ArticleGenome biology2013
Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data.
Article in Genome biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.
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Who cites it
110 citing papers in PubMed.
- Gillespie-based simulation and inference for non-Markovian stochastic reaction networks.Briefings in bioinformatics · 2026Article
- Deciphering global transcriptional dynamics coordinated by gene-gene regulatory interactions using single-cell data.Molecular systems biology · 2026Article
- Transcriptional Bursting in Pluripotent Stem Cells.Biology · 2026Review
- Cell trajectory inference based on schrödinger problem and a mechanistic model of stochastic gene expression.NPJ systems biology and applications · 2026Article
- Beyond the mean: genetic control of gene expression fidelity and dispersion.bioRxiv : the preprint server for biology · 2026Article
- Article
- From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq.PLoS computational biology · 2026Article
- Assessment of dispersion metrics for estimating single-cell transcriptional variability.PLoS computational biology · 2026Article
- Article
- Gene regulatory network transitions reveal the central transcription factors in lung adenocarcinoma progression.NPJ systems biology and applications · 2026Article
- Interpretable trajectory inference with single-cell linear adaptive negative-binomial expression (scLANE) testing.Nucleic acids research · 2026Article
- AI/ML-Driven Gene Analysis: New Perspectives on Variant Calling in Normal Human Tissue Using scRNA-seq Data.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Scalable inference and identifiability of kinetic parameters for transcriptional bursting from single cell data.Bioinformatics (Oxford, England) · 2025Article
- Multi-layered dosage compensation of the avian Z chromosome by increased transcriptional burst frequency and elevated translational rates.Nature communications · 2025Article
- Interpretable Trajectory Inference with Single Cell Linear Adaptive Negative-binomial Expression (scLANE) Testing.bioRxiv : the preprint server for biology · 2025Article
- scMultiSim: simulation of single-cell multi-omics and spatial data guided by gene regulatory networks and cell-cell interactions.Nature methods · 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
- Deciphering HIV-1 Transcription Initiation and Elongation from Single-Molecule Imaging Data.Research (Washington, D.C.) · 2025Article
- Marker genes reveal dynamic features of cell evolving processes.Bioinformatics advances · 2025Article
50 more citing papers are in PubMed but not listed here.
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundGenetically identical populations of cells grown in the same environmental condition show substantial variability in gene expression profiles. Although single-cell RNA-seq provides an opportunity to explore this phenomenon, statistical methods need to be developed to interpret the variability of gene expression counts.
resultsWe develop a statistical framework for studying the kinetics of stochastic gene expression from single-cell RNA-seq data. By applying our model to a single-cell RNA-seq dataset generated by profiling mouse embryonic stem cells, we find that the inferred kinetic parameters are consistent with RNA polymerase II binding and chromatin modifications. Our results suggest that histone modifications affect transcriptional bursting by modulating both burst size and frequency. Furthermore, we show that our model can be used to identify genes with slow promoter kinetics, which are important for probabilistic differentiation of embryonic stem cells.
conclusionsWe conclude that the proposed statistical model provides a flexible and efficient way to investigate the kinetics of transcription.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.