Dynast: Inclusive and efficient quantification of metabolically labeled transcripts in single cells
Name
Min-kmin-SM-EECS-2022-thesis.pdf
Description
Thesis PDF
Size
4.17 MB
Format
Adobe PDF
Checksum (MD5)
214562cc28ed527f0138de6a2cf15023
Author(s)
Min, Kyung Hoi (Joseph)
Advisor(s)
Weissman, Jonathan S.
Date Issued
September 2022
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
