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Highly Scalable Short Read Alignment with the Burrows-Wheeler Transform and Cloud Computing

dc.contributor.advisorSalzberg, Steven Len_US
dc.contributor.advisorPop, Mihaien_US
dc.contributor.authorLangmead, Benjamin Thomasen_US
dc.description.abstractImprovements in DNA sequencing have both broadened its utility and dramatically increased the size of sequencing datasets. Sequencing instruments are now used regularly as sources of high-resolution evidence for genotyping, methylation profiling, DNA-protein interaction mapping, and characterizing gene expression in the human genome and in other species. With existing methods, the computational cost of aligning short reads from the Illumina instrument to a mammalian genome can be very large: on the order of many CPU months for one human genotyping project. This thesis presents a novel application of the Burrows-Wheeler Transform that enables the alignment of short DNA sequences to mammalian genomes at a rate much faster than existing hashtable-based methods. The thesis also presents an extension of the technique that exploits the scalability of Cloud Computing to perform the equivalent of one human genotyping project in hours.en_US
dc.format.extent2021792 bytes
dc.titleHighly Scalable Short Read Alignment with the Burrows-Wheeler Transform and Cloud Computingen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pqcontrolledBiology, Bioinformaticsen_US
dc.subject.pqcontrolledBiology, Geneticsen_US

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