This Article Full Text Full Text (PDF) Supplemental Research Data Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Google Scholar Articles by Corcoran, D. L. Articles by Benos, P. V. Search for Related Content PubMed PubMed Citation Articles by Corcoran, D. L. Articles by Benos, P. V. Social Bookmarking                   What's this?

Methods

Footer: A quantitative comparative genomics method for efficient recognition of cis-regulatory elements

¹ Department of Biostatistics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania 15621, USA ² Department of Human Genetics, GSPH, University of Pittsburgh, Pittsburgh, Pennsylvania 15621, USA ³ Department of Computational Biology and University of Pittsburgh Cancer Institute, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania 15621, USA ⁴ Children's Hospital of Pittsburgh, Pittsburgh 15213, USA

The search for mammalian DNA regulatory regions poses a challenging problem in computational biology. The short length of the DNA patterns compared with the size of the promoter regions and the degeneracy of the patterns makes their identification difficult. One way to overcome this problem is to use evolutionary information to reduce the number of false-positive predictions. We developed a novel method for pattern identification that compares a pair of putative binding sites in two species (e.g., human and mouse) and assigns two probability scores based on the relative position of the sites in the promoter and their agreement with a known model of binding preferences. We tested the algorithm's ability to predict known binding sites on various promoters. Overall, it exhibited 83% sensitivity and the specificity was 72%, which is a clear improvement over existing methods. Our algorithm also successfully predicted two novel NF-B binding sites in the promoter region of the mouse autotaxin gene (ATX, ENPP2), which we were able to verify by using chromatin immunoprecipitation assay coupled with quantitative real-time PCR.

[Supplemental material is available online at www.genome.org.]

Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.2952005.

CiteULike    Connotea    Del.icio.us    Digg    Reddit    Technorati    What's this?

This article has been cited by other articles:

				D. GuhaThakurta Computational identification of transcriptional regulatory elements in DNA sequence Nucleic Acids Res., July 19, 2006; 34(12): 3585 - 3598. [Abstract] [Full Text] [PDF]