Functional characterization of the human genome
| dc.contributor.author | Zhang, Junke | |
| dc.contributor.chair | Yu, Haiyuan | en_US |
| dc.contributor.committeeMember | Feschotte, Cedric | en_US |
| dc.contributor.committeeMember | Hooker, Giles | en_US |
| dc.date.accessioned | 2025-01-14T20:01:25Z | |
| dc.date.issued | 2024-08 | |
| dc.description | 100 pages | en_US |
| dc.description.abstract | In this dissertation, I present my studies on functionally characterizing the human genome through a comprehensive evaluation of massively parallel reporter assays (MPRAs) for identifying human enhancers and prioritizing oncogenic gene fusions using a gene-based permutation test.Enhancers play crucial roles in regulating gene expression, and emerging evidence has revealed the association between genetic variants in enhancers and complex traits and diseases. This highlights the significance of identifying and characterizing enhancers for comprehending disease pathogenesis and developing new therapeutic approaches. The advances in high-throughput sequencing technologies have enabled the quantification of regulatory activities of millions of sequences simultaneously using MPRAs and self-transcribing active regulatory region sequencing (STARR-seq). Through comprehensive evaluation of MPRA/STARR-seq assays, I demonstrate factors affecting assay consistencies. By developing a uniform processing pipeline that addresses those factors, I identify more reliable enhancer regions and improve assay consistencies. The study provides valuable insights into areas for improvement in future applications of MPRA/STARR-seq to better characterize human enhancers. Gene fusions are potential products resulting from structural variants and have been recognized as an important class of somatic alterations in cancer. Previous experimental studies discovered and functionally characterized several oncogenic gene fusions, leading to the development of several drugs targeting gene fusion products. Recent advances in sequencing technologies and bioinformatics have enabled the detection of thousands of gene fusion events in cancer. I conduct a computational study to prioritize potential oncogenic fusions and provide functional characterization in the context of protein interactome networks. I identify a list of candidate driver fusions and map retained and lost protein-protein interactions through gene fusions. This serves as a valuable resource for future studies to understand how protein-interactome network rewiring by fusions can contribute to their oncogenic roles in cancer. | en_US |
| dc.description.embargo | 2025-09-03 | |
| dc.identifier.doi | https://doi.org/10.7298/fs8d-2588 | |
| dc.identifier.other | Zhang_cornellgrad_0058F_14589 | |
| dc.identifier.other | http://dissertations.umi.com/cornellgrad:14589 | |
| dc.identifier.uri | https://hdl.handle.net/1813/116640 | |
| dc.language.iso | en | |
| dc.subject | Enhancer | en_US |
| dc.subject | Gene Fusions | en_US |
| dc.subject | Massively Parallel Reporter Assay | en_US |
| dc.title | Functional characterization of the human genome | en_US |
| dc.type | dissertation or thesis | en_US |
| dcterms.license | https://hdl.handle.net/1813/59810.2 | |
| thesis.degree.discipline | Computational Biology | |
| thesis.degree.grantor | Cornell University | |
| thesis.degree.level | Doctor of Philosophy | |
| thesis.degree.name | Ph. D., Computational Biology |
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