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Genome-wide footprinting: ready for prime time?


High-throughput sequencing technologies have allowed many gene locus–level molecular biology assays to become genome-wide profiling methods. DNA-cleaving enzymes such as DNase I have been used to probe accessible chromatin. The accessible regions contain functional regulatory sites, including promoters, insulators and enhancers. Deep sequencing of DNase-seq libraries and computational analysis of the cut profiles have been used to infer protein occupancy in the genome at the nucleotide level, a method introduced as 'digital genomic footprinting'. The approach has been proposed as an attractive alternative to the analysis of transcription factors (TFs) by chromatin immunoprecipitation followed by sequencing (ChIP-seq), and in theory it should overcome antibody issues, poor resolution and batch effects. Recent reports point to limitations of the DNase-based genomic footprinting approach and call into question the scope of detectable protein occupancy, especially for TFs with short-lived chromatin binding. The genomics community is grappling with issues concerning the utility of genomic footprinting and is reassessing the proposed approaches in terms of robust deliverables. Here we summarize the consensus as well as different views emerging from recent reports, and we describe the remaining issues and hurdles for genomic footprinting.

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Figure 1: DHSs versus TF footprints.
Figure 2: Comparison of observed and DNA-intrinsic cut profiles averaged over motif elements bound by TFs.
Figure 3: DNase sequence-bias-corrected profiles showing the correlation between footprint depth and TF binding residence time in vivo.
Figure 4: The difficulty of assigning TFs on the basis of motif matches.


  1. 1

    Tullius, T.D. Physical studies of protein-DNA complexes by footprinting. Annu. Rev. Biophys. Biophys. Chem. 18, 213–237 (1989).

    CAS  PubMed  Google Scholar 

  2. 2

    Church, G.M., Ephrussi, A., Gilbert, W. & Tonegawa, S. Cell-type-specific contacts to immunoglobulin enhancers in nuclei. Nature 313, 798–801 (1985).

    CAS  PubMed  Google Scholar 

  3. 3

    Jackson, P.D. & Felsenfeld, G. A method for mapping intranuclear protein-DNA interactions and its application to a nuclease hypersensitive site. Proc. Natl. Acad. Sci. USA 82, 2296–2300 (1985).

    CAS  PubMed  Google Scholar 

  4. 4

    Hesselberth, J.R. et al. Global mapping of protein-DNA interactions in vivo by digital genomic footprinting. Nat. Methods 6, 283–289 (2009).

    CAS  Article  Google Scholar 

  5. 5

    Thurman, R.E. et al. The accessible chromatin landscape of the human genome. Nature 489, 75–82 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

    Baek, S., Sung, M.H. & Hager, G.L. Quantitative analysis of genome-wide chromatin remodeling. Methods Mol. Biol. 833, 433–441 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Morris, S.A. et al. Overlapping chromatin remodeling systems collaborate genome-wide at dynamic chromatin transitions. Nat. Struct. Mol. Biol. 21, 73–81 (2014).

    CAS  PubMed  Google Scholar 

  8. 8

    John, S. et al. Chromatin accessibility pre-determines glucocorticoid receptor binding patterns. Nat. Genet. 43, 264–268 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9

    Sung, M.H., Guertin, M.J., Baek, S. & Hager, G.L. DNase footprint signatures are dictated by factor dynamics and DNA sequence. Mol. Cell 56, 275–285 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    He, H.H. et al. Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification. Nat. Methods 11, 73–78 (2014).

    CAS  Google Scholar 

  11. 11

    Pique-Regi, R. et al. Accurate inference of transcription factor binding from DNA sequence and chromatin accessibility data. Genome Res. 21, 447–455 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Neph, S. et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 489, 83–90 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Mercer, T.R. et al. The human mitochondrial transcriptome. Cell 146, 645–658 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14

    Boyle, A.P. et al. High-resolution genome-wide in vivo footprinting of diverse transcription factors in human cells. Genome Res. 21, 456–464 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Cuellar-Partida, G. et al. Epigenetic priors for identifying active transcription factor binding sites. Bioinformatics 28, 56–62 (2012).

