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Bioinformatics Advance Access originally published online on October 10, 2006
Bioinformatics 2006 22(23):2858-2864; doi:10.1093/bioinformatics/btl499
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Identifying cis-regulatory modules by combining comparative and compositional analysis of DNA

Nora Pierstorff 1,2,*, Casey M. Bergman 2 and Thomas Wiehe 1

1 Institute for Genetics, University of Cologne Zuelpicher Strasse 47, 50674 Cologne, Germany
2 Faculty of Life Sciences, University of Manchester Michael Smith Building, Oxford Road, M13 9PT Manchester, UK

*To whom correspondence should be addressed.

Motivation: Predicting cis-regulatory modules (CRMs) in higher eukaryotes is a challenging computational task. Commonly used methods to predict CRMs based on the signal of transcription factor binding sites (TFBS) are limited by prior information about transcription factor specificity. More general methods that bypass the reliance on TFBS models are needed for comprehensive CRM prediction.

Results: We have developed a method to predict CRMs called CisPlusFinder that identifies high density regions of perfect local ungapped sequences (PLUSs) based on multiple species conservation. By assuming that PLUSs contain core TFBS motifs that are locally overrepresented, the method attempts to capture the expected features of CRM structure and evolution. Applied to a benchmark dataset of CRMs involved in early Drosophila development, CisPlusFinder predicts more annotated CRMs than all other methods tested. Using the REDfly database, we find that some ‘false positive’ predictions in the benchmark dataset correspond to recently annotated CRMs. Our work demonstrates that CRM prediction methods that combine comparative genomic data with statistical properties of DNA may achieve reasonable performance when applied genome-wide in the absence of an a priori set of known TFBS motifs.

Availability: The program CisPlusFinder can be downloaded at http://jakob.genetik.uni-koeln.de/bioinformatik/people/nora/nora.html. All software is licensed under the Lesser GNU Public License (LGPL).

Contact: nora.pierstorff{at}uni-koeln.de.

Supplementary information: Supplementary data are available at Bioinformatics online.


Received on June 2, 2006; revised on September 8, 2006; accepted on September 28, 2006

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