GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Summary: Modern experimental techniques, as for example DNA microarrays, as a result usually produce a long list of genes, which are potentially interesting in the analyzed process. In order to gain biological understanding from this type of data, it is necessary to analyze the functional annotations of all genes in this list. The Gene-Ontology (GO) database provides a useful tool to annotate and analyze the functions of a large number of genes. Here, we introduce a tool that utilizes this information to obtain an understanding of which annotations are typical for the analyzed list of genes. This program automatically obtains the GO annotations from a database and generates statistics of which annotations are overrepresented in the analyzed list of genes. This results in a list of GO terms sorted by their specificity. Availability: Our program GOstat is accessible via the Internet at http://gostat.wehi.edu.au

References in zbMATH (referenced in 9 articles )

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  1. Chopra, Pankaj; Shin, Hanjun; Kang, Jaewoo; Lee, Sunwon: SignatureClust: a tool for landmark gene-guided clustering (2012)
  2. Jacob, Laurent; Neuvial, Pierre; Dudoit, Sandrine: More power via graph-structured tests for differential expression of gene networks (2012)
  3. Antoniotti, Marco; Carreras, Marco; Farinaccio, Antonella; Mauri, Giancario; Merico, Daniele; Zoppis, Italo: An application of kernel methods to gene cluster temporal meta-analysis (2010)
  4. Jupiter, Daniel; Şahutoğlu, Jessica; Vanburen, Vincent: TreeHugger: a new test for enrichment of gene ontology terms (2010)
  5. Barry, William T.; Nobel, Andrew B.; Wright, Fred A.: A statistical framework for testing functional categories in microarray data (2008)
  6. Newton, Michael A.; Quintana, Fernando A.; Den Boon, Johan A.; Sengupta, Srikumar; Ahlquist, Paul: Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis (2007)
  7. Grossmann, Steffen; Bauer, Sebastian; Robinson, Peter N.; Vingron, Martin: An improved statistic for detecting over-represented gene ontology annotations in gene sets (2006)
  8. Speer, Nora; Spiet, Christian; Zell, Andreas: Biological cluster validity indices based on the gene ontology (2005)
  9. Rahnenführer, Jörg; Domingues, Francisco S.; Maydt, Jochen; Lengauer, Thomas: Calculating the statistical significance of changes in pathway activity from gene expression data (2004)