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dc.contributor.advisorWeissman, Jonathan S.
dc.contributor.authorMin, Kyung Hoi (Joseph)
dc.date.accessioned2023-01-19T19:52:55Z
dc.date.available2023-01-19T19:52:55Z
dc.date.issued2022-09
dc.date.submitted2022-10-19T18:58:19.996Z
dc.identifier.urihttps://hdl.handle.net/1721.1/147475
dc.description.abstractSingle cell RNA velocity, defined as the time derivative of gene expression, is a powerful concept that can predict the future transcriptional state of the cell. Traditionally, RNA velocity estimations relied on the distinction between spliced and unspliced mRNA in single cell RNA-seq (scRNA-seq) data, resulting in noisy and biased approximations. Recent advancements in metabolic labeling enabled the direct, unbiased measurement of nascent RNA, yielding significantly improved RNA velocity estimates. However, there is still a lack of a standardized computational framework to process these data. This study introduces Dynast, a pipeline to comprehensively and efficiently quantify metabolically labeled and splicing transcripts from high-throughput metabolic labeling-enabled scRNA-seq.
dc.publisherMassachusetts Institute of Technology
dc.rightsIn Copyright - Educational Use Permitted
dc.rightsCopyright MIT
dc.rights.urihttp://rightsstatements.org/page/InC-EDU/1.0/
dc.titleDynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells
dc.typeThesis
dc.description.degreeS.M.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.orcid0000-0003-0894-4017
mit.thesis.degreeMaster
thesis.degree.nameMaster of Science in Electrical Engineering and Computer Science


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