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Mining and Integrating Epigenomics Big Data to Discover Novel Mechanisms of Gene Regulation


Besides DNA sequences, the genes are regulated by epigenomic mechanisms. Advances in high-throughput sequencing technologies have enabled the generation of huge amount of epigenomic data sets. Those epigenomic big data then requires the application of sophisticated computational approaches and statistical algorithms. My dissertation then focuses on mining and integrating epigenomic big data to inform novel biological mechanisms behind those datasets. The first research project compares the binding patterns of pluripotent regulatory factors, i.e. Oct4, Sox2, Klf4 and c-Myc, between human and mouse in induced pluripotent stem cells. The result suggests the genome-wide regulatory mechanisms are conserved between those two species, but the detailed transcriptional mechanisms are diverged. The second research project analyzes the temporal expression data from embryonic stem cells to cardiomyocytes. The results in this project then identify regulators, including transcription factors and long intergenic non-coding RNAs, which are strongly associated with the cardiogenesis differentiation process. The third research project integrates datasets of DNA methylation and histone modification in 35 human cell types. The result shows histone modifications, especially for H3K4me3, are highly predictable of DNA methylation. As a summary, my dissertation analyzes and integrates epigenomic big data in biological context related with embryonic stem cells and induced pluripotent cells, provides and discoveries novel insights to understand epigenetic regulation of gene expression.

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