Assessing Markovian and Delay Models for Single-Nucleus RNA Sequencing
Abstract
The serial nature of reactions involved in the RNA life-cycle motivates the incorporation of delays in models of transcriptional dynamics. The models couple a transcriptional process to a fairly general set of delayed monomolecular reactions with no feedback. We provide numerical strategies for calculating the RNA copy number distributions induced by these models, and solve several systems with splicing, degradation, and catalysis. An analysis of single-cell and single-nucleus RNA sequencing data using these models reveals that the kinetics of nuclear export do not appear to require invocation of a non-Markovian waiting time.
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Acknowledgement
G.G. thanks Dr. John J. Vastola and Catherine Felce for valuable discussions. The MCMC inference procedure was based on the algorithm developed by Dr. John J. Vastola and Meichen Fang (Gorin et al. 2022). G.G. and L.P. were partially funded by the National Institutes of Health Grants U19MH114830 and 5UM1HG012077-02. S.Y. was supported by the National Science Foundation Graduate Research Fellowship under Grant No. DGE-1745301. A part of the reported results were obtained during a Data Sciences Co-op with Celsius Therapeutics, Inc. The DNA and RNA illustrations are derived from the DNA Twemoji by Twitter, Inc., used under CC-BY 4.0.
Conflict of Interest
The authors declare no conflict of interests.
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Additional details
- ISSN
- 1522-9602
- National Institutes of Health
- U19MH114830
- NIH
- 5UM1HG012077-02
- National Science Foundation
- Graduate Research Fellowship DGE-1745301
- Caltech groups
- Division of Biology and Biological Engineering, Tianqiao and Chrissy Chen Institute for Neuroscience