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  Predicting transcription factor affinities to DNA from a biophysical model

Roider, H. G., Kanhere, A., Manke, T., & Vingron, M. (2007). Predicting transcription factor affinities to DNA from a biophysical model. Bioinformatics, 23(2), 134-141. doi:10.1093/bioinformatics/btl565.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8260-1 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8261-0
Genre: Journal Article
Alternative Title : Bioinformatics

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Roider, Helge G.1, Author              
Kanhere, Aditi2, Author
Manke, Thomas1, Author              
Vingron, Martin3, Author              
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              
2Max Planck Society, ou_persistent13              
3Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

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 Abstract: Motivation: Theoretical efforts to understand the regulation of gene expression are traditionally centered around the identification of transcription factor binding sites at specific DNA positions. More recently these efforts have been supplemented by experimental data for relative binding affinities of proteins to longer intergenic sequences. The question arises to what extent these two approaches converge. In this paper, we adopt a physical binding model to predict the relative binding affinity of a transcription factor for a given sequence. Results: We find that a significant fraction of genome-wide binding data in yeast can be accounted for by simple count matrices and a physical model with only two parameters. We demonstrate that our approach is both conceptually and practically more powerful than traditional methods, which require selection of a cutoff. Our analysis yields biologically meaningful parameters, suitable for predicting relative binding affinities in the absence of experimental binding data. Availability: The C source code for our TRAP program is freely available for non-commercial use at http://www.molgen.mpg.de/~manke/papers/TFaffinities/

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Language(s): eng - English
 Dates: 2007-02-01
 Publication Status: Published in print
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Title: Bioinformatics
  Alternative Title : Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 23 (2) Sequence Number: - Start / End Page: 134 - 141 Identifier: ISSN: 1367-4803