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Nucleic Acids Research 2005 33(2):605-615; doi:10.1093/nar/gki166
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Published online 31 January 2005

© The Author 2005. Published by Oxford University Press. All rights reserved.
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions{at}oupjournals.org.

Article

Extraction of transcription regulatory signals from genome-wide DNA–protein interaction data

Yael Garten, Shai Kaplan and Yitzhak Pilpel*

Department of Molecular Genetics, Weizmann Institute of Science Rehovot 76100, Israel

*To whom correspondence should be addressed. Tel: +972 8 9346058; Fax: +972 8 9344108; Email: pilpel{at}weizmann.ac.il

Received August 5, 2004. Revised November 26, 2004. Accepted December 15, 2004.

Deciphering gene regulatory network architecture amounts to the identification of the regulators, conditions in which they act, genes they regulate, cis-acting motifs they bind, expression profiles they dictate and more complex relationships between alternative regulatory partnerships and alternative regulatory motifs that give rise to sub-modalities of expression profiles. The ‘location data’ in yeast is a comprehensive resource that provides transcription factor–DNA interaction information in vivo. Here, we provide two contributions: first, we developed means to assess the extent of noise in the location data, and consequently for extracting signals from it. Second, we couple signal extraction with better characterization of the genetic network architecture. We apply two methods for the detection of combinatorial associations between transcription factors (TFs), the integration of which provides a global map of combinatorial regulatory interactions. We discover the capacity of regulatory motifs and TF partnerships to dictate fine-tuned expression patterns of subsets of genes, which are clearly distinct from those displayed by most genes assigned to the same TF. Our findings provide carefully prioritized, high-quality assignments between regulators and regulated genes and as such should prove useful for experimental and computational biologists alike.

The authors wish it to be known that, in their opinion, the first two authors should be regarded as joint First Authors


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