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Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification

Nature Methods volume 11, pages 73–78 (2014)Cite this article

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Abstract

Sequencing of DNase I hypersensitive sites (DNase-seq) is a powerful technique for identifying cis-regulatory elements across the genome. We studied the key experimental parameters to optimize performance of DNase-seq. Sequencing short fragments of 50–100 base pairs (bp) that accumulate in long internucleosome linker regions was more efficient for identifying transcription factor binding sites compared to sequencing longer fragments. We also assessed the potential of DNase-seq to predict transcription factor occupancy via generation of nucleotide-resolution transcription factor footprints. In modeling the sequence-specific DNase I cutting bias, we found a strong effect that varied over more than two orders of magnitude. This indicates that the nucleotide-resolution cleavage patterns at many transcription factor binding sites are derived from intrinsic DNase I cleavage bias rather than from specific protein-DNA interactions. In contrast, quantitative comparison of DNase I hypersensitivity between states can predict transcription factor occupancy associated with particular biological perturbations.

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Figure 1: Effect of digestion level and fragment size on recovery of known TF binding sites.
Figure 2: Nucleosome-positioning effects on DNase-seq results.
Figure 3: Pair-end sequencing of DHS sites.
Figure 4: CTCF footprint.
Figure 5: DNase I cleavage bias as revealed by AR and P53 binding.
Figure 6: Predicting TF binding from DNase-seq tag count and footprint score.

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Acknowledgements

This work was supported by grants from the US National Institutes of Health (1R01 GM099409 to X.S.L.; 1U41 HG007000 to X.S.L. and C.A.M.; 2P50 CA090381-06 to C.A.M. and M.B.; 2R01 DK074967-06 to M.B. and X.S.L., 1K99CA172948-01 to H.H.H.); the Mazzone Award (to X.S.L.), the Department of Defense (W81XWH-10-1-0557 to H.H.H.) and the Prostate Cancer Foundation (to M.B.).

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  1. Housheng Hansen He, Clifford A Meyer and Sheng'en Shawn Hu: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts, USA

    Housheng Hansen He, Clifford A Meyer, Chongzhi Zang, Teng Fei, Han Xu & X Shirley Liu

  2. Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts, USA

    Housheng Hansen He, Teng Fei & Myles Brown

  3. Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts, USA

    Housheng Hansen He, Clifford A Meyer, Sheng'en Shawn Hu, Mei-Wei Chen, Chongzhi Zang, Yin Liu, Prakash K Rao, Teng Fei, Han Xu, Henry Long, X Shirley Liu & Myles Brown

  4. Ontario Cancer Institute, Princess Margaret Cancer Center–University Health Network, Toronto, Ontario, Canada

    Housheng Hansen He

  5. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

    Housheng Hansen He

  6. Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China

    Sheng'en Shawn Hu & Yin Liu

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  1. Housheng Hansen He
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Contributions

H.H.H., C.A.M., H.L., X.S.L. and M.B. designed the experiments and wrote the manuscript. M.-W.C. and H.H.H. performed the experiments with the help from Y.L., P.K.R. and T.F. C.A.M., S.S.H. and H.H.H. conducted the data analysis with the help from C.Z. and H.X.

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Correspondence to Henry Long, X Shirley Liu or Myles Brown.

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The authors declare no competing financial interests.

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He, H., Meyer, C., Hu, S. et al. Refined DNase-seq protocol and data analysis reveals intrinsic bias in transcription factor footprint identification. Nat Methods 11, 73–78 (2014). https://doi.org/10.1038/nmeth.2762

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