DAVID Bioinformatics Resources

DAVID Bioinformatics Resources provides functional interpretation of large gene lists from genomic studies by aggregating annotations and analyzing gene-term and pathway relationships to reveal underlying biological mechanisms.

Key Features:

• DAVID Knowledgebase: Integrates diverse public bioinformatics resources under the DAVID Gene Concept using a single-linkage method to aggregate gene/protein identifiers and annotation terms.
• DAVID Gene Functional Classification Tool: Condenses large gene lists into functionally related clusters based on shared annotation profiles.
• DAVID Functional Annotation Tool: Appends descriptive annotations from multiple public databases to user-uploaded gene lists for rapid annotation.
• DAVID Gene ID Conversion Tool: Converts between different gene and protein identifier types to harmonize datasets.
• DAVID Gene Name Viewer: Displays and manages gene names to clarify gene identification.
• DAVID NIAID Pathogen Genome Browser: Visualizes genes within bio-pathways to support exploration of pathogenic genomes.
• Gene-Term Enrichment Analysis: Performs enrichment analysis, clusters redundant and heterogeneous terms into groups, visualizes many-genes-to-many-terms relationships, and searches for related genes or terms.
• GoCharts: Assigns genes to Gene Ontology functional categories based on selected classifications and term specificity levels.
• KeggCharts: Maps genes to KEGG pathway and metabolic maps to view genes in biochemical pathway context.
• DomainCharts: Groups genes according to PFAM conserved protein domains.
• Result Linking: Provides dynamic links between analysis results, primary data, and external repositories for deeper inspection.

Scientific Applications:

• Functional interpretation of gene lists: Elucidates biological mechanisms from genomic-derived gene lists, including microarray datasets.
• Genome-scale data analysis: Supports interpretation of large-scale genomic experiments by aggregating annotations and grouping genes by function.
• Pathway and ontology mapping: Maps genes to Gene Ontology categories, KEGG pathways, and PFAM domains to contextualize gene function.
• Identifier harmonization: Enables cross-dataset analyses by converting and unifying gene/protein identifiers.
• Pathogen genome exploration: Facilitates exploration of pathogenic genomes within pathway contexts via the NIAID Pathogen Genome Browser.

Methodology:

Integrates public databases under the DAVID Gene Concept using a single-linkage aggregation method; appends annotations from multiple databases; converts gene/protein identifiers; clusters redundant terms; visualizes many-to-many gene-term relationships; and assigns genes to Gene Ontology categories, KEGG pathways, and PFAM domains.