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      The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists

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          Abstract

          The DAVID gene functional classification tool uses a novel fuzzy clustering algorithm to condense a list of genes or associated biological terms into organized classes of related genes or biology, called biological modules.

          Abstract

          The DAVID Gene Functional Classification Tool http://david.abcc.ncifcrf.gov 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.

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          DAVID: Database for Annotation, Visualization, and Integrated Discovery.

          Glynn Dennis, Brad T. Sherman, Douglas A Hosack … (2003)
          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. Database for Annotation, Visualization, and Integrated Discovery (DAVID; http://www.david.niaid.nih.gov) 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. 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.
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            Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization.

            Srinivasan Iyer, Michael Eisen, A Futcher … (1998)
            We sought to create a comprehensive catalog of yeast genes whose transcript levels vary periodically within the cell cycle. To this end, we used DNA microarrays and samples from yeast cultures synchronized by three independent methods: alpha factor arrest, elutriation, and arrest of a cdc15 temperature-sensitive mutant. Using periodicity and correlation algorithms, we identified 800 genes that meet an objective minimum criterion for cell cycle regulation. In separate experiments, designed to examine the effects of inducing either the G1 cyclin Cln3p or the B-type cyclin Clb2p, we found that the mRNA levels of more than half of these 800 genes respond to one or both of these cyclins. Furthermore, we analyzed our set of cell cycle-regulated genes for known and new promoter elements and show that several known elements (or variations thereof) contain information predictive of cell cycle regulation. A full description and complete data sets are available at http://cellcycle-www.stanford.edu
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              BIND: the Biomolecular Interaction Network Database.

              Gary Bader, Doron Betel, Christopher W. V. Hogue (2003)
              The Biomolecular Interaction Network Database (BIND: http://bind.ca) archives biomolecular interaction, complex and pathway information. A web-based system is available to query, view and submit records. BIND continues to grow with the addition of individual submissions as well as interaction data from the PDB and a number of large-scale interaction and complex mapping experiments using yeast two hybrid, mass spectrometry, genetic interactions and phage display. We have developed a new graphical analysis tool that provides users with a view of the domain composition of proteins in interaction and complex records to help relate functional domains to protein interactions. An interaction network clustering tool has also been developed to help focus on regions of interest. Continued input from users has helped further mature the BIND data specification, which now includes the ability to store detailed information about genetic interactions. The BIND data specification is available as ASN.1 and XML DTD.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2007
                Publication date (Electronic): 4 September 2007
                Volume: 8
                Issue: 9
                Page: R183
                Affiliations
                [1 ]Laboratory of Immunopathogenesis and Bioinformatics, Clinical Services Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, MD 21702, USA
                [2 ]Advanced Biomedical Computing Center, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, MD 21702, USA
                [3 ]Computer and Statistical Services, Data Management Services, National Cancer Institute at Frederick, Frederick, MD 21702, USA
                [4 ]Clinical Services Program, SAIC-Frederick, Inc., National Cancer Institute at Frederick, Frederick, MD 21702, USA
                [5 ]Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA
                Article
                Publisher ID: gb-2007-8-9-r183
                DOI: 10.1186/gb-2007-8-9-r183
                PMC ID: 2375021
                PubMed ID: 17784955
                SO-VID: a8ec2a7d-0828-4a86-a55d-8c804a0d08e1
                Copyright © Copyright © 2007 Huang et al.; licensee BioMed Central Ltd.
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 5 February 2007
                Date revision received : 20 April 2007
                Date accepted : 4 September 2007
                Categories
                Subject: Software

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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