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DAVID: Database for Annotation, Visualization, and Integrated Discovery
1 Science Applications International Corporation-Frederick, Clinical Services Program, Laboratory of Immunopathogenesis and Bioinformatics, National Cancer Institute at Frederick, MD 21702, USA
2 Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 20892, USA
Genome Biology 2003, 4:P3 doi:10.1186/gb-2003-4-5-p3
This is the first version of this article to be made available publicly. A peer-reviewed and modified version is now available in full at http://genomebiology.com/2003/4/9/R60Published: 3 April 2003
Functional annotation of differentially expressed genes is a necessary and critical step in the analysis of microarray data. The distributed nature of biological knowledge frequently requires researchers to navigate through numerous web-accessible databases gathering information one gene at a time. A more judicious approach is to provide query-based access to an integrated database that disseminates biologically rich information across large datasets and displays graphic summaries of functional information.
Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov webcite) addresses this need via four web-based analysis modules: 1) Annotation Tool - rapidly appends descriptive data from several public databases to lists of genes; 2) GoCharts - assigns genes to Gene Ontology functional categories based on user selected classifications and term specificity level; 3) KeggCharts - assigns genes to KEGG metabolic processes and enables users to view genes in the context of biochemical pathway maps; and 4) DomainCharts - groups genes according to PFAM conserved protein domains.
Analysis results and graphical displays remain dynamically linked to primary data and external data repositories, thereby furnishing in-depth as well as broad-based data coverage. The functionality provided by DAVID accelerates the analysis of genome-scale datasets by facilitating the transition from data collection to biological meaning.