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dc.contributor.advisorChairperson, Graduate Committee: Brendan Mumeyen
dc.contributor.authorCleary, Alan Michaelen
dc.description.abstractAs the cost of sequencing DNA continues to drop, the number of sequenced genomes rapidly grows. In the recent past, the cost dropped so low that it is no longer prohibitively expensive to sequence multiple genomes for the same species. This has led to a shift from the single reference genome per species paradigm to the more comprehensive pan-genomics approach, where populations of genomes from one or more species are analyzed together. The total genomic content of a population is vast, requiring algorithms for analysis that are more sophisticated and scalable than existing methods. In this dissertation, we explore new algorithms and their applications to pan-genome analysis, both at the nucleotide and genic resolutions. Specifically, we present the Approximate Frequent Subpaths and Frequented Regions problems as a means of mining syntenic blocks from pan-genomic de Bruijn graphs and provide efficient algorithms for mining these structures. We then explore a variety of analyses that mining synteny blocks from pan-genomic data enables, including meaningful visualization, genome classification, and multidimensional-scaling. We also present a novel interactive data mining tool for pan-genome analysis -- the Genome Context Viewer -- which allows users to explore pan-genomic data distributed across a heterogeneous set of data providers by using gene family annotations as a unit of search and comparison. Using this approach, the tool is able to perform traditionally cumbersome analyses on-demand in a federated manner.en
dc.publisherMontana State University - Bozeman, College of Engineeringen
dc.subject.lcshComputational biologyen
dc.titleComputational pan-genomics: algorithms and applicationsen
dc.rights.holderCopyright 2018 by Alan Michael Clearyen, Graduate Committee: Brittany Fasy; John Sheppard; Binhai Zhu; Travis Wheeler; Indika Kahanda.en School of Computing.en

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