Structural Variation Discovery and Genotyping from Whole Genome Sequencing: Methodology and Applications: A Dissertation
| dc.contributor.advisor | Zhiping Weng, PhD | |
| dc.contributor.author | Zhuang, Jiali | |
| dc.date | 2022-08-11T08:08:46.000 | |
| dc.date.accessioned | 2022-08-23T16:07:27Z | |
| dc.date.available | 2022-08-23T16:07:27Z | |
| dc.date.issued | 2015-09-15 | |
| dc.date.submitted | 2016-12-06 | |
| dc.identifier.doi | 10.13028/M2Q30P | |
| dc.identifier.uri | http://hdl.handle.net/20.500.14038/32251 | |
| dc.description.abstract | A comprehensive understanding about how genetic variants and mutations contribute to phenotypic variations and alterations entails experimental technologies and analytical methodologies that are able to detect genetic variants/mutations from various biological samples in a timely and accurate manner. High-throughput sequencing technology represents the latest achievement in a series of efforts to facilitate genetic variants discovery and genotyping and promises to transform the way we tackle healthcare and biomedical problems. The tremendous amount of data generated by this new technology, however, needs to be processed and analyzed in an accurate and efficient way in order to fully harness its potential. Structural variation (SV) encompasses a wide range of genetic variations with different sizes and generated by diverse mechanisms. Due to the technical difficulties of reliably detecting SVs, their characterization lags behind that of SNPs and indels. In this dissertation I presented two novel computational methods: one for detecting transposable element (TE) transpositions and the other for detecting SVs in general using a local assembly approach. Both methods are able to pinpoint breakpoint junctions at single-nucleotide resolution and estimate variant allele frequencies in the sample. I also applied those methods to study the impact of TE transpositions on the genomic stability, the inheritance patterns of TE insertions in the population and the molecular mechanisms and potential functional consequences of somatic SVs in cancer genomes. | |
| dc.language.iso | en_US | |
| dc.rights | Copyright is held by the author, with all rights reserved. | |
| dc.subject | Dissertations, UMMS | |
| dc.subject | Genomic Structural Variation | |
| dc.subject | DNA Transposable Elements | |
| dc.subject | Genome-Wide Association Study | |
| dc.subject | Genomic Structural Variation | |
| dc.subject | DNA Transposable Elements | |
| dc.subject | Genome-Wide Association Study | |
| dc.subject | Bioinformatics | |
| dc.subject | Computational Biology | |
| dc.subject | Genomics | |
| dc.title | Structural Variation Discovery and Genotyping from Whole Genome Sequencing: Methodology and Applications: A Dissertation | |
| dc.type | Doctoral Dissertation | |
| dc.identifier.legacyfulltext | https://escholarship.umassmed.edu/cgi/viewcontent.cgi?article=1877&context=gsbs_diss&unstamped=1 | |
| dc.identifier.legacycoverpage | https://escholarship.umassmed.edu/gsbs_diss/875 | |
| dc.legacy.embargo | 2015-09-21T00:00:00-07:00 | |
| dc.identifier.contextkey | 9442106 | |
| refterms.dateFOA | 2022-08-30T03:21:38Z | |
| html.description.abstract | <p>A comprehensive understanding about how genetic variants and mutations contribute to phenotypic variations and alterations entails experimental technologies and analytical methodologies that are able to detect genetic variants/mutations from various biological samples in a timely and accurate manner. High-throughput sequencing technology represents the latest achievement in a series of efforts to facilitate genetic variants discovery and genotyping and promises to transform the way we tackle healthcare and biomedical problems. The tremendous amount of data generated by this new technology, however, needs to be processed and analyzed in an accurate and efficient way in order to fully harness its potential. Structural variation (SV) encompasses a wide range of genetic variations with different sizes and generated by diverse mechanisms. Due to the technical difficulties of reliably detecting SVs, their characterization lags behind that of SNPs and indels. In this dissertation I presented two novel computational methods: one for detecting transposable element (TE) transpositions and the other for detecting SVs in general using a local assembly approach. Both methods are able to pinpoint breakpoint junctions at single-nucleotide resolution and estimate variant allele frequencies in the sample. I also applied those methods to study the impact of TE transpositions on the genomic stability, the inheritance patterns of TE insertions in the population and the molecular mechanisms and potential functional consequences of somatic SVs in cancer genomes.</p> | |
| dc.identifier.submissionpath | gsbs_diss/875 | |
| dc.contributor.department | Biochemistry and Molecular Pharmacology | |
| dc.description.thesisprogram | Bioinformatics and Computational Biology |
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