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Nat Methods. 2014 Jan;11(1):73-78. doi: 10.1038/nmeth.2762. Epub 2013 Dec 8.

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

He HH#1,2,3,4,5, Meyer CA#1,3, Hu SS#3,6, Chen MW3, Zang C1,3, Liu Y3,6, Rao PK3, Fei T1,2,3, Xu H1,3, Long H3, Liu XS1,3, Brown M2,3.

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Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA.
Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, Massachusetts 02115, USA.
Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
Ontario Cancer Institute, Princess Margaret Cancer Center/University Health Network, Toronto, Ontario, M5G1L7, Canada.
Department of Medical Biophysics, University of Toronto, Toronto, Ontario, M5G2M9, Canada.
Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 20092, China.
Contributed equally


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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