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 20 articles )

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  1. Wolff, Alexander: Analysis of expression profile and gene variation via development of methods for next generation sequencing data (2018)
  2. Keith, Jonathan M. (ed.): Bioinformatics. Volume I. Data, sequence analysis, and evolution (2017)
  3. Chopra, Pankaj; Shin, Hanjun; Kang, Jaewoo; Lee, Sunwon: SignatureClust: a tool for landmark gene-guided clustering (2012) ioport
  4. Drăghici, Sorin: Statistics and data analysis for microarrays using R and Bioconductor. With CD-ROM. (2012)
  5. Jacob, Laurent; Neuvial, Pierre; Dudoit, Sandrine: More power via graph-structured tests for differential expression of gene networks (2012)
  6. Faure, Andre J.; Seoighe, Cathal; Mulder, Nicola J.: Investigating the effect of paralogs on microarray gene-set analysis (2011) ioport
  7. Antoniotti, Marco; Carreras, Marco; Farinaccio, Antonella; Mauri, Giancario; Merico, Daniele; Zoppis, Italo: An application of kernel methods to gene cluster temporal meta-analysis (2010)
  8. Bruckskotten, Marc; Looso, Mario; Cemic, Franz; Konzer, Anne; Hemberger, Jurgen; Kruger, Markus; Braun, Thomas: PCA2GO: a new multivariate statistics based method to identify highly expressed GO-terms (2010) ioport
  9. Jupiter, Daniel; Şahutoğlu, Jessica; Vanburen, Vincent: TreeHugger: a new test for enrichment of gene ontology terms (2010)
  10. Tchagang, Alain B.; Gawronski, Alexander; Bérubé, Hugo; Phan, Sieu; Famili, Fazel; Pan, Youlian: GOAL: A software tool for assessing biological significance of genes groups (2010) ioport
  11. Den Berg, Bart H. J. Van; Thanthiriwatte, Chamali; Manda, Prashanti; Bridges, Susan M.: Comparing gene annotation enrichment tools for functional modeling of agricultural microarray data (2009) ioport
  12. Eden, Eran; Navon, Roy; Steinfeld, Israel; Lipson, Doron; Yakhini, Zohar: \textitgorilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists (2009) ioport
  13. Milanesi, Luciano; 0001, Paolo Romano; Castellani, Gastone C.; Remondini, Daniel; Liò, Pietro: Trends in modeling biomedical complex systems (2009) ioport
  14. Barry, William T.; Nobel, Andrew B.; Wright, Fred A.: A statistical framework for testing functional categories in microarray data (2008)
  15. Dudoit, Sandrine; Keleş, Sündüz; van der Laan, Mark J.: Multiple tests of association with biological annotation metadata (2008)
  16. Dudoit, Sandrine; van der Laan, Mark J.: Multiple testing procedures with applications to genomics. (2008)
  17. 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)
  18. Grossmann, Steffen; Bauer, Sebastian; Robinson, Peter N.; Vingron, Martin: An improved statistic for detecting over-represented gene ontology annotations in gene sets (2006)
  19. Speer, Nora; Spiet, Christian; Zell, Andreas: Biological cluster validity indices based on the gene ontology (2005)
  20. Rahnenführer, Jörg; Domingues, Francisco S.; Maydt, Jochen; Lengauer, Thomas: Calculating the statistical significance of changes in pathway activity from gene expression data (2004)