ArticleCommunications biology2025
Gene function revealed at the moment of stochastic gene silencing
Article in Communications biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Stochastic Gene Expression Model with State-Dependent Protein Activation Delay.bioRxiv : the preprint server for biology · 2026Article
- Destabilization of hsa_circ_0015508 by YTHDF2 Enhances miR-496-Mediated FOXN3 Suppression to Drive Nasopharyngeal Carcinoma Progression.Oncology research · 2026Article
- Comparative single-cell analysis of transcriptional bursting reveals the role of genome organization in de novo transcript origination.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Comparative single cell analysis of transcriptional bursting reveals the role of genome organization onbioRxiv : the preprint server for biology · 2025Article
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Authors and funding
2 authors.
Funding
Abstract
Gene expression is a dynamic and stochastic process characterized by transcriptional bursting followed by periods of silence. Single-cell RNA sequencing (scRNA-seq) is a powerful tool to measure transcriptional bursting and silencing at the individual cell level. In this study, we introduce the single-cell Stochastic Gene Silencing (scSGS) method, which leverages the natural variability in single-cell gene expression to decipher gene function. For a target gene g under investigation, scSGS classifies cells into transcriptionally active (g + ) and silenced (g-) samples. It then compares these cell samples to identify differentially expressed genes, referred to as SGS-responsive genes, which are used to infer the function of the target gene g. Analysis of real data demonstrates that scSGS can reveal regulatory relationships up- and downstream of target genes, circumventing the survivorship bias that often affects gene knockout and perturbation studies. scSGS thus offers an efficient approach for gene function prediction, with significant potential to reduce the use of genetically modified animals in gene function research.
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Registered trials
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 Socratic graph.