Nucleic Acids Research Advance Access originally published online on June 10, 2008
Nucleic Acids Research 2008 36(Web Server issue):W352-W357; doi:10.1093/nar/gkn323
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Nucleic Acids Research, 2008, Vol. 36, No. suppl_2 W352-W357
© 2008 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Articles |
Onto-CC: a web server for identifying Gene Ontology conceptual clusters
1Departamento de Ciencias de la Computación e Inteligencia Artificial, Escuela Técnica Superior de Ingenierías Informática y de Telecomunicación, c/. Daniel Saucedo Aranda, s/n 18071 Granada, Spain, 2Cellular Injury and Adaptation Laboratory, Washington University School of Medicine and 3Department of Molecular Microbiology, Howard Hughes Medical Institute, Washington University School of Medicine, St. Louis, Missouri, USA
*To whom correspondence should be addressed. Tel: +34 958240469; Fax: +34 958243317; Email: zwir{at}borcim.wustl.edu
Received February 1, 2008. Revised April 22, 2008. Accepted May 7, 2008.
The Gene Ontology (GO) vocabulary has been extensively explored to analyze the functions of coexpressed genes. However, despite its extended use in Biology and Medical Sciences, there are still high levels of uncertainty about which ontology (i.e. Molecular Process, Cellular Component or Molecular Function) should be used, and at which level of specificity. Moreover, the GO database can contain incomplete information resulting from human annotations, or highly influenced by the available knowledge about a specific branch in an ontology. In spite of these drawbacks, there is a trend to ignore these problems and even use GO terms to conduct searches of gene expression profiles (i.e. expression + GO) instead of more cautious approaches that just consider them as an independent source of validation (i.e. expression versus GO). Consequently, propagating the uncertainty and producing biased analysis of the required gene grouping hypotheses. We proposed a web tool, Onto-CC, as an automatic method specially suited for independent explanation/validation of gene grouping hypotheses (e.g. coexpressed genes) based on GO clusters (i.e. expression versus GO). Onto-CC approach reduces the uncertainty of the queries by identifying optimal conceptual clusters that combine terms from different ontologies simultaneously, as well as terms defined at different levels of specificity in the GO hierarchy. To do so, we implemented the EMO-CC methodology to find clusters in structural databases [GO Directed acyclic Graph (DAG) tree], inspired on Conceptual Clustering algorithms. This approach allows the management of optimal cluster sets as potential parallel hypotheses, guided by multiobjective/multimodal optimization techniques. Therefore, we can generate alternative and, still, optimal explanations of queries that can provide new insights for a given problem. Onto-CC has been successfully used to test different medical and biological hypotheses including the explanation and prediction of gene expression profiles resulting from the host response to injuries in the inflammatory problem. Onto-CC provides two versions: Ready2GO, a precalculated EMO-CC for several genomes and an Advanced Onto-CC for custom annotation files (http://gps-tools2.wustl.edu/onto-cc/index.html).