Bioinformatics Advance Access published online on February 18, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm055
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Position dependencies in transcription factor binding sites
1Friedrich Miescher Institute for Biomedical Research, Novartis Research Foundation, Maulbeerestrasse 66, CH-4058 Basel, Switzerland
*To whom correspondence should be addressed. Dr. Edward Oakeley, E-mail: edward.oakeley{at}fmi.ch
 |
Abstract |
|---|
Motivation: Most of the available tools for transcription factor binding site prediction are based on methods which assume no sequence dependence between the binding site base positions. Our primary objective was to investigate the statistical basis for either a claim of dependence or independence, to determine w. Whether such a claim is generally true, and to use these resulting data to develop improved scoring functions for binding site prediction.
Results: Using three statistical tests, we analyzed the number of binding sites showing dependent positions. We analyzed transcription factor-DNA crystal structures for evidence of position dependence. Our final conclusions were that some factors show evidence for of dependencies whereas others do not. We observed that the conformational energy (Z-score) of the transcription factor-DNA complexes was lower (better) for sequences that showed dependency than for those that did not (p<0.02). We suggest that where evidence exists for dependencies, these should be modeled in order to improve binding-site predictions. However, when no significant dependency is found, this correction should be omitted. This may be done by converting any existing scoring function, which assumes independence into a form which includes a dependency correction. We present an example of such an algorithm and its implementation as a web tool.
Availability: http://promoterplot.fmi.ch/cgi-bin/dep.html
Supplemental material: Supplementary data (1, 2, 3, 4, 5, 6, 7, and 8) are available from Bioinformatics online.
Associate Editor: Alfonso Valencia
Received on October 29, 2006; revised on January 17, 2007; accepted on February 9, 2007
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  K. B. S. Swamy, C.-Y. Cho, S. Chiang, Z. T.-Y. Tsai, and H.-K. Tsai Impact of DNA-binding position variants on yeast gene expression Nucleic Acids Res., September 18, 2009; (2009) gkp743v1. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 J. Liu and G. D. Stormo Context-dependent DNA recognition code for C2H2 zinc-finger transcription factors Bioinformatics, September 1, 2008; 24(17): 1850 - 1857. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
B. Hooghe, P. Hulpiau, F. van Roy, and P. De Bleser ConTra: a promoter alignment analysis tool for identification of transcription factor binding sites across species Nucleic Acids Res., July 1, 2008; 36(suppl_2): W128 - W132. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 A. Vandenbon, Y. Miyamoto, N. Takimoto, T. Kusakabe, and K. Nakai Markov Chain-based Promoter Structure Modeling for Tissue-specific Expression Pattern Prediction DNA Res, February 7, 2008; (2008) dsm034v1. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
L. Segal, M. Lapidot, Z. Solan, E. Ruppin, Y. Pilpel, and D. Horn Nucleotide variation of regulatory motifs may lead to distinct expression patterns Bioinformatics, July 1, 2007; 23(13): i440 - i449. [Abstract] [Full Text] [PDF]
|
|