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Nucleic Acids Research
Article . 2004 . Peer-reviewed
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Identification of sparsely distributed clusters of cis-regulatory elements in sets of co-expressed genes

Authors: Kreiman, Gabriel;

Identification of sparsely distributed clusters of cis-regulatory elements in sets of co-expressed genes

Abstract

Sequence information and high-throughput methods to measure gene expression levels open the door to explore transcriptional regulation using computational tools. Combinatorial regulation and sparseness of regulatory elements throughout the genome allow organisms to control the spatial and temporal patterns of gene expression. Here we study the organization of cis-regulatory elements in sets of co-regulated genes. We build an algorithm to search for combinations of transcription factor binding sites that are enriched in a set of potentially co-regulated genes with respect to the whole genome. No knowledge is assumed about involvement of specific sets of transcription factors. Instead, the search is exhaustively conducted over combinations of up to four binding sites obtained from databases or motif search algorithms. We evaluate the performance on random sets of genes as a negative control and on three biologically validated sets of co-regulated genes in yeasts, flies and humans. We show that we can detect DNA regions that play a role in the control of transcription. These results shed light on the structure of transcription regulatory regions in eukaryotes and can be directly applied to clusters of co-expressed genes obtained in gene expression studies. Supplementary information is available at http://www.mit.edu/ approximately kreiman/resources/cisregul/.

Country
United States
Keywords

570, Binding Sites, Cell Cycle, Computational Biology, Saccharomyces cerevisiae, Response Elements, 576, Drosophila melanogaster, Gene Expression Regulation, Animals, Humans, Muscle, Skeletal, Algorithms, Transcription Factors

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    42
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
42
Average
Top 10%
Top 10%
gold