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  4. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape

Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape

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Author(s)
Lewis, Sophia
•
van Hoff, John Peter
•
Karun, Vivek
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Hashimoto, Tatsunori Benjamin
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Sherwood, Richard I.
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O'Donnell, Charles William
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Barkal, Amira A.
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Jaakkola, Tommi S.
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Gifford, David K.
Date Issued
January 2014
Journal
Nature Biotechnology
Publisher
Nature Publishing Group
Citation
Sherwood, Richard I, Tatsunori Hashimoto, Charles W O’Donnell, Sophia Lewis, Amira A Barkal, John Peter van Hoff, Vivek Karun, Tommi Jaakkola, and David K Gifford. “Discovery of Directional and Nondirectional Pioneer Transcription Factors by Modeling DNase Profile Magnitude and Shape.” Nature Biotechnology 32, no. 2 (January 19, 2014): 171–178.
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Author's final manuscript
Abstract
We describe protein interaction quantitation (PIQ), a computational method for modeling the magnitude and shape of genome-wide DNase I hypersensitivity profiles to identify transcription factor (TF) binding sites. Through the use of machine-learning techniques, PIQ identified binding sites for >700 TFs from one DNase I hypersensitivity analysis followed by sequencing (DNase-seq) experiment with accuracy comparable to that of chromatin immunoprecipitation followed by sequencing (ChIP-seq). We applied PIQ to analyze DNase-seq data from mouse embryonic stem cells differentiating into prepancreatic and intestinal endoderm. We identified 120 and experimentally validated eight 'pioneer' TF families that dynamically open chromatin. Four pioneer TF families only opened chromatin in one direction from their motifs. Furthermore, we identified 'settler' TFs whose genomic binding is principally governed by proximity to open chromatin. Our results support a model of hierarchical TF binding in which directional and nondirectional pioneer activity shapes the chromatin landscape for population by settler TFs.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
http://creativecommons.org/licenses/by-nc-sa/4.0/
Persistent DSpace Link
http://hdl.handle.net/1721.1/100228
DOI of Published Version
https://doi.org/10.1038/nbt.2798
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