| dc.identifier.uri | http://hdl.handle.net/11401/76405 | |
| dc.description.sponsorship | This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. | en_US |
| dc.format | Monograph | |
| dc.format.medium | Electronic Resource | en_US |
| dc.language.iso | en_US | |
| dc.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dc.type | Dissertation | |
| dcterms.abstract | Epigenetic gene regulations are essential processes for development and differentiation in both animals and plants. With the advent and rapid advance of sequencing techniques, the high-throughput genome-wide epigenetic modification profiles have been extensively studied in the past few years. In this thesis work, we studied the relationship between gene regulation and two major epigenetic modifications, i.e., DNA methylation and histone modifications. In the DNA methylation analysis, we studied two strains of Arabidopsis grown under different levels of carbon dioxide concentrations (430ppm vs. 810ppm) to simulate the impact of global climate change. The differentially methylated regions were identified by genome-wide hypothesis tests and the potentially impacted genes were located on the genome. We successfully detected the differentially expressed genes that function in plants development. This study illustrated how plants adapted to the environmental stress through epigenetic mechanism. In histone modification analysis, we proposed a data-driven model developed from Multivariate Adaptive Regression Splines (MARS). This step-wise MARS model is able to capture interactions among different chromatin features as well as among genomic loci. Not only can our method outperform existing methods in terms of prediction accuracy, it can also identify potential interactions that could shed light on further study of histone code hypothesis. | |
| dcterms.available | 2017-09-20T16:50:10Z | |
| dcterms.contributor | Yang, Jie | en_US |
| dcterms.contributor | Wu, Song | en_US |
| dcterms.contributor | Pameijer, Colette. | en_US |
| dcterms.contributor | Zhu, Wei | en_US |
| dcterms.creator | Wu, Yijin | |
| dcterms.dateAccepted | 2017-09-20T16:50:10Z | |
| dcterms.dateSubmitted | 2017-09-20T16:50:10Z | |
| dcterms.description | Department of Applied Mathematics and Statistics. | en_US |
| dcterms.extent | 116 pg. | en_US |
| dcterms.format | Application/PDF | en_US |
| dcterms.format | Monograph | |
| dcterms.identifier | http://hdl.handle.net/11401/76405 | |
| dcterms.issued | 2015-08-01 | |
| dcterms.language | en_US | |
| dcterms.provenance | Made available in DSpace on 2017-09-20T16:50:10Z (GMT). No. of bitstreams: 1
Wu_grad.sunysb_0771E_12067.pdf: 1945176 bytes, checksum: f0c490ddb114b15e58a4811c0769c6ef (MD5)
Previous issue date: 2014 | en |
| dcterms.publisher | The Graduate School, Stony Brook University: Stony Brook, NY. | |
| dcterms.subject | Bioinformatics | |
| dcterms.title | Epigenetic Study with Genome-wide Hypothesis Test and Stepwise Multivariate Adaptive Regression Splines (SMARS) | |
| dcterms.type | Dissertation | |
