ArticleCell genomics2025
Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.
Article in Cell genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- VeloRM: disentangling pre- and post-splicing RNA modification dynamics at single-cell resolution.Nucleic acids research · 2026Article
- Sequence determinant and functional relevance of 8-oxoguanine RNA modