Database Commons

a catalog of biological databases

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Database Profile

General information

Full name: The Database for Annotation, Visualization and Integrated Discovery
Description: The Database for Annotation, Visualization and Integrated Discovery (DAVID ) v6.8 comprises a full Knowledgebase update to the sixth version of our original web-accessible programs. DAVID now provides a comprehensive set of functional annotation tools for investigators to understand biological meaning behind large list of genes.
Year founded: 2003
Last update: 2020-11-13
Version: version6.8
Real time : Checking...
Country/Region: United States
Data type:
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Contact information

University/Institution: National Cancer Institute
Address: P.O. Box B or Bldg. 310 Laboratory of Human Retrovirology and Immunoinformatics Applied/Developmental Research Directorate Frederick National Laboratory for Cancer Research Frederick, MD 21702
Country/Region: United States
Contact name (PI/Team): DAVID Bioinformatic Team
Contact email (PI/Helpdesk):


DAVID-WS: a stateful web service to facilitate gene/protein list analysis. [PMID: 22543366]
Xiaoli Jiao, Brad T Sherman, Da Wei Huang, Robert Stephens, Michael W Baseler, H Clifford Lane, Richard A Lempicki,

SUMMARY: The database for annotation, visualization and integrated discovery (DAVID), which can be freely accessed at, is a web-based online bioinformatics resource that aims to provide tools for the functional interpretation of large lists of genes/proteins. It has been used by researchers from more than 5000 institutes worldwide, with a daily submission rate of ∼1200 gene lists from ∼400 unique researchers, and has been cited by more than 6000 scientific publications. However, the current web interface does not support programmatic access to DAVID, and the uniform resource locator (URL)-based application programming interface (API) has a limit on URL size and is stateless in nature as it uses URL request and response messages to communicate with the server, without keeping any state-related details. DAVID-WS (web service) has been developed to automate user tasks by providing stateful web services to access DAVID programmatically without the need for human interactions.
AVAILABILITY: The web service and sample clients (written in Java, Perl, Python and Matlab) are made freely available under the DAVID License at

Bioinformatics. 2012:28(13) | 344 Citations (from Europe PMC, 2021-09-18)
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. [PMID: 19131956]
Huang da W, Sherman BT, Lempicki RA.

DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.

Nat Protoc. 2009:4(1) | 17285 Citations (from Europe PMC, 2021-09-18)
Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. [PMID: 19033363]
Huang da W, Sherman BT, Lempicki RA.

Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.

Nucleic Acids Res. 2009:37(1) | 7083 Citations (from Europe PMC, 2021-09-18)
Extracting biological meaning from large gene lists with DAVID. [PMID: 19728287]
Huang da W, Sherman BT, Zheng X, Yang J, Imamichi T, Stephens R, Lempicki RA.

High-throughput genomics screening studies, such as microarray, proteomics, etc., often result in large, "interesting" gene lists, ranging in size from hundreds to thousands of genes. Given the challenges of functionally interpreting such large gene lists, it is necessary to incorporate bioinformatics tools in the analysis. DAVID is a Web-based application that provides a high-throughput and integrative gene functional annotation environment to systematically extract biological themes behind large gene lists. High-throughput gene functional analysis with DAVID will provide important insights that allow investigators to understand the biological themes within their given genomic study. This unit will describe step-by-step procedures to use DAVID tools, as well as a brief rationale and key parameters in the DAVID analysis.

Curr Protoc Bioinformatics. 2009:Chapter 13() | 148 Citations (from Europe PMC, 2021-09-18)
DAVID gene ID conversion tool. [PMID: 18841237]
Da Wei Huang, Brad T Sherman, Robert Stephens, Michael W Baseler, H Clifford Lane, Richard A Lempicki,

Our current biological knowledge is spread over many independent bioinformatics databases where many different types of gene and protein identifiers are used. The heterogeneous and redundant nature of these identifiers limits data analysis across different bioinformatics resources. It is an even more serious bottleneck of data analysis for larger datasets, such as gene lists derived from microarray and proteomic experiments. The DAVID Gene ID Conversion Tool (DICT), a web-based application, is able to convert user's input gene or gene product identifiers from one type to another in a more comprehensive and high-throughput manner with a uniquely enhanced ID-ID mapping database.

Bioinformation. 2008:2(10) | 91 Citations (from Europe PMC, 2021-09-18)
DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. [PMID: 17980028]
Sherman BT, Huang da W, Tan Q, Guo Y, Bour S, Liu D, Stephens R, Baseler MW, Lane HC, Lempicki RA.

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

BMC Bioinformatics. 2007:8() | 276 Citations (from Europe PMC, 2021-09-18)
DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. [PMID: 17576678]
Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Lane HC, Lempicki RA.

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 (, investigators gain more power to interpret the biological mechanisms associated with large gene lists.

Nucleic Acids Res. 2007:35(Web Server issue) | 967 Citations (from Europe PMC, 2021-09-18)
The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. [PMID: 17784955]
Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA.

The DAVID Gene Functional Classification Tool uses a novel agglomeration algorithm to condense a list of genes or associated biological terms into organized classes of related genes or biology, called biological modules. This organization is accomplished by mining the complex biological co-occurrences found in multiple sources of functional annotation. It is a powerful method to group functionally related genes and terms into a manageable number of biological modules for efficient interpretation of gene lists in a network context.

Genome Biol. 2007:8(9) | 1056 Citations (from Europe PMC, 2021-09-18)
DAVID: Database for Annotation, Visualization, and Integrated Discovery. [PMID: 12734009]
Glynn Dennis, Brad T Sherman, Douglas A Hosack, Jun Yang, Wei Gao, H Clifford Lane, Richard A Lempicki,

BACKGROUND: 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.
RESULTS: Database for Annotation, Visualization, and Integrated Discovery (DAVID; 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.
CONCLUSIONS: 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.

Genome Biol.. 2003:4(5) | 4711 Citations (from Europe PMC, 2021-09-18)
Identifying biological themes within lists of genes with EASE. [PMID: 14519205]
Hosack DA, Dennis G, Sherman BT, Lane HC, Lempicki RA.

EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from 'genes' to 'themes'.

Genome Biol. 2003:4(10) | 1233 Citations (from Europe PMC, 2021-09-18)


All databases:
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1/353 (100%)
Standard ontology and nomenclature:
1/206 (100%)
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Record metadata

Created on: 2018-01-26
Curated by:
lin liu [2021-03-29]
lin liu [2021-03-28]
lin liu [2021-01-21]
Lina Ma [2019-12-25]
Dong Zou [2019-12-02]
Rabail Raza [2018-12-27]
Mengyu Pan [2018-09-20]
Tongkun Guo [2018-02-24]