ArticleRoyal Society open science2023
Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model.
Article in Royal Society open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Transcriptional Bursting in Pluripotent Stem Cells.Biology · 2026Review
- Highly Constrained Kinetic Models for Single-Cell Gene Expression Analysis.bioRxiv : the preprint server for biology · 2026Article
- Cell trajectory inference based on schrödinger problem and a mechanistic model of stochastic gene expression.NPJ systems biology and applications · 2026Article
- Article
- From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq.PLoS computational biology · 2026Article
- Efficient approximations of transcriptional bursting effects on the dynamics of a gene regulatory network.Journal of the Royal Society, Interface · 2025Article
- Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data.Nucleic acids research · 2025Article
- Incorporating spatial diffusion into models of bursty stochastic transcription.Journal of the Royal Society, Interface · 2025Article
- Quantifying cell fate change under different stochastic gene activation frameworks.Quantitative biology (Beijing, China) · 2025Article
- SLAM-seq reveals independent contributions of RNA processing and stability to gene expression in African trypanosomes.Nucleic acids research · 2025Article
- Deciphering HIV-1 Transcription Initiation and Elongation from Single-Molecule Imaging Data.Research (Washington, D.C.) · 2025Article
- Analysis of a detailed multi-stage model of stochastic gene expression using queueing theory and model reduction.Mathematical biosciences · 2024Article
- Mitigating transcription noise via protein sharing in syncytial cells.Biophysical journal · 2024Article
- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
- Transcriptional bursting dynamics in gene expression.Frontiers in genetics · 2024Review
- Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model.Royal Society open science · 2023Article
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7 authors.
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Abstract
Gene expression has inherent stochasticity resulting from transcription's burst manners. Single-cell snapshot data can be exploited to rigorously infer transcriptional burst kinetics, using mathematical models as blueprints. The classical telegraph model (CTM) has been widely used to explain transcriptional bursting with Markovian assumptions. However, growing evidence suggests that the gene-state dwell times are generally non-exponential, as gene-state switching is a multi-step process in organisms. Therefore, interpretable non-Markovian mathematical models and efficient statistical inference methods are urgently required in investigating transcriptional burst kinetics. We develop an interpretable and tractable model, the generalized telegraph model (GTM), to characterize transcriptional bursting that allows arbitrary dwell-time distributions, rather than exponential distributions, to be incorporated into the ON and OFF switching process. Based on the GTM, we propose an inference method for transcriptional bursting kinetics using an approximate Bayesian computation framework. This method demonstrates an efficient and scalable estimation of burst frequency and burst size on synthetic data. Further, the application of inference to genome-wide data from mouse embryonic fibroblasts reveals that GTM would estimate lower burst frequency and higher burst size than those estimated by CTM. In conclusion, the GTM and the corresponding inference method are effective tools to infer dynamic transcriptional bursting from static single-cell snapshot data.
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