Off-campus UNL users: To download campus access dissertations, please use the following link to log into our proxy server with your NU ID and password. When you are done browsing please remember to return to this page and log out.

Non-UNL users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Sequential, Spatial and Functional Disposition of CpG Islands

Dicle Yalcin, University of Nebraska - Lincoln

Abstract

In this dissertation, we characterize and analyze CpG islands (CGIs) using Bioinformatics tools and techniques that lead to an understanding of their biological functions and role in epigenetics and disease mechanisms. We demonstrate the spatial significance of CGIs within the human genome and comparatively, across multiple mammalian genomes. We discuss the spatial characterization of CGIs and introduce a tool (dbCGI), which annotates CGIs based on their colocalization with regulatory regions of genes for multiple species and CGI detection algorithms. We characterize such CGIs under four classes: promoter (pCGI), intragenic (iCGI), gene-terminal (tCGI), and intergenic CGI. Utilizing dbCGI, we present a comparative analysis of CGIs in the human and mouse genomes. We also perform a comprehensive analysis of the spatial CGI frequency distributions across hundreds of biological pathways and gene categories in five mammalian genomes. We assess the level of evolutionary signature conservation of CGI frequencies among the species and highlight pathways or categories governing the same frequency distribution in the majority of the organisms. In the human genome, we analyze spatial CGI distribution patterns across well-studied gene families and perform an enrichment analysis to reveal important distinguishing patterns. We propose an unbiased predictive model that uses a combination of sequential features to identify a genome-wide methylation signature to assess the methylation propensity of CGIs. A successful application of such a signature results in improved accuracies over previous studies and reveals features that contribute to this susceptibility, increasing our understanding of the mechanisms of CGI hypermethylation. Based on this predictive model, we present a proof of concept, which demonstrates the impact of short genetic variations (SGVs) on the methylation propensity of a CGI. Identifying such variants would explain why certain CGIs show a predisposition to methylation in cancer, and advance personalized preventative therapies.

Subject Area

Bioinformatics|Electrical engineering

Recommended Citation

Yalcin, Dicle, "Sequential, Spatial and Functional Disposition of CpG Islands" (2020). ETD collection for University of Nebraska - Lincoln. AAI28259388.
https://digitalcommons.unl.edu/dissertations/AAI28259388

Share

COinS