dc.contributor.advisor | Weissman, Jonathan S. | |
dc.contributor.author | Min, Kyung Hoi (Joseph) | |
dc.date.accessioned | 2023-01-19T19:52:55Z | |
dc.date.available | 2023-01-19T19:52:55Z | |
dc.date.issued | 2022-09 | |
dc.date.submitted | 2022-10-19T18:58:19.996Z | |
dc.identifier.uri | https://hdl.handle.net/1721.1/147475 | |
dc.description.abstract | Single 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.publisher | Massachusetts Institute of Technology | |
dc.rights | In Copyright - Educational Use Permitted | |
dc.rights | Copyright MIT | |
dc.rights.uri | http://rightsstatements.org/page/InC-EDU/1.0/ | |
dc.title | Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells | |
dc.type | Thesis | |
dc.description.degree | S.M. | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
dc.identifier.orcid | 0000-0003-0894-4017 | |
mit.thesis.degree | Master | |
thesis.degree.name | Master of Science in Electrical Engineering and Computer Science | |