Nucleic Acids Research Advance Access originally published online on December 10, 2008
Nucleic Acids Research 2009 37(2):622-628; doi:10.1093/nar/gkn982
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Nucleic Acids Research, 2009, Vol. 37, No. 2 622-628
© 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.
Computational Biology |
Identification of candidate disease genes by integrating Gene Ontologies and protein-interaction networks: case study of primary immunodeficiencies
1Institute of Medical Technology, FI-33014 University of Tampere and 2Tampere University Hospital, FI-33520 Tampere, Finland
*To whom correspondence should be addressed: Tel: +358 3 3551 7735; Fax: +358 3 3551 7710; Email: mauno.vihinen{at}uta.fi
Received September 25, 2008. Revised November 19, 2008. Accepted November 20, 2008.
Disease gene identification is still a challenge despite modern high-throughput methods. Many diseases are very rare or lethal and thus cannot be investigated with traditional methods. Several in silico methods have been developed but they have some limitations. We introduce a new method that combines information about protein-interaction network properties and Gene Ontology terms. Genes with high-calculated network scores and statistically significant gene ontology terms based on known diseases are prioritized as candidate genes. The method was applied to identify novel primary immunodeficiency-related genes, 26 of which were found. The investigation uses the protein-interaction network for all essential immunome human genes available in the Immunome Knowledge Base and an analysis of their enriched gene ontology annotations. The identified disease gene candidates are mainly involved in cellular signaling including receptors, protein kinases and adaptor and binding proteins as well as enzymes. The method can be generalized for any disease group with sufficient information.
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