Reduced Representations for Efficient Analysis of Genomic Data; from Microarray to High-throughput Sequencing

OF THE DISSERTATION Reduced Representations for Efficient Analysis of Genomic Data; From Microarray to High-throughput Sequencing by Md Pavel Mahmud Dissertation Director: Prof. Alexander Schliep Since the genomics era has started in the ’70s, microarray technologies have been extensively used for biological applications such as gene expression profiling, copy number variation (CNV) or Single Neucleotide Polymorphism (SNP) detection. To analyze microarray data, numerous statistical and algorithmic techniques have been developed over the last two decades; specially, for detecting CNV from array comparative genomic hybridization (arrayCGH) data, Hidden Markov Models (HMMs) have been successfully used. Still, due to computational reasons, the benefits of using Bayesian HMMs have been overlooked, and their use has been, at best, minimal in practice. The large demand for computational resources has also affected the analysis of high throughput sequencing (HTS) data, which, over the last few years, has started to revolutionize the field of computational biology. For example, the most sensitive tools for mapping HTS data to reference genomes are generally ignored in favor of fast, less accurate ones. In this dissertation, we strive for reduced representations of biological data which enable us to perform efficient computations on large datasets. Since biological datasets often contain repetitive, sometimes redundant, elements, it is a natural idea to identify groups of similar elements and directly perform computations on these groups. Usually,