    CAS  PubMed  Google Scholar 

  16. 16

    Luo, K. & Hartemink, A.J. Using DNase digestion data to accurately identify transcription factor binding sites. Pac. Symp. Biocomput. 2013, 80–91 (2013).

    Google Scholar 

  17. 17

    Gusmao, E.G., Dieterich, C., Zenke, M. & Costa, I.G. Detection of active transcription factor binding sites with the combination of DNase hypersensitivity and histone modifications. Bioinformatics 30, 3143–3151 (2014).

    CAS  PubMed  Google Scholar 

  18. 18

    Sherwood, R.I. et al. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nat. Biotechnol. 32, 171–178 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Piper, J. et al. Wellington: a novel method for the accurate identification of digital genomic footprints from DNase-seq data. Nucleic Acids Res. 41, e201 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Lazarovici, A. et al. Probing DNA shape and methylation state on a genomic scale with DNase I. Proc. Natl. Acad. Sci. USA 110, 6376–6381 (2013).

    CAS  Google Scholar 

  21. 21

    Yardimci, G.G., Frank, C.L., Crawford, G.E. & Ohler, U. Explicit DNase sequence bias modeling enables high-resolution transcription factor footprint detection. Nucleic Acids Res. 42, 11865–11878 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    Grøntved, L. et al. Rapid genome-scale mapping of chromatin accessibility in tissue. Epigenetics Chromatin 5, 10 (2012).

    PubMed  PubMed Central  Google Scholar 

  23. 23

    Nakahashi, H. et al. A genome-wide map of CTCF multivalency redefines the CTCF code. Cell Rep. 3, 1678–1689 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Poorey, K. et al. Measuring chromatin interaction dynamics on the second time scale at single-copy genes. Science 342, 369–372 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Buenrostro, J.D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Cusanovich, D.A. et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Voss, T.C. & Hager, G.L. Dynamic regulation of transcriptional states by chromatin and transcription factors. Nat. Rev. Genet. 15, 69–81 (2014).

    CAS  PubMed  Google Scholar 

  28. 28

    Sung, M.H. & McNally, J.G. Live cell imaging and systems biology. Wiley Interdiscip. Rev. Syst. Biol. Med. 3, 167–182 (2011).

    CAS  PubMed  Google Scholar 

  29. 29

    Hager, G.L., McNally, J.G. & Misteli, T. Transcription dynamics. Mol. Cell 35, 741–753 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Voss, T.C. et al. Dynamic exchange at regulatory elements during chromatin remodeling underlies assisted loading mechanism. Cell 146, 544–554 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

    Morisaki, T., Muller, W.G., Golob, N., Mazza, D. & McNally, J.G. Single-molecule analysis of transcription factor binding at transcription sites in live cells. Nat. Commun. 5, 4456 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32

    van Royen, M.E. et al. Androgen receptor complexes probe DNA for recognition sequences by short random interactions. J. Cell Sci. 127, 1406–1416 (2014).

    CAS  PubMed  Google Scholar 

  33. 33

    Groeneweg, F.L. et al. Quantitation of glucocorticoid receptor DNA-binding dynamics by single-molecule microscopy and FRAP. PLoS ONE 9, e90532 (2014).

    PubMed  PubMed Central  Google Scholar 

  34. 34

    Gebhardt, J.C. et al. Single-molecule imaging of transcription factor binding to DNA in live mammalian cells. Nat. Methods 10, 421–426 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35

    Crocker, J. et al. Low affinity binding site clusters confer Hox specificity and regulatory robustness. Cell 160, 191–203 (2015).

    CAS  PubMed  Google Scholar 

  36. 36

    Chen, J. et al. Single-molecule dynamics of enhanceosome assembly in embryonic stem cells. Cell 156, 1274–1285 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Sharp, Z.D. et al. Estrogen-receptor-alpha exchange and chromatin dynamics are ligand- and domain-dependent. J. Cell Sci. 119, 4101–4116 (2006).

