Nucleic Acids Research Advance Access published online on September 18, 2006
Nucleic Acids Research, doi:10.1093/nar/gkl595
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© 2006 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-commerical use, distribution, and reproduction in any medium, provided the original work is properly cited.
Computational Biology |
Computational analysis of tissue-specific combinatorial gene regulation: predicting interaction between transcription factors in human tissues
1 Wilmer Institute, Johns Hopkins University School of Medicine Maumenee Building 844, 600 N. Wolfe Street, Baltimore, MD 21287, USA 2 Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine Maumenee Building 844, 600 N. Wolfe Street, Baltimore, MD 21287, USA 3 Department of Neuroscience, Johns Hopkins University School of Medicine Maumenee Building 844, 600 N. Wolfe Street, Baltimore, MD 21287, USA 4 McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine Maumenee Building 844, 600 N. Wolfe Street, Baltimore, MD 21287, USA
*To whom correspondence should be addressed. Tel: +1 443 287 3882; Fax: 1+ 410 502 5382; Email: jiang.qian{at}jhmi.edu
Received April 25, 2006. Revised July 13, 2006. Accepted August 1, 2006.
Tissue-specific gene expression is generally regulated by more than a single transcription factor (TF). Multiple TFs work in concert to achieve tissue specificity. In order to explore these complex TF interaction networks, we performed a large-scale analysis of TF interactions for 30 human tissues. We first identified tissue-specific genes for 30 tissues based on gene expression databases. We then evaluated the relationships between TFs using the relative position and co-occurrence of their binding sites in the promoters of tissue-specific genes. The predicted TF–TF interactions were validated by both known protein–protein interactions and co-expression of their target genes. We found that our predictions are enriched in known protein–protein interactions (>80 times that of random expectation). In addition, we found that the target genes show the highest co-expression in the tissue of interest. Our findings demonstrate that non-tissue specific TFs play a large role in regulation of tissue-specific genes. Furthermore, they show that individual TFs can contribute to tissue specificity in different tissues by interacting with distinct TF partners. Lastly, we identified several tissue-specific TF clusters that may play important roles in tissue-specific gene regulation.
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