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PMID: 24317252 Published · ppublish English Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S.

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

Nature methods ·Vol. 11 ·No. 1 ·2014-01-00 ·Pages 73-78

He HH, Meyer CA, Hu SS, Chen MW, Zang C, Liu Y, Rao PK, Fei T, Xu H, Long H, Liu XS, Brown M

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.

MeSH Terms
Amino Acid Motifs Binding Sites Cell Line, Tumor Chromatin/chemistry Deoxyribonuclease I/chemistry Female Gene Expression Regulation Gene Regulatory Networks Humans K562 Cells MCF-7 Cells Male Nucleosomes/chemistry Nucleotides/chemistry Receptors, Androgen/chemistry Sequence Analysis, DNA/methods Transcription Factors/chemistry Tumor Suppressor Protein p53/chemistry
Chemicals
Chromatin Nucleosomes Nucleotides Receptors, Androgen TP53 protein, human Transcription Factors Tumor Suppressor Protein p53 Deoxyribonuclease I
Authors & Affiliations
12 authors, click to expand affiliations / ORCID
He Housheng Hansen
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.
Meyer Clifford A
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA. | Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
Hu Sheng'en Shawn
Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA. | Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 20092, China.
Chen Mei-Wei
Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
Zang Chongzhi
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA. | Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
Liu Yin
Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA. | Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, 20092, China.
Rao Prakash K
Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
Fei Teng
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.
Xu Han
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA. | Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
Long Henry
Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
Liu X Shirley
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA. | Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
Brown Myles
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.
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Article Info
Journal
Nature methods
Abbr.
Nat Methods
ISSN
1548-7105
Published
2014-01-00
Epub
2013-00-08
Pages
73-78
Language
English
Region
United States
NLM ID
101215604
PMCID
PMC4018771
Subset
IM
Grants
NCI NIH HHS · P50 CA090381 · United States
NCI NIH HHS · 2P50 CA090381-06 · United States
NIGMS NIH HHS · 1R01 GM099409 · United States
NHGRI NIH HHS · 1U41 HG007000 · United States
NCI NIH HHS · 1K99CA172948-01 · United States
NIGMS NIH HHS · R01 GM099409 · United States
NIDDK NIH HHS · 2R01 DK074967-06 · United States
Databases
GEO
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