    CAS  PubMed  Google Scholar 

  38. 38

    Bosisio, D. et al. A hyper-dynamic equilibrium between promoter-bound and nucleoplasmic dimers controls NF-kB-dependent gene activity. EMBO J. 25, 798–810 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    McNally, J.G., Mueller, W.G., Walker, D., Wolford, R.G. & Hager, G.L. The glucocorticoid receptor: rapid exchange with regulatory sites in living cells. Science 287, 1262–1265 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Stergachis, A.B. et al. Conservation of trans-acting circuitry during mammalian regulatory evolution. Nature 515, 365–370 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Neph, S. et al. Circuitry and dynamics of human transcription factor regulatory networks. Cell 150, 1274–1286 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Badis, G. et al. Diversity and complexity in DNA recognition by transcription factors. Science 324, 1720–1723 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

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

    CAS  Google Scholar 

  44. 44

    Weirauch, M.T. et al. Evaluation of methods for modeling transcription factor sequence specificity. Nat. Biotechnol. 31, 126–134 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

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

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Bailey, T.L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    Grant, C.E., Bailey, T.L. & Noble, W.S. FIMO: scanning for occurrences of a given motif. Bioinformatics 27, 1017–1018 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    Biddie, S.C. et al. Transcription factor AP1 potentiates chromatin accessibility and glucocorticoid receptor binding. Mol. Cell 43, 145–155 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Lickwar, C.R., Mueller, F., Hanlon, S.E., McNally, J.G. & Lieb, J.D. Genome-wide protein-DNA binding dynamics suggest a molecular clutch for transcription factor function. Nature 484, 251–255 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Malnou, C.E. et al. Heterodimerization with different Jun proteins controls c-Fos intranuclear dynamics and distribution. J. Biol. Chem. 285, 6552–6562 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. 52

    Mayr, B.M., Guzman, E. & Montminy, M. Glutamine rich and basic region/leucine zipper (bZIP) domains stabilize cAMP-response element-binding protein (CREB) binding to chromatin. J. Biol. Chem. 280, 15103–15110 (2005).

    CAS  PubMed  Google Scholar 

  53. 53

    Guertin, M.J., Zhang, X., Coonrod, S.A. & Hager, G.L. Transient ER binding and p300 redistribution support a squelching mechanism for E2-repressed genes. Mol. Endocrinol. 28, 1522–1533 (2014).

    PubMed  PubMed Central  Google Scholar 

  54. 54

    Grøntved, L. et al. Transcriptional activation by the thyroid hormone receptor through ligand dependent receptor recruitment and chromatin remodeling. Nat. Commun. 6, 7048 (2015).

    PubMed  PubMed Central  Google Scholar 

  55. 55

    Marcelli, M. et al. Quantifying effects of ligands on androgen receptor nuclear translocation, intranuclear dynamics, and solubility. J. Cell. Biochem. 98, 770–788 (2006).

    CAS  PubMed  Google Scholar 

  56. 56

    Yu, J. et al. An integrated network of androgen receptor, polycomb, and TMPRSS2-ERG gene fusions in prostate cancer progression. Cancer Cell 17, 443–454 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

    Tirard, M., Almeida, O.F., Hutzler, P., Melchior, F. & Michaelidis, T.M. Sumoylation and proteasomal activity determine the transactivation properties of the mineralocorticoid receptor. Mol. Cell. Endocrinol. 268, 20–29 (2007).

    CAS  PubMed  Google Scholar 

  58. 58

    Le Billan, F. et al. Cistrome of the aldosterone-activated mineralocorticoid receptor in human renal cells. FASEB J. 29, 3977–3989 (2015).

    CAS  PubMed  Google Scholar 

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Computationally intensive tasks were performed using the US National Institutes of Health (NIH) Biowulf cluster, a GNU-Linux parallel processing system. We thank the NIH Helix systems staff for the management of this system. This work was supported by the Intramural Research Program of the NIH, National Cancer Institute.

Author information




M.-H.S. and G.L.H. conceived the project. S.B. and M.-H.S. performed the analysis. M.-H.S. and G.L.H. wrote the manuscript.

Corresponding authors

Correspondence to Myong-Hee Sung or Gordon L Hager.

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The authors declare no competing financial interests.

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Sung, MH., Baek, S. & Hager, G. Genome-wide footprinting: ready for prime time?. Nat Methods 13, 222–228 (2016).

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