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Large-scale analysis of transcriptional cis-regulatory modules reveals both common features and distinct subclasses

Long Li1, Qianqian Zhu1, Xin He3, Saurabh Sinha3 and Marc S Halfon1245*

Author Affiliations

1 Department of Biochemistry, State University of New York at Buffalo, Buffalo, NY 14214, USA

2 Department of Biological Sciences, State University of New York at Buffalo, Buffalo, NY 14214, USA

3 Department of Computer Science, University of Illinois Urbana-Champaign, Urbana, IL 61801, USA

4 New York State Center of Excellence in Bioinformatics and the Life Sciences, Buffalo, NY 14203, USA

5 Department of Molecular and Cellular Biology, Roswell Park Cancer Institute, Buffalo, NY 14263, USA

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Genome Biology 2007, 8:R101  doi:10.1186/gb-2007-8-6-r101

Published: 5 June 2007



Transcriptional cis-regulatory modules (for example, enhancers) play a critical role in regulating gene expression. While many individual regulatory elements have been characterized, they have never been analyzed as a class.


We have performed the first such large-scale study of cis-regulatory modules in order to determine whether they have common properties that might aid in their identification and contribute to our understanding of the mechanisms by which they function. A total of 280 individual, experimentally verified cis-regulatory modules from Drosophila were analyzed for a range of sequence-level and functional properties. We report here that regulatory modules do indeed share common properties, among them an elevated GC content, an increased level of interspecific sequence conservation, and a tendency to be transcribed into RNA. However, we find that dense clustering of transcription factor binding sites, especially homotypic clustering, which is commonly believed to be a general characteristic of regulatory modules, is rather a feature that belongs chiefly to a specific subclass. This has important implications for current computational approaches, many of which are biased toward this subset. We explore two new strategies to assess binding site clustering and gauge their performances with respect to their ability to detect all 280 modules and various functionally coherent subsets.


Our findings demonstrate that cis-regulatory modules share common features that help to define them as a class and that may lead to new insights into mechanisms of gene regulation. However, these properties alone may not be sufficient to reliably distinguish regulatory from non-regulatory sequences. We also demonstrate that there are distinct subclasses of cis-regulatory modules that are more amenable to in silico detection than others and that these differences must be taken into account when attempting genome-wide regulatory element discovery.