Cheap Map: Hi-C from ChIP-Seq through Machine Learning
| dc.contributor | Cheung, Margaret S. | |
| dc.contributor.author | Haji Taheri, Arya | |
| dc.date.accessioned | 2019-01-03T17:49:37Z | |
| dc.date.available | 2019-01-03T17:49:37Z | |
| dc.date.issued | 2018-10-18 | |
| dc.description.abstract | In 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.description.department | Honors College | |
| dc.description.department | Physics, Department of | |
| dc.identifier.uri | http://hdl.handle.net/10657/3883 | |
| dc.language.iso | en_US | |
| dc.rights | The author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s). | |
| dc.title | Cheap Map: Hi-C from ChIP-Seq through Machine Learning | |
| dc.type | Poster |