ArticleBioinformatics (Oxford, England)2023
Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- Coordinated dysregulation of modular gene activity in human neuropathologies.bioRxiv : the preprint server for biology · 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
- Article
- From noise to models to numbers: Evaluating negative binomial models and parameter estimations in single-cell RNA-seq.PLoS computational biology · 2026Article
- Intrinsic OASL expression governs heterogeneity in interferon induction during influenza A virus infection.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Article
- RoCK and ROI: single-cell transcriptomics with multiplexed enrichment of selected transcripts and region-specific sequencing.Nature communications · 2025Article
- Biophysical constraints on mRNA decay rates shape macroevolutionary divergence in steady-state abundances.bioRxiv : the preprint server for biology · 2025Article
- Single-cell RNA sequencing in osteosarcoma: applications in diagnosis, prognosis, and treatment.Medical oncology (Northwood, London, England) · 2025Review
- Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data.Nature methods · 2025Article
- Scalable inference and identifiability of kinetic parameters for transcriptional bursting from single cell data.Bioinformatics (Oxford, England) · 2025Article
- A conserved coupling of transcriptional ON and OFF periods underlies bursting dynamics.Nature structural & molecular biology · 2025Article
- Cell-cycle dependence of bursty gene expression: insights from fitting mechanistic models to single-cell RNA-seq data.Nucleic acids research · 2025Article
- Identification of molecular determinants of gene-specific bursting patterns by high-throughput imaging screens.Molecular cell · 2025Article
- Batch correcting single-cell spatial transcriptomics count data with Crescendo improves visualization and detection of spatial gene patterns.Genome biology · 2025Article
- Marker genes reveal dynamic features of cell evolving processes.Bioinformatics advances · 2025Article
- Transcriptional bursting: from fundamentals to novel insights.Biochemical Society transactions · 2024Review
- Identification of molecular determinants of gene-specific bursting patterns by high-throughput imaging screens.bioRxiv : the preprint server for biology · 2024Article
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5 authors.
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Abstract
motivationGene expression is characterized by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data are prone to technical variability, including low and variable capture efficiency of transcripts from individual cells.
resultsHere, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and nonallele-specific scRNA-seq data. AVAILABILITY AND IMPLEMENTATION: The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC, respectively.
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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.