Skip Navigation


Bioinformatics Advance Access originally published online on April 23, 2009
Bioinformatics 2009 25(16):2126-2133; doi:10.1093/bioinformatics/btp278
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrowOA All Versions of this Article:
25/16/2126    most recent
btp278v1
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 PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Google Scholar
Right arrow Articles by Schultheiss, S. J.
Right arrow Articles by Rätsch, G.
PubMed
Right arrow PubMed Citation
Right arrow Articles by Schultheiss, S. J.
Right arrow Articles by Rätsch, G.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2009 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.

KIRMES: kernel-based identification of regulatory modules in euchromatic sequences

Sebastian J. Schultheiss 1,2,*, Wolfgang Busch 2,{dagger}, Jan U. Lohmann 2,3, Oliver Kohlbacher 4 and Gunnar Rätsch 1

1Friedrich Miescher Laboratory of the Max Planck Society, and 2Max Planck Institute for Developmental Biology, Tübingen, 3Department of Stem Cell Biology, University of Heidelberg and 4Wilhelm Schickard Institute for Computer Science, University of Tübingen, Germany

*To whom correspondence should be addressed.


  Abstract

Motivation: Understanding transcriptional regulation is one of the main challenges in computational biology. An important problem is the identification of transcription factor (TF) binding sites in promoter regions of potential TF target genes. It is typically approached by position weight matrix-based motif identification algorithms using Gibbs sampling, or heuristics to extend seed oligos. Such algorithms succeed in identifying single, relatively well-conserved binding sites, but tend to fail when it comes to the identification of combinations of several degenerate binding sites, as those often found in cis-regulatory modules.

Results: We propose a new algorithm that combines the benefits of existing motif finding with the ones of support vector machines (SVMs) to find degenerate motifs in order to improve the modeling of regulatory modules. In experiments on microarray data from Arabidopsis thaliana, we were able to show that the newly developed strategy significantly improves the recognition of TF targets.

Availability: The python source code (open source-licensed under GPL), the data for the experiments and a Galaxy-based web service are available at http://www.fml.mpg.de/raetsch/suppl/kirmes/

Contact: sebi{at}tuebingen.mpg.de

Supplementary information: Supplementary data are available at Bioinformatics online.

{dagger}Present Address: Biology Department Duke University Box 90338, Durham, NC 27708, USA

Associate Editor: Trey Ideker

Received on October 31, 2008; revised on March 16, 2009; accepted on April 21, 2009

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




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.