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Nucleic Acids Research Advance Access published online on July 3, 2009
Nucleic Acids Research, doi:10.1093/nar/gkp554
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© 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.

Computational Biology

Discovery of protein–DNA interactions by penalized multivariate regression

Leonid Zamdborg1,2 and Ping Ma2,3,*

1Center for Biophysics and Computational Biology, 2Institute for Genomic Biology and 3Department of Statistics, University of Illinois at Urbana-Champaign, IL, USA

*To whom correspondence should be addressed. Tel: +1 217 244 7095; Fax: +1 217 244 7190; Email: pingma{at}illinois.edu

Received May 15, 2009. Revised June 11, 2009. Accepted June 14, 2009.

Discovering which regulatory proteins, especially transcription factors (TFs), are active under certain experimental conditions and identifying the corresponding binding motifs is essential for understanding the regulatory circuits that control cellular programs. The experimental methods used for this purpose are laborious. Computational methods have been proven extremely effective in identifying TF-binding motifs (TFBMs). In this article, we propose a novel computational method called MotifExpress for discovering active TFBMs. Unlike existing methods, which either use only DNA sequence information or integrate sequence information with a single-sample measurement of gene expression, MotifExpress integrates DNA sequence information with gene expression measured in multiple samples. By selecting TFBMs that are significantly associated with gene expression, we can identify active TFBMs under specific experimental conditions and thus provide clues for the construction of regulatory networks. Compared with existing methods, MotifExpress substantially reduces the number of spurious results. Statistically, MotifExpress uses a penalized multivariate regression approach with a composite absolute penalty, which is highly stable and can effectively find the globally optimal set of active motifs. We demonstrate the excellent performance of MotifExpress by applying it to synthetic data and real examples of Saccharomyces cerevisiae. MotifExpress is available at http://www.stat.illinois.edu/~pingma/MotifExpress.htm.


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