2026-09-21T02:21:13Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/879452022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
David Gifford and Tommi Jaakkola.
Hashimoto, Tatsunori B. (Tatsunori Benjamin)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2014-06-13T22:35:12Z
2014-06-13T22:35:12Z
2014
2014
http://hdl.handle.net/1721.1/87945
880413424
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 41-43).
Here we describe Protein Interaction Quantitation (PIQ), a computational method that models the magnitude and shape of genome-wide DNase profiles to facilitate the identification of transcription factor (TF) binding sites. Through the use of machine learning techniques, PIQ identified binding sites for >700 TFs from one DNase-seq experiment with accuracy comparable to ChIP-seq for motif-associated TFs (median AUC=0.93 across 303 TFs). We applied PIQ to analyze DNase-seq data from mouse embryonic stem cells differentiating into pre-pancreatic and intestinal endoderm. We identified (n=120) and experimentally validated eight 'pioneer' TF families that dynamically open chromatin, enabling other TFs to bind to adjacent DNA. Four pioneer TF families only open chromatin in one direction from their motifs. Furthermore, we identified a class of '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 non-directional pioneer activity shapes the chromatin landscape for population by settler TFs. Substational parts of this thesis are taken from our publication on PIQ currently in press at Nature biotechnology.
by Tatsunori B. Hashimoto.
S.M.
43 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Computation identification of transcription factor binding using DNase-seq
Computation identification of TF binding using DNase-seq
Thesis
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<Abstract>Here we describe Protein Interaction Quantitation (PIQ), a computational method that models the magnitude and shape of genome-wide DNase profiles to facilitate the identification of transcription factor (TF) binding sites. Through the use of machine learning techniques, PIQ identified binding sites for >700 TFs from one DNase-seq experiment with accuracy comparable to ChIP-seq for motif-associated TFs (median AUC=0.93 across 303 TFs). We applied PIQ to analyze DNase-seq data from mouse embryonic stem cells differentiating into pre-pancreatic and intestinal endoderm. We identified (n=120) and experimentally validated eight 'pioneer' TF families that dynamically open chromatin, enabling other TFs to bind to adjacent DNA. Four pioneer TF families only open chromatin in one direction from their motifs. Furthermore, we identified a class of '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 non-directional pioneer activity shapes the chromatin landscape for population by settler TFs. Substational parts of this thesis are taken from our publication on PIQ currently in press at Nature biotechnology.</Abstract>
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