Type
Text
Type
Dissertation
Advisor
Yang, Jie | Wu, Song | Pameijer, Colette. | Zhu, Wei
Date
2015-08-01
Keywords
Bioinformatics
Department
Department of Applied Mathematics and Statistics.
Language
en_US
Source
This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree.
Identifier
http://hdl.handle.net/11401/76405
Publisher
The Graduate School, Stony Brook University: Stony Brook, NY.
Format
application/pdf
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. | 116 pages
Recommended Citation
Wu, Yijin, "Epigenetic Study with Genome-wide Hypothesis Test and Stepwise Multivariate Adaptive Regression Splines (SMARS)" (2015). Stony Brook Theses and Dissertations Collection, 2006-2020 (closed to submissions). 2328.
https://commons.library.stonybrook.edu/stony-brook-theses-and-dissertations-collection/2328