Cited by 168Genomics and Chromatin DynamicsChromatin Remodeling and CancerPeroxisome Proliferator-Activated Receptors

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Systematic dissection of genomic features determining transcription factor binding and enhancer function

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Grossman, Sharon R., Zhang, Xiaolan, Wang, Li, Engreitz, Jesse, Melnikov, Alexandre, Rogov, Peter, Tewhey, Ryan, Isakova, Alina, Deplancke, Bart, Bernstein, Bradley E., Mikkelsen, Tarjei S., Lander, Eric S.·2017·Proceedings of the National Academy of Sciences

DOI 10.1073/pnas.1621150114PMC PMC5321001

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Abstract

Significance: A central question in biology is how transcription factors (TFs) recognize specific binding sites in enhancers and regulate gene expression. In general, only a fraction of potential binding sites for TFs are occupied in a particular cell type. TF affinity for a motif site, local interactions among TFs, and larger-scale chromatin accessibility can influence binding, although the relative contributions of these factors is unclear. Moreover, little is known about how specific combinations of TFs control quantitative gene expression once bound. Here, we use large-scale synthetic biology approaches to explore the features that govern TF binding vs. enhancer activity. This approach provides a paradigm for systematic study of key regulatory sequences within enhancers and how they interact to influence gene expression.

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An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
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Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368
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FIMO: scanning for occurrences of a given motif
Grant, C. E., Bailey, T. L., & Noble, W. S. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr064 · Cited by 4,247
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Mikkelsen, T. S., Ku, M., Jaffe, D. B., Issac, B., Lieberman, E., Giannoukos, G., Alvarez, P., Brockman, W., Kim, T.-K., Koche, R. P., et al. (2007) · Nature · doi:10.1038/nature06008 · Cited by 4,129
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Johnson, D. S., Mortazavi, A., Myers, R. M., & Wold, B. (2007) · Science · doi:10.1126/science.1141319 · Cited by 2,927
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Thurman, R. E., Rynes, E., Humbert, R., Vierstra, J., Maurano, M. T., Haugen, E., Sheffield, N. C., Stergachis, A. B., Wang, H., Vernot, B., et al. (2012) · Nature · doi:10.1038/nature11232 · Cited by 2,679
Transcription factors: from enhancer binding to developmental control
Spitz, F., & Furlong, E. E. M. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3207 · Cited by 2,227
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Kamei, Y., Xu, L., Heinzel, T., Torchia, J., Kurokawa, R., Gloss, B., Lin, S.-C., Heyman, R. A., Rose, D. W., Glass, C. K., et al. (1996) · Cell · doi:10.1016/s0092-8674(00)81118-6 · Cited by 2,150

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Additional file 2 of Quantitative-enhancer-FACS-seq (QeFS) reveals epistatic interactions among motifs within transcriptional enhancers in developing Drosophila tissue
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Additional file 5 of Quantitative-enhancer-FACS-seq (QeFS) reveals epistatic interactions among motifs within transcriptional enhancers in developing Drosophila tissue
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Authors: Grossman, S. R., Zhang, X., Wang, L., Engreitz, J., Melnikov, A., Rogov, P., Tewhey, R., Isakova, A., Deplancke, B., Bernstein, B. E., Mikkelsen, T. S., & Lander, E. S.
Affiliations: Broad Institute (US)
SIB Swiss Institute of Bioinformatics (CH)
ORCID: Grossman, Sharon R.
Zhang, Xiaolan
Wang, Li
Engreitz, Jesse
Melnikov, Alexandre
Rogov, Peter
Tewhey, Ryan
Isakova, Alina
Deplancke, Bart
Bernstein, Bradley E.
Mikkelsen, Tarjei S.
Lander, Eric S.
Venue: Proceedings of the National Academy of Sciences
Publisher: National Academy of Sciences
Published: 2017-01-30
Volume: 114
Issue: 7
Pages: E1291-E1300
ISSN: 0027-8424 (ISSN-L)
1091-6490
DOI: 10.1073/pnas.1621150114
PMC: PMC5321001
OpenAlex: W2581468399
Cited by: 165
References: 101
Citations by year: 2026: 15 · 2025: 15 · 2024: 9 · 2023: 14 · 2022: 20 · 2021: 21 · 2020: 23 · 2019: 20 · 2018: 18 · 2017: 10
FWCI: 7.93
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: other-oa
www.pnas.org/site/misc/userlicense.xhtml (vor)
Locations: Proceedings of the National Academy of Sciences (publishedVersion, other-oa)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
Funders: HHS | NIH | National Human Genome Research Institute (2U54HG003067-10)
HHS | NIH | National Institute of General Medical Sciences (T32GM007753)
Office of Extramural Research, National Institutes of Health (R01 HG006785)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Peroxisome Proliferator-Activated Receptors
Keywords: enhancer function, transcription factor binding, PPARγ, transcription factor interaction, chromatin accessibility, binding site affinity, massively parallel reporter assay, synthetic enhancers, adipogenesis, gene expression regulation, human genetic variation
MeSH terms: Animals, Binding Sites, Enhancer Elements, Genetic, Gene Expression Regulation, Mutation, Protein Binding, Transcription Factors, Genomics, 3T3-L1 Cells, PPAR gamma, Mice, Nucleotide Motifs

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Systematic dissection of genomic features determining transcription factor binding and enhancer function

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