Skip Navigation


Bioinformatics Advance Access originally published online on November 17, 2007
Bioinformatics 2008 24(1):146-148; doi:10.1093/bioinformatics/btm551
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
24/1/146    most recent
btm551v1
Right arrow Comments: Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when Comments are posted
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Schwarz, D. F.
Right arrow Articles by Möller, S.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Schwarz, D. F.
Right arrow Articles by Möller, S.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2007. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

SNPtoGO: characterizing SNPs by enriched GO terms

Daniel F. Schwarz 1,*, Oliver Hädicke 1,4, Jeanette Erdmann 3, Andreas Ziegler 1, Daniel Bayer 2 and Steffen Möller 2,*

1Institut für Medizinische Biometrie und Statistik, 2Institut für Neuro- und Bioinformatik, 3Medizinische Klinik II, Universität zu Lübeck, Ratzeburger Allee 160, 23538 Lübeck and 4Current Address: Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany

*To whom correspondence should be addressed.


  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 tool presented here hence adds external information, i.e. GeneOntology (GO) terms (Gene Ontology Consortium), 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

Associate Editor: John Quackenbush

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

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BioinformaticsHome page
L. Taher and I. Ovcharenko
Variable locus length in the human genome leads to ascertainment bias in functional inference for non-coding elements
Bioinformatics, March 1, 2009; 25(5): 578 - 584.
[Abstract] [Full Text] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.