Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Mapping genome-wide transcription-factor binding sites using DAP-seq


To enable low-cost, high-throughput generation of cistrome and epicistrome maps for any organism, we developed DNA affinity purification sequencing (DAP-seq), a transcription factor (TF)-binding site (TFBS) discovery assay that couples affinity-purified TFs with next-generation sequencing of a genomic DNA library. The method is fast, inexpensive, and more easily scaled than chromatin immunoprecipitation sequencing (ChIP-seq). DNA libraries are constructed using native genomic DNA from any source of interest, preserving cell- and tissue-specific chemical modifications that are known to affect TF binding (such as DNA methylation) and providing increased specificity as compared with in silico predictions based on motifs from methods such as protein-binding microarrays (PBMs) and systematic evolution of ligands by exponential enrichment (SELEX). The resulting DNA library is incubated with an affinity-tagged in vitro-expressed TF, and TF–DNA complexes are purified using magnetic separation of the affinity tag. Bound genomic DNA is eluted from the TF and sequenced using next-generation sequencing. Sequence reads are mapped to a reference genome, identifying genome-wide binding locations for each TF assayed, from which sequence motifs can then be derived. A researcher with molecular biology experience should be able to follow this protocol, processing up to 400 samples per week.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: DAP-seq protocol overview.
Figure 2: DAP-seq DNA library titration experiment with Arabidopsis transcription factor TGA5 (AT5G06960).


  1. Swinnen, G., Goossens, A. & Pauwels, L. Lessons from domestication: targeting cis-regulatory elements for crop improvement. Trends Plant Sci. (2016).

  2. Deplancke, B., Alpern, D. & Gardeux, V. The genetics of transcription factor DNA binding variation. Cell (2016).

  3. Babu, M.M., Luscombe, N.M., Aravind, L., Gerstein, M. & Teichmann, S.A. Structure and evolution of transcriptional regulatory networks. Curr. Opin. Struct. Biol. 14, 283–291 (2004).

    Article  CAS  Google Scholar 

  4. Niu, W. et al. Diverse transcription factor binding features revealed by genome-wide ChIP-seq in C. elegans. Genome Res. 21, 245–254 (2011).

    Article  CAS  Google Scholar 

  5. Negre, N. et al. A cis-regulatory map of the Drosophila genome. Nature 471, 527–531 (2011).

    Article  CAS  Google Scholar 

  6. Gerstein, M.B. et al. Architecture of the human regulatory network derived from ENCODE data. Nature 489, 91–100 (2012).

    Article  CAS  Google Scholar 

  7. Celniker, S.E. et al. Unlocking the secrets of the genome. Nature 18, 927–930 (2009).

    Article  Google Scholar 

  8. Landt, S.G. et al. ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Res. 22, 1813–1831 (2012).

    Article  CAS  Google Scholar 

  9. Weirauch, M.T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).

    Article  CAS  Google Scholar 

  10. Jolma, A. et al. DNA-binding specificities of human transcription factors. Cell 152, 327–339 (2013).

    Article  CAS  Google Scholar 

  11. Jolma, A. et al. DNA-dependent formation of transcription factor pairs alters their binding specificity. Nature 527, 384–388 (2015).

    Article  CAS  Google Scholar 

  12. Domcke, S. et al. Competition between DNA methylation and transcription factors determines binding of NRF1. Nature 528, 575–579 (2015).

    Article  CAS  Google Scholar 

  13. Hu, S. et al. DNA methylation presents distinct binding sites for human transcription factors. Elife 2013 (2013).

  14. Raghav, S.K. et al. Integrative genomics identifies the corepressor SMRT as a gatekeeper of adipogenesis through the transcription factors C/EBPB and KAISO. Mol. Cell 46, 335–350 (2012).

    Article  CAS  Google Scholar 

  15. O'Malley, R.C. et al. Cistrome and epicistrome features shape the regulatory DNA landscape. Cell (2016).

  16. Dror, I., Golan, T., Levy, C., Rohs, R. & Mandel-Gutfreund, Y. A widespread role of the motif environment in transcription factor binding across diverse protein families. Genome Res. (2015).

  17. Los, G.V. et al. HaloTag: a novel protein labeling technology for cell imaging and protein analysis. ACS Chem. Biol. (2008).

