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dc.contributorCheung, Margaret S.
dc.contributor.authorHaji Taheri, Arya
dc.date.accessioned2019-01-03T17:49:37Z
dc.date.available2019-01-03T17:49:37Z
dc.date.issued2018
dc.identifier.urihttp://hdl.handle.net/10657/3883
dc.description.abstractIn the nucleus of eukaryotic cells, chromatin is organized in specific conformations which depend on cell type; these conformations have been most recently studied through DNA-DNA ligation assays. Obtaining these data about the three-dimensional structure of the chromosome is an expensive and time consuming process. We exploit the idea that epigenetic data determine chromatin architecture by developing a tool that can predict the chromatin contact map purely from epigenetic data without using any structural information. The model has been trained on chromosome 1 and 2 and can quickly predict the high-resolution contact maps (Hi-C) of chromosome 1-22 for the human lymphoblastoid cell line. This project was completed with contributions from Jose Onuchic from the Rice University Chemistry department.
dc.language.isoen_US
dc.titleCheap Map: Hi-C from ChIP-Seq through Machine Learning
dc.typePoster
dc.description.departmentHonors College
dc.description.departmentPhysics, Department of


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