Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells
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) da...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/147475 |
| _version_ | 1826196477076045824 |
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| author | Min, Kyung Hoi (Joseph) |
| author2 | Weissman, Jonathan S. |
| author_facet | Weissman, Jonathan S. Min, Kyung Hoi (Joseph) |
| author_sort | Min, Kyung Hoi (Joseph) |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T10:27:09Z |
| format | Thesis |
| id | mit-1721.1/147475 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:27:09Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1474752023-01-20T03:46:36Z Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells Min, Kyung Hoi (Joseph) Weissman, Jonathan S. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science 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. 2023-01-19T19:52:55Z 2023-01-19T19:52:55Z 2022-09 2022-10-19T18:58:19.996Z Thesis https://hdl.handle.net/1721.1/147475 0000-0003-0894-4017 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Min, Kyung Hoi (Joseph) Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells |
| title | Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells |
| title_full | Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells |
| title_fullStr | Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells |
| title_full_unstemmed | Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells |
| title_short | Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells |
| title_sort | dynast inclusive and efficient quantification of metabolically labeled transcripts in single cells |
| url | https://hdl.handle.net/1721.1/147475 |
| work_keys_str_mv | AT minkyunghoijoseph dynastinclusiveandefficientquantificationofmetabolicallylabeledtranscriptsinsinglecells |