2026-09-18T20:55:59Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1474752023-01-20T03:46:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Weissman, Jonathan S.
Min, Kyung Hoi (Joseph)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-01-19T19:52:55Z
2023-01-19T19:52:55Z
2022-09
2022-10-19T18:58:19.996Z
https://hdl.handle.net/1721.1/147475
0000-0003-0894-4017
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.
S.M.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells
Thesis
application/pdf
Master
Master of Science in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<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>
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