{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/147475"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/147475","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Min, Kyung Hoi (Joseph)"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science","school":null,"contributors":[],"advisors":["Weissman, Jonathan S."],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-09","date_published":"2022-09","updated_at":"2026-07-22T22:21:15Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright MIT"],"rights_urls":["http://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/147475","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Weissman, Jonathan S."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"]},{"key":"dc:creator","label":"Author","values":["Min, Kyung Hoi (Joseph)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2023-01-19T19:52:55Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2023-01-19T19:52:55Z"]},{"key":"dc:date.issued","label":"Date","values":["2022-09"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master","Master of Science in Electrical Engineering and Computer Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright - Educational Use Permitted","Copyright MIT"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/page/InC-EDU/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1721.1/147475"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells"]}]}],"canonical_facts":{"dc:contributor.advisor":["Weissman, Jonathan S."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science"],"dc:creator":["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: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:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/147475"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","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"],"thesis:degree_name":["Master","Master of Science in Electrical Engineering and Computer Science"]},"updated_at":"2026-07-22T22:21:15Z"}