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David K. Gifford. Reeder, Christopher Campbell Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2014-09-19T21:33:24Z 2014-09-19T21:33:24Z 2014 2014 http://hdl.handle.net/1721.1/89997 890132290 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. Cataloged from PDF version of thesis. Includes bibliographical references (pages 91-98). This thesis presents two computational approaches for identifying chromatin interactions at high spatial resolution from ChIA-PET data. We introduce SPROUT which is a hierarchical probabilistic model that discovers high confidence interactions between binding events that it accurately locates. We apply SPROUT to CTCF ChIA-PET data from mouse embryonic stem cells and demonstrate that SPROUT discovers interactions that are more consistently supported by biological replicates than an alternative method called The ChIA-PET Tool. We also introduce GERM which models genome-wide distributions of protein occupancy without assuming that proteins can be accurately modeled as binding to point locations. We demonstrate that the locations that GERM identifies as interacting with transcription start sites of genes accurately align with ChIP-Seq data that are associated with active enhancers. Finally, we apply GERM to RNA Polymerase II ChIA-PET data from embryonic stem cells and motor neuron progenitors and make several observations about the usage of enhancers during motor neuron development. by Christopher Campbell Reeder. Ph.D. 98 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. Identifying chromatin interactions at high spatial resolution Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="14f80fef-ae13-4220-9086-b4ebb5a3b97f"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Identifying chromatin interactions at high spatial resolution</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2014</PublicationDate> <Authors> <Author> <DisplayName>Reeder, Christopher Campbell</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>This thesis presents two computational approaches for identifying chromatin interactions at high spatial resolution from ChIA-PET data. We introduce SPROUT which is a hierarchical probabilistic model that discovers high confidence interactions between binding events that it accurately locates. We apply SPROUT to CTCF ChIA-PET data from mouse embryonic stem cells and demonstrate that SPROUT discovers interactions that are more consistently supported by biological replicates than an alternative method called The ChIA-PET Tool. We also introduce GERM which models genome-wide distributions of protein occupancy without assuming that proteins can be accurately modeled as binding to point locations. We demonstrate that the locations that GERM identifies as interacting with transcription start sites of genes accurately align with ChIP-Seq data that are associated with active enhancers. Finally, we apply GERM to RNA Polymerase II ChIA-PET data from embryonic stem cells and motor neuron progenitors and make several observations about the usage of enhancers during motor neuron development.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>