DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists

Abstract

All tools in the DAVID Bioinformatics Resources aim to provide functional interpretation of large lists of genes derived from genomic studies. The newly updated DAVID Bioinformatics Resources consists of the DAVID Knowledgebase and five integrated, web-based functional annotation tool suites: the DAVID Gene Functional Classification Tool, the DAVID Functional Annotation Tool, the DAVID Gene ID Conversion Tool, the DAVID Gene Name Viewer and the DAVID NIAID Pathogen Genome Browser. The expanded DAVID Knowledgebase now integrates almost all major and well-known public bioinformatics resources centralized by the DAVID Gene Concept, a single-linkage method to agglomerate tens of millions of diverse gene/protein identifiers and annotation terms from a variety of public bioinformatics databases. For any uploaded gene list, the DAVID Resources now provides not only the typical gene-term enrichment analysis, but also new tools and functions that allow users to condense large gene lists into gene functional groups, convert between gene/protein identifiers, visualize many-genes-to-many-terms relationships, cluster redundant and heterogeneous terms into groups, search for interesting and related genes or terms, dynamically view genes from their lists on bio-pathways and more. With DAVID (http://david.niaid.nih.gov), investigators gain more power to interpret the biological mechanisms associated with large gene lists.

Figure 1.

A DAVID gene constructed by a single linkage algorithm. Two UniRef100 clusters, two NRef 100 clusters and one Entrez Gene cluster were systematically found sharing one or more protein identifiers with each other. The single-linkage rule can further iteratively agglomerate them as a whole into one DAVID gene. Thus, for this particular example of tyrosine-protein phosphatase non-receptor type 21 (PTPN21), the resulting DAVID gene is able to collect and integrate all gene/protein identifiers more comprehensively than each original gene cluster.

Figure 2.

An HTML report from the Functional Annotation Clustering. The annotation cluster 1 in the example shows that GO term cytokine activity, KEGG pathway cytokine–cytokine receptor interaction, and GO term receptor binding, etc. are grouped together. Thus, the different biological aspects regarding a relevant biology can be explored at the same time.

Figure 3.

A roadmap to choose appropriate DAVID functions and tools.