  18. Worsley Hunt, R. & Wasserman, W.W. Non-targeted transcription factors motifs are a systemic component of ChIP-seq datasets. Genome Biol. 15, 412 (2014).

    Article  Google Scholar 

  19. Schultz, M.D. et al. Human body epigenome maps reveal noncanonical DNA methylation variation. Nature 523, 212–216 (2015).

    Article  CAS  Google Scholar 

  20. Kawakatsu, T. et al. Unique cell-type-specific patterns of DNA methylation in the root meristem. Nat. Plants 2, 16058 (2016).

    Article  CAS  Google Scholar 

  21. Song, L. & Crawford, G.E. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. (2010).

  22. Buenrostro, J.D., Giresi, P.G., Zaba, L.C., Chang, H.Y. & Greenleaf, W.J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).

    Article  CAS  Google Scholar 

  23. Rizzo, J.M. & Sinha, S. Epidermal Cells: Methods and Protocols (ed. Turksen, K.) 49–59 (Springer, 2014).

  24. Kawakatsu, T. et al. Epigenomic diversity in a global collection of Arabidopsis thaliana accessions. Cell 166, 492–506 (2016).

    Article  CAS  Google Scholar 

  25. Chen, K., Zhao, B.S. & He, C. Nucleic acid modifications in regulation of gene expression. Cell Chem. Biol. 23, 74–85 (2016).

    Article  CAS  Google Scholar 

  26. Arabidopsis Interactome Mapping Consortium. Evidence for network evolution in an Arabidopsis interactome map. Science 333, 601–607 (2011).

  27. Yazaki, J. et al. Mapping transcription factor interactome networks using HaloTag protein arrays 113, E4238–E4247.

  28. Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  Google Scholar 

  29. Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    Article  Google Scholar 

  30. Guo, Y., Mahony, S. & Gifford, D.K. High resolution genome wide binding event finding and motif discovery reveals transcription factor spatial binding constraints. PLoS Comput. Biol. 8, e1002638 (2012).

    Article  CAS  Google Scholar 

  31. Robinson, J.T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).

    Article  CAS  Google Scholar 

  32. Machanick, P. & Bailey, T.L. MEME-ChIP: motif analysis of large DNA datasets. Bioinformatics 27, 1696–1697 (2011).

    Article  CAS  Google Scholar 

  33. Carroll, T.S., Liang, Z., Salama, R., Stark, R. & de Santiago, I. Impact of artifact removal on ChIP quality metrics in ChIP-seq and ChIP-exo data. Front. Genet. 5, 75 (2014).

    Article  Google Scholar 

  34. Ou, J. & Zhu, L.J. motifStack: plot stacked logos for single or multiple DNA, RNA and amino acid sequence. (2015).

  35. Quinlan, A.R. & Hall, I.M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).

    Article  CAS  Google Scholar 

  36. Harper, S. & Speicher, D.W. in Protein Chromatography (Humana Press) 681, 259–280 (2011).

    Article  CAS  Google Scholar 

  37. Structural Genomics Consortium. et al. Protein production and purification. Nat. Methods 5, 135–146 (2008).

  38. Urich, M.A., Nery, J.R., Lister, R., Schmitz, R.J. & Ecker, J.R. MethylC-seq library preparation for base-resolution whole-genome bisulfite sequencing. Nat. Protoc. 10, 475–83 (2015).

    Article  CAS  Google Scholar 

  39. Gallagher, S. & Chakavarti, D. Immunoblot analysis. J. Vis. Exp. 2, 2008 (2008).

    Google Scholar 

Download references


This work was supported by grants from the National Science Foundation (MCB1024999) and the Gordon and Betty Moore Foundation (GBMF3034) to J.R.E., as well as from the National Science Foundation to A.G. (IOS1114484 and IOS1546873). J.R.E. is a Howard Hughes Medical Institute Investigator.

Author information

Authors and Affiliations



R.C.O. and J.R.E. designed the original protocol. R.C.O., A.B., S.-s.C.H., M.G., and A.G. modified and updated the protocol to its current state. J.R.N. performed all the sequencing. A.B., M.G., S.-s.C.H., and J.R.E. wrote the manuscript with contributions from all authors.

Corresponding author

Correspondence to Joseph R Ecker.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bartlett, A., O'Malley, R., Huang, Ss. et al. Mapping genome-wide transcription-factor binding sites using DAP-seq. Nat Protoc 12, 1659–1672 (2017).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing