The Candida Genome Database: Annotation and Visualization Updates

The Candida Genome Database (CGD; www.candidagenome.org ) is unique in being both a model organism database and a fungal pathogen database. As a fungal pathogen database, CGD hosts locus pages for five species of the best-studied pathogenic fungi in the Candida group. As a model organism database, the species Candida albicans serves as a model both for other Candida spp. and for non- Candida fungi that form biofilms and undergo routine morphogenic switching from the planktonic form to the filamentous form, which is not done by other model yeasts. As pathogenic Candida species have become increasingly drug resistant, the high lethality of invasive candidiasis in immunocompromised people is increasingly alarming. There is a pressing need for additional research into basic Candida biology, epidemiology and phylogeny, and potential new antifungals. CGD serves the needs of this diverse research community by curating the entire gene-based Candida experimental literature as it is published, extracting, organizing and standardizing gene annotations. Most recently, we have begun linking clinical data on disease to relevant Literature Topics to improve searchability for clinical researchers. Because CGD curates for multiple species and most research focuses on aspects related to pathogenicity, we focus our curation efforts on assigning Literature Topic tags, collecting detailed mutant phenotype data, and assigning controlled Gene Ontology terms with accompanying evidence codes. Our Summary pages for each feature include the primary name and all aliases for that locus, a description of the gene and/or gene product, detailed ortholog information with links, a JBrowse window with a visual view of the gene on its chromosome, summarized phenotype, Gene Ontology, and sequence information, references cited on the summary page itself, and any locus notes. The database serves as a community hub, where we link to various types of reference material of relevance to Candida researchers, including colleague information, news, and notice of upcoming meetings. We routinely survey the community to learn how the field is evolving and how needs may have changed. A key future challenge is management of the flood of high-throughput expression data to make it as useful as possible to as many researchers as possible. The central challenge for any community database is to turn data into knowledge, which the community can access, use, and build upon.