptococcus neoformans var. neoformans strains JEC21 and B-3501A, C. neoformans var. grubii strain H99, and Cryptococcus gattii strains R265 and WM276, and have written brief descriptions of their functions. We also compiled a protocol for gene naming that summarizes guidelines proposed by members of the Cryptococcus research community. The centralization of genomic and literature-based information for Cryptococcus at FungiDB will help researchers communicate about genes of interest, such as those related to virulence, and will further facilitate research on the pathogen.

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.

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.