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

Nature Protocols volume 6, pages 1860–1869 (2011)Cite this article

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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.

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Figure 1: General principle of hit-based and affinity-based approaches.
Figure 3: The 'TRAP multiple sequences' form.
Figure 4: The 'TRAP multiple sequences' result for the glucocorticoid receptor example.
Figure 5: The results page of the binding site predictions on the ChIP-seq example data set, shown after clicking on the 'Sites' button.
Figure 6: The results page for 'TRAP multiple sequences' analyses.
Figure 7: The results page for the 'sTRAP' regulatory SNP analysis.
Figure 8: Affinity profiles for 'sTRAP' regulatory SNP analysis, for the top-ranking matrix.
Figure 2: Home page of the TRAP web tools.

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Acknowledgements

This work was supported by the Alexander von Humboldt foundation. We thank A. Mysickova for her insightful comments on this protocol, and S. Haas for helpful discussions.

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Authors and Affiliations

  1. Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany

    Morgane Thomas-Chollier, Andrew Hufton, Matthias Heinig, Sean O'Keeffe, Nassim El Masri, Helge G Roider & Martin Vingron

  2. Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany

    Thomas Manke

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Contributions

H.G.R., T.M. and M.V. developed the original TRAP approach and the P value calculation. M.H., T.M. and M.V. developed the sTRAP approach. A.H. implemented the combination of P values approach and the first version of the web tools. M.T.-C. integrated the different approaches, added new matrix and background models, and supervised the remodeling of the website by N.E.M. S.O. helped in the implementation of the website. M.T.-C. wrote the manuscript, and A.H., S.O., H.G.R. and T.M. edited the manuscript.

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Correspondence to Morgane Thomas-Chollier.

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The authors declare no competing financial interests.

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Thomas-Chollier, M., Hufton, A., Heinig, M. et al. Transcription factor binding predictions using TRAP for the analysis of ChIP-seq data and regulatory SNPs. Nat Protoc 6, 1860–1869 (2011). https://doi.org/10.1038/nprot.2011.409

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