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Bioinformatics Advance Access published online on November 17, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm551
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

SNPtoGO: Characterising SNPs by Enriched GO Terms

Daniel F. Schwarz a,*, Oliver Hädicke a,d, Jeanette Erdmann c, Andreas Ziegler a, Daniel Bayer b and Steffen Möller b

aInstitut für Medizinische Biometrie und Statistik, bInstitut für Neuro- und Bioinformatik, and cMedizinische Klinik II, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck, Germany; dMax Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, 39106 Magdeburg, Germany;

*To whom correspondence should be addressed. Mr. Daniel F.Schwarz, E-mail: schwarz{at}imbs.uni-luebeck.de


  Abstract

For the analysis of complex polygenic diseases, one does not expect all patients to share the same disease-associated alleles. Not even will disease-causing variations be assigned to the identical sets of genes between patients. However, one does expect overlaps in the sets of genes that are involved and even more so in their assigned molecular processes. Furthermore, the assignment of single nucleotide polymorphisms (SNPs) to genes is highly ambiguous for intergenic SNPs. The here presented tool hence adds external information, i.e, GeneOntology (GO) terms (Gene Ontology Consortium, 2006), to the analysis of SNP data.

Availability: A web interface and source code are offered at https://webtools.imbs.uni-luebeck.de/snptogo

Contact: schwarz{at}imbs.uni-luebeck.de, moeller{at}inb.uni-luebeck.de

Associate Editor: Prof. John Quackenbush

Received on May 31, 2007; revised on October 12, 2007; accepted on October 31, 2007

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