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Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources

Nature Protocols volume 4, pages 44–57 (2009)Cite this article

Abstract

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.

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Figure 1: An example layout of DAVID gene name batch viewer.
Figure 2: An example layout of DAVID gene functional classification.
Figure 3: An example layout of DAVID annotation chart.
Figure 4: An example layout of DAVID functional annotation clustering.
Figure 5: An example layout of DAVID Annotation Table.
Figure 6: Analytic tools/modules in DAVID.
Figure 7: Submit a gene list to DAVID and access various analytic tools/modules.
Figure 8: Pathway map viewer.

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Acknowledgements

We are grateful to the referees for their constructive comments and thank Robert Stephens, David Bryant and David Liu in the ABCC group for Web server support. Thanks also go to Xin Zheng and Jun Yang in the Laboratory of Immunopathogenesis and Bioinformatics (LIB) group for discussion. We also thank Bill Wilton and Mike Tartakovsky for information technology and network support. The project has been funded with federal funds from the National Institute of Allergy and Infectious Diseases (NIAID) and National Institutes of Health (NIH), under Contract no. NO1-CO-56000. The annotation of this tool and publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products or organizations imply endorsement by the United States Government.

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Author notes

  1. Da Wei Huang and Brad T Sherman: These authors contributed equally to this work.

Authors and Affiliations

  1. Laboratory of Immunopathogenesis and Bioinformatics, Clinical Services Program, SAIC-Frederick Inc., National Cancer Institute at Frederick, Frederick, 21702, Maryland, USA

    Da Wei Huang, Brad T Sherman & Richard A Lempicki

Authors
  1. Da Wei Huang
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  2. Brad T Sherman
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  3. Richard A Lempicki
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Corresponding authors

Correspondence to Da Wei Huang or Richard A Lempicki.

Supplementary information

Supplementary Data 1.

Collection of ∼68 similar enrichment analysis tools. The tools are roughly categorized into three classes according to their backend algorithms. Reference links are provided for more information about each tool. (XLS 101 kb)

Supplementary Data 2.

∼400 Affymetrix IDs16 used in this paper (XLS 71 kb)

Supplementary Data 3.

Comparisons of the enrichment p-values between gene lists derived from microarray study vs. same size gene lists generated randomly. A 'good' gene lists should consistently contain more enriched biology than that of random list of the same sizes. (PDF 338 kb)

Supplementary Data 4.

Summaries of gene identifier types and annotation categories supported in the DAVID system. (XLS 38 kb)

Supplementary Data 5.

Screen shots of each major analysis step according to the description in the manuscript. (PDF 1600 kb)

Supplementary Data 6.

Examples for the input formats of a gene list. (PDF 69 kb)

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Huang, D., Sherman, B. & Lempicki, R. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4, 44–57 (2009). https://doi.org/10.1038/nprot.2008.211

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