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Bioinformatics Advance Access originally published online on June 27, 2008
Bioinformatics 2008 24(17):1874-1880; doi:10.1093/bioinformatics/btn332
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© 2008 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.

Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data

Huai Li and Ming Zhan *

Bioinformatics Unit, Branch of Research Resources, National Institute on Aging, NIH, Baltimore, MD 21224, USA

*To whom correspondence should be addressed.


  Abstract

Motivation: Unraveling the transcriptional regulatory program mediated by transcription factors (TFs) is a fundamental objective of computational biology, yet still remains a challenge.

Method: Here, we present a new methodology that integrates microarray and TF binding data for unraveling transcriptional regulatory networks. The algorithm is based on a two-stage constrained matrix decomposition model. The model takes into account the non-linear structure in gene expression data, particularly in the TF-target gene interactions and the combinatorial nature of gene regulation by TFs. The gene expression profile is modeled as a linear weighted combination of the activity profiles of a set of TFs. The TF activity profiles are deduced from the expression levels of TF target genes, instead directly from TFs themselves. The TF-target gene relationships are derived from ChIP-chip and other TF binding data. The proposed algorithm can not only identify transcriptional modules, but also reveal regulatory programs of which TFs control which target genes in which specific ways (either activating or inhibiting).

Results: In comparison with other methods, our algorithm identifies biologically more meaningful transcriptional modules relating to specific TFs. We applied the new algorithm on yeast cell cycle and stress response data. While known transcriptional regulations were confirmed, novel TF-gene interactions were predicted and provide new insights into the regulatory mechanisms of the cell.

Contact: zhanmi{at}mail.nih.gov

Supplementary information: Supplementary data are available at Bioinformatics online.

Associate Editor: Joaquin Dopazo

Received on March 12, 2008; revised on June 3, 2008; accepted on June 25, 2008

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