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Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons50595

Thomas-Chollier,  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/persons73914

Hufton,  A.
Evolution and Development (Albert Poustka), Dept. of Vertebrate Genomics (Head: Hans Lehrach), 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/persons50500

Roider,  H. G.
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/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/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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Citation

Thomas-Chollier, M., Hufton, A., Heinig, M., O'Keeffe, S., Masri, N. E., Roider, H. G., et al. (2011). Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nat Protoc, 6(12), 1860-9. Retrieved from http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=22051799 http://www.nature.com/nprot/journal/v6/n12/pdf/nprot.2011.409.pdf.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-7934-3
Abstract
The transcription factor affinity prediction (TRAP) method calculates the affinity of transcription factors for DNA sequences on the basis of a biophysical model. This method has proven to be useful for several applications, including for determining the putative target genes of a given factor. This protocol covers two other applications: (i) determining which transcription factors have the highest affinity in a set of sequences (illustrated with chromatin immunoprecipitation-sequencing (ChIP-seq) peaks), and (ii) finding which factor is the most affected by a regulatory single-nucleotide polymorphism. The protocol describes how to use the TRAP web tools to address these questions, and it also presents a way to run TRAP on random control sequences to better estimate the significance of the results. All of the tools are fully available online and do not need any additional installation. The complete protocol takes about 45 min, but each individual tool runs in a few minutes.