Tracking Dissemination of Plasmids in the Murine Gut Using Hi-C Sequencing and Bayesian Landmark-based Shape Analysis of Tumor Pathology Images

dc.contributor.advisorZhang, Michael Qiwei
dc.contributor.advisorChen, Min
dc.contributor.advisorYang, Duck Joo
dc.contributor.committeeMemberPalmer, Kelli
dc.contributor.committeeMemberLi, Qiwei
dc.contributor.committeeMemberShin, Sunyoung
dc.creatorZhang, Cong
dc.date.accessioned2023-03-29T16:02:50Z
dc.date.available2023-03-29T16:02:50Z
dc.date.created2020-12
dc.date.issued2020-12-01T06:00:00.000Z
dc.date.submittedDecember 2020
dc.date.updated2023-03-29T16:02:51Z
dc.description.abstractStarting from the experimental design and simple group result comparison using studentst test to the analysis need of explosively growing digital information in the big data era, extensive statistical approaches have been developed and incorporated into biology studies in order to understand the mechanism of life processes and disease. The term ”omics” refers to the various disciplines in biology performing a comprehensive, or global, assessment of a set of biological features in a high-throughput way, such as genomics, transcriptomics, proteomics and metagenomics. When analyzing such a huge amount of data, a proper framework needs to be developed with a thorough knowledge of the associated biology as well as statistical models. In this work, I focus on two critical health-related problems, antibiotic resistance spread in microbial communities by conjugative plasmids, and the association between tumor shape and prognosis in pathology images. In my first work, metagenomics and Hi-C sequencing were employed to analyze plasmid dissemination from Enterococcus faecalis donor strains in the murine intestine. I clustered assembled contigs into metagenomeassembled genomes (MAGs) and showed that the quality of obtained MAGs was improved by combining those two types of sequencing techniques. Then, I demonstrated that Hi-C is able to detect the in situ hosts of native resistance genes in the murine gut microbiota. We also confirmed the association between introduced E. faecalis plasmids and the donor strains and found potential new gram-positive host for the pAM830 resistance plasmid. In my second work, we developed a framework with a novel automatic landmark detection model for tumor shape boundary in pathology image called Bayesian LAndmark-based Shape Analysis (BayesLASA). Two types of landmark-based boundary roughness features were proposed, and we demonstrated the predictive value of them in a large cohort of lung cancer patients.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/10735.1/9652
dc.language.isoen
dc.subjectStatistics
dc.subjectBiology, Bioinformatics
dc.titleTracking Dissemination of Plasmids in the Murine Gut Using Hi-C Sequencing and Bayesian Landmark-based Shape Analysis of Tumor Pathology Images
dc.typeThesis
dc.type.materialtext
thesis.degree.collegeSchool of Natural Sciences and Mathematics
thesis.degree.departmentStatistics
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.namePHD

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