Statistical Modeling and Estimation Strategies for Repetitive and Noncoding Nucleic Acid SequencesMcKerrow, Wilson HalescreatorLawrence, CharlesAdvisorReenan, RobertReaderBienenstock, ElieReaderBrown University. Department of Applied Mathematicssponsor2018xi, 94 p.born digitalThesis (Ph. D.)--Brown University, 2018thesesFor most eukaryotic organisms, protein coding sequences are only a small portion of the genome. Much of the genome is made up of transposable elements, regulatory sequence and functional RNA molecules. Modern sequencing technologies show that these noncoding sequences are not merely “junk” DNA, but have important biological function. However the exact role of many of these sequences remains poorly understood. Probabilistic modeling and estimation provides a fruitful method for us to gain insight into this genomic dark matter. In the first part of this thesis, I will describe a model that makes it possible to apply next-generation sequencing data to transposable elements and other repetitive sequence. This method provides accurate predictions of RNA hyper-editing, genomic variation, and TE expression. The final chapter discusses the most informative basepair method, which provides insight into the Boltzmann ensemble of RNA secondary structure.DNA sequencingRNA sequencingTransposonsBioinformaticsSequence alignment (Bioinformatics)RNA--StructureEnglishRPB2018061510.26300/kame-jf78In CopyrightCollection is open for research.dissertations