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Bonnie Berger. Yorukoglu, Deniz Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 2017-05-11T19:59:29Z 2017-05-11T19:59:29Z 2017 2017 http://hdl.handle.net/1721.1/108991 986521809 Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. Cataloged from PDF version of thesis. Includes bibliographical references (pages 179-189). Massive amounts of next-generation sequencing (NGS) reads generated from sequencing machines around the world have revolutionized biotechnology enabling wide-scale disease and variation studies, personalized medicine and helping us understand our evolutionary history. However, the amount of sequencing data generated every day increases at an exponential rate posing an imminent need for smart algorithmic solutions to handle massive sequencing datasets and efficiently extract the useful knowledge within them. This thesis consists of four research contributions on these two fronts. First, we present a computational framework that leverages the redundancy within large genomic datasets for performing faster read-mapping while improving sensitivity. Second, we describe a lossy compression method for quality scores within sequencing datasets that strikingly improves the downstream accuracy for genotyping. Third, we introduce a Bayesian framework for accurate diploid and polyploid haplotype reconstruction of an individual genome using NGS datasets. Lastly, we extend this haplotype reconstruction framework to high-throughput transcriptome sequencing datasets. by Deniz Yorukoglu. Ph.D. 189 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Electrical Engineering and Computer Science. Scalable methods for storage, processing and analysis of sequencing datasets Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="ea193358-6d87-427d-9a78-e974c82c223f"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Scalable methods for storage, processing and analysis of sequencing datasets</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2017</PublicationDate> <Authors> <Author> <DisplayName>Yorukoglu, Deniz</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Electrical Engineering and Computer Science.</Keyword> <Abstract>Massive amounts of next-generation sequencing (NGS) reads generated from sequencing machines around the world have revolutionized biotechnology enabling wide-scale disease and variation studies, personalized medicine and helping us understand our evolutionary history. However, the amount of sequencing data generated every day increases at an exponential rate posing an imminent need for smart algorithmic solutions to handle massive sequencing datasets and efficiently extract the useful knowledge within them. This thesis consists of four research contributions on these two fronts. First, we present a computational framework that leverages the redundancy within large genomic datasets for performing faster read-mapping while improving sensitivity. Second, we describe a lossy compression method for quality scores within sequencing datasets that strikingly improves the downstream accuracy for genotyping. Third, we introduce a Bayesian framework for accurate diploid and polyploid haplotype reconstruction of an individual genome using NGS datasets. Lastly, we extend this haplotype reconstruction framework to high-throughput transcriptome sequencing datasets.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>