Journal Article

MotifMap: a human genome-wide map of candidate regulatory motif sites

Xiaohui Xie, Paul Rigor and Pierre Baldi

in Bioinformatics

Volume 25, issue 2, pages 167-174
Published in print January 2009 | ISSN: 1367-4803
Published online November 2008 | e-ISSN: 1460-2059 | DOI:
MotifMap: a human genome-wide map of candidate regulatory motif sites

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Motivation: Achieving a comprehensive map of all the regulatory elements encoded in the human genome is a fundamental challenge of biomedical research. So far, only a small fraction of the regulatory elements have been characterized, and there is great interest in applying computational techniques to systematically discover these elements. Such efforts, however, have been significantly hindered by the overwhelming size of non-coding DNA regions and the statistical variability and complex spatial organizations of mammalian regulatory elements.

Results: Here we combine information from multiple mammalian genomes to derive the first fairly comprehensive map of regulatory elements in the human genome. We develop a procedure for identifying regulatory sites, with high levels of conservation across different species, using a new scoring scheme, the Bayesian branch length score (BBLS). Using BBLS, we predict 1.5 million regulatory sites, corresponding to 380 known regulatory motifs, with an estimated false discovery rate (FDR) of <50%. We demonstrate that the method is particularly effective for 155 motifs, for which 121 056 sites can be mapped with an estimated FDR of <10%. Over 28K SNPs are located in regions overlapping the 1.5 million predicted motif sites, suggesting potential functional implications for these SNPs. We have deposited these elements in a database and created a user-friendly web server for the retrieval, analysis and visualization of these elements. The initial map provides a systematic view of gene regulation in the genome, which will be refined as additional motifs become available.



Supplementary information: Supplementary data are available at Bioinformatics online.

Journal Article.  6106 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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