Incorporating genome annotation in the statistical analysis of genomic and epigenomic tiling array data

Gayla R Olbricht, Purdue University

Abstract

A wealth of information and technologies are currently available for the genome-wide investigation of many types of biological phenomena. Genomic annotation databases provide information about the DNA sequence of a particular organism and give locations of different types of genomic elements, such as the exons and introns of genes. Microarrays are a powerful type of technology that make use of DNA sequence information to investigate different types of biological phenomena on a genome-wide level. Tiling arrays are a unique type of microarray that provide unbiased, high-density coverage of a genomic region, making them well suited for many applications, such as the mapping of transcription and the profiling of epigenetic mechanisms that can occur anywhere in the genome. Epigenetic mechanisms, such as DNA methylation and histone modifications, are important for understanding heritable changes in genome function that cannot be explained by a change in the DNA sequence alone. In this work, statistical approaches for both gene expression and DNA methylation tiling array data are investigated. The proposed methods take advantage of the genomic annotation that are available and that to date have not been effectively utilized in current statistical methods. For gene expression data, an initial bioinformatic step, prior to differential expression analysis, is proposed for the purpose of filtering out probes that are biologically irrelevant. For DNA methylation data, a hidden Markov model, which allows for different transition probabilities between gene and intergenic regions is developed in an effort to improve the predicted locations of DNA methylation across the genome. These methods are investigated through simulation studies and real data analyses.

Degree

Ph.D.

Advisors

Craig, Purdue University.

Subject Area

Statistics

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