Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression
cis- Mathieu Blanchette1,5,
- Alain R. Bataille2,
- Xiaoyu Chen1,
- Christian Poitras2,
- Josée Laganière3,
- Céline Lefèbvre3,
- Geneviève Deblois3,
- Vincent Giguère3,
- Vincent Ferretti4,
- Dominique Bergeron2,
- Benoit Coulombe2, and
- François Robert2,5
- 1 McGill Centre for Bioinformatics, Montreal, Quebec, Canada, H3A 2B4;
- 2 Institut de Recherches Cliniques de Montréal, Montreal, Quebec, Canada H2W 1R7;
- 3 Molecular Oncology Group Department of Medicine, Oncology and Biochemistry, McGill University, Montreal, Quebec, Canada H3A 1A1;
- 4 McGill University and Genome Quebec Innovation Center, Montreal, Quebec, Canada H3A 1A4
Abstract
The identification of regulatory regions is one of the most important and challenging problems toward the functional annotation of the human genome. In higher eukaryotes, transcription-factor (TF) binding sites are often organized in clusters called cis-regulatory modules (CRM). While the prediction of individual TF-binding sites is a notoriously difficult problem, CRM prediction has proven to be somewhat more reliable. Starting from a set of predicted binding sites for more than 200 TF families documented in Transfac, we describe an algorithm relying on the principle that CRMs generally contain several phylogenetically conserved binding sites for a few different TFs. The method allows the prediction of more than 118,000 CRMs within the human genome. A subset of these is shown to be bound in vivo by TFs using ChIP-chip. Their analysis reveals, among other things, that CRM density varies widely across the genome, with CRM-rich regions often being located near genes encoding transcription factors involved in development. Predicted CRMs show a surprising enrichment near the 3′ end of genes and in regions far from genes. We document the tendency for certain TFs to bind modules located in specific regions with respect to their target genes and identify TFs likely to be involved in tissue-specific regulation. The set of predicted CRMs, which is made available as a public database called PReMod (http://genomequebec.mcgill.ca/PReMod), will help analyze regulatory mechanisms in specific biological systems.
Footnotes
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↵5 Corresponding authors.
↵5 E-mail blanchem{at}mcb.mcgill.ca; fax (514) 398-3387.
↵5 E-mail francois.Robert{at}ircm.qc.ca; fax (514) 987-5743.
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[Supplemental material is available online at www.genome.org.]
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Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.4866006
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6 Since PhastCons was designed to detect any type of region under selective pressure, many of its noncoding predictions are likely to have other nonregulatory functions.
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7 Note that the formula for moduleScore is actually an approximation of the true P-value, for the following reasons: (1) Since competition for space between different tags is not modeled, the computed P-value of the total score of the 2nd, 3rd, 4th, and 5th tags are slightly conservative; (2) since the totalScores are discrete variables (but with a very large number of possible values), the approximation with a continuous uniform distribution introduces a small error; (3) since the moduleScore is obtained by selecting the best of five P-values, a multiple hypothesis testing correction should be applied. However, since we are mostly interested in the ranking of modules, this correction would make no difference.
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8 Only a small number of maximal lengths could be tried, as the calculation of the TotalScore P-values are computationally expensive and depend on that length.
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- Received October 31, 2005.
- Accepted March 2, 2006.
- Cold Spring Harbor Laboratory Press
