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      GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists.

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          Abstract

          Since the inception of the GO annotation project, a variety of tools have been developed that support exploring and searching the GO database. In particular, a variety of tools that perform GO enrichment analysis are currently available. Most of these tools require as input a target set of genes and a background set and seek enrichment in the target set compared to the background set. A few tools also exist that support analyzing ranked lists. The latter typically rely on simulations or on union-bound correction for assigning statistical significance to the results.

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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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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              FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes.

              Fatima Al-Shahrour, Ramón Díaz-Uriarte, Joaquin Dopazo (2004)
              We present a simple but powerful procedure to extract Gene Ontology (GO) terms that are significantly over- or under-represented in sets of genes within the context of a genome-scale experiment (DNA microarray, proteomics, etc.). Said procedure has been implemented as a web application, FatiGO, allowing for easy and interactive querying. FatiGO, which takes the multiple-testing nature of statistical contrast into account, currently includes GO associations for diverse organisms (human, mouse, fly, worm and yeast) and the TrEMBL/Swissprot GOAnnotations@EBI correspondences from the European Bioinformatics Institute.
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                Author and article information

                Journal
                Journal ID (iso-abbrev): BMC Bioinformatics
                Title: BMC bioinformatics
                Publisher: Springer Science and Business Media LLC
                ISSN (Electronic): 1471-2105
                ISSN (Print): 1471-2105
                Publication date (Electronic): Feb 03 2009
                Volume: 10
                Affiliations
                [1 ] Molecular Cell Biology Department, Weizmann Institute of Science, Rehovot, Israel. eraneden@gmail.com
                Article
                Publisher Item ID: 1471-2105-10-48
                DOI: 10.1186/1471-2105-10-48
                PMC ID: 2644678
                PubMed ID: 19192299
                SO-VID: a8ace288-9ec9-46ad-bc82-564a20f40932
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