FatiGO

FatiGO: a web tool for finding significant associations of Gene ontology terms with groups of genes. Summary: 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. Availability: http://fatigo.bioinfo.cnio.es

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References in zbMATH (referenced in 21 articles )

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  1. Zimek, Arthur; Vreeken, Jilles: The blind men and the elephant: on meeting the problem of multiple truths in data from clustering and pattern mining perspectives (2015)
  2. Drăghici, Sorin: Statistics and data analysis for microarrays using R and Bioconductor. With CD-ROM. (2012)
  3. Nueda, Maria J.; Ferrer, Alberto; Conesa, Ana: ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments (2012)
  4. Rodríguez-Ezpeleta, Naiara (ed.); Hackenberg, Michael (ed.); Aransay, Ana M. (ed.): Bioinformatics for high throughput sequencing (2012)
  5. Faure, Andre J.; Seoighe, Cathal; Mulder, Nicola J.: Investigating the effect of paralogs on microarray gene-set analysis (2011) ioport
  6. Maulik, Ujjwal; Bandyopadhyay, Sanghamitra; Mukhopadhyay, Anirban: Multiobjective genetic algorithms for clustering. Applications in data mining and bioinformatics. (2011)
  7. Jupiter, Daniel; Şahutoğlu, Jessica; Vanburen, Vincent: TreeHugger: a new test for enrichment of gene ontology terms (2010)
  8. 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
  9. Dudoit, Sandrine; Keleş, Sündüz; van der Laan, Mark J.: Multiple tests of association with biological annotation metadata (2008)
  10. Dudoit, Sandrine; van der Laan, Mark J.: Multiple testing procedures with applications to genomics. (2008)
  11. Guy Brock; Vasyl Pihur; Susmita Datta; Somnath Datta: clValid: An R Package for Cluster Validation (2008) not zbMATH
  12. Gaj, Stan; Van Erk, Arie; Van Haaften, Rachel Im; Evelo, Chris Ta: Linking microarray reporters with protein functions (2007) ioport
  13. Marco, Antonio; Marin, Ignacio: A general strategy to determine the congruence between a hierarchical and a non-hierarchical classification (2007) ioport
  14. 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)
  15. Sánchez, Alex; Salicrú, Miquel; Ocaña, Jordi: Statistical methods for the analysis of high-throughput data based on functional profiles derived from the Gene Ontology (2007)
  16. Thorne, Thomas; Stumpf, Michael P. H.: Generating confidence intervals on biological networks (2007) ioport
  17. Chen, Chin-Fu; Feng, Xin; Szeto, Jack: Identification of critical genes in microarray experiments by a neuro-fuzzy approach (2006)
  18. Lewin, Alex; Richardson, Sylvia; Marshall, Clare; Glazier, Anne; Aitman, Tim: Bayesian modeling of differential gene expression (2006)
  19. Pan, Wei: Incorporating biological information as a prior in an empirical Bayes approach to analyzing microarray data (2005)
  20. Lambrix, Patrick: Ontologies in bioinformatics and systems biology (2004)

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