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Quantifying the effect of sequence variation on regulatory interactions

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50420

Manke,  T.
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50198

Heinig,  M.
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50613

Vingron,  M.
Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Manke.pdf
(beliebiger Volltext), 304KB

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Zitation

Manke, T., Heinig, M., & Vingron, M. (2010). Quantifying the effect of sequence variation on regulatory interactions. Hum Mutation, 31(4), 477-483. doi:10.1002/humu.21209.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-7B78-A
Zusammenfassung
The increasing amount of sequence data provides new opportunities and challenges to derive mechanistic models that can link sequence variations to phenotypic diversity. Here we introduce a new computational framework to suggest possible consequences of sequence variations on regulatory networks. Our method, called sTRAP (strap.molgen.mpg.de), analyses variations in the DNA sequence and predicts quantitative changes to the binding strength of any transcription factor for which there is a binding model. We have tested the method against a set of known associations between SNPs and their regulatory consequences. Our predictions are robust with respect to different parameters and model assumptions. Importantly we set an objective and quantifiable benchmark against which future improvements can be compared. Given the good performance of our method, we developed a publicly available tool that can serve as an important starting point for routine analysis of disease-associated sequence regions.