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

Simple item page
dc.contributor.advisorSalzberg, Steven Len_US
dc.contributor.advisorPop, Mihaien_US
dc.contributor.authorLangmead, Benjamin Thomasen_US
dc.contributor.departmentComputer Scienceen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.date.accessioned2009-10-06T05:34:53Z
dc.date.available2009-10-06T05:34:53Z
dc.date.issued2009en_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.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/9458
dc.language.isoen_US
dc.subject.pqcontrolledComputer Scienceen_US
dc.subject.pqcontrolledBiology, Bioinformaticsen_US
dc.subject.pqcontrolledBiology, Geneticsen_US
dc.subject.pquncontrolledalignmenten_US
dc.subject.pquncontrolledbioinformaticsen_US
dc.subject.pquncontrolledburrowsen_US
dc.subject.pquncontrolledindexen_US
dc.subject.pquncontrolledwheeleren_US
dc.titleHighly Scalable Short Read Alignment with the Burrows-Wheeler Transform and Cloud Computingen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Langmead_umd_0117N_10426.pdf
Size:
1.93 MB
Format:
Adobe Portable Document Format
Download

Collections

UMD Theses and Dissertations
Computer Science Theses and Dissertations