DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis

Abstract

Background: Due to the complex and distributed nature of biological research, our current biological knowledge is spread over many redundant annotation databases maintained by many independent groups. Analysts usually need to visit many of these bioinformatics databases in order to integrate comprehensive annotation information for their genes, which becomes one of the bottlenecks, particularly for the analytic task associated with a large gene list. Thus, a highly centralized and ready-to-use gene-annotation knowledgebase is in demand for high throughput gene functional analysis.

Description: The DAVID Knowledgebase is built around the DAVID Gene Concept, a single-linkage method to agglomerate tens of millions of gene/protein identifiers from a variety of public genomic resources into DAVID gene clusters. The grouping of such identifiers improves the cross-reference capability, particularly across NCBI and UniProt systems, enabling more than 40 publicly available functional annotation sources to be comprehensively integrated and centralized by the DAVID gene clusters. The simple, pair-wise, text format files which make up the DAVID Knowledgebase are freely downloadable for various data analysis uses. In addition, a well organized web interface allows users to query different types of heterogeneous annotations in a high-throughput manner.

Conclusion: The DAVID Knowledgebase is designed to facilitate high throughput gene functional analysis. For a given gene list, it not only provides the quick accessibility to a wide range of heterogeneous annotation data in a centralized location, but also enriches the level of biological information for an individual gene. Moreover, the entire DAVID Knowledgebase is freely downloadable or searchable at http://david.abcc.ncifcrf.gov/knowledgebase/.

Figure 1

The cross-reference among different types of gene identifiers is improved by the DAVID Gene Concept. A. As global examples, four popular types of protein identifiers (PIR ID, UniProt Accession, RefSeq Protein, and GenPept Accession) are only cross-referenced partially by NCBI Entrez Gene (EG), UniProt UniRef100 (UP), and PIR NRef100 (NF). B. The DAVID Gene Concept, a single-linkage algorithm, iteratively agglomerates all types of gene IDs, belonging to the same gene entry, for example, ubiquitin specific peptidase 8 (USP8), into one DAVID gene cluster (ID 2859041). Such integration makes the cross-reference of different types of gene IDs more comprehensive than that in each of the original databases, particularly for the IDs across NCBI and UniProt systems.

Figure 2

The gene-centric DAVID Knowledgebase in a simple pair-wise text format centralized by DAVID gene identifiers. A. A hypothetical graph shows that a wide-range of annotation categories are collected and integrated by the DAVID gene cluster IDs. B. A real example demonstrates how the pair-wise text data structures are used in the DAVID Knowledgebase. Each independent annotation source or gene identifier system is separated into independent files in the same pair-wise format of "DID-to-annotation". In this example, a user starts with an Affymetrix identifier (affy_id) 207849_at (IL2). The first step is to obtain the corresponding DAVID gene identifier (DID:2864938). Then, with this DID (red), the annotation terms of interest (underlined) in different source files (OMIM, SMART, Pfam, GO Molecular Function, KEGG Pathway, BioCart Pathway, etc.) can be queried sequentially.

Figure 3

The improved annotation coverage in the DAVID Knowledgebase. For all genes in the entire human genome, the coverage of Gene Ontology annotations, one of the most highly used annotation resources, is 10–20% greater in the DAVID Knowledgebase than that in other individual source (e.g. Entrez Gene, PIR, Panther and UniProt).

Figure 4

The web-based interface of the DAVID Knowledgebase to query annotation for given gene list. Firstly, users can submit various types of gene identifiers through the 'Gene List Manager' panel on the left side. Then the associated annotation categories may be selected and queried through the right panel accordingly.

Figure 5

About 10–20% increase of gene-GO annotation assignments for the ~400 Affymetrix IDs (additional file 4) derived from a HIV microarray study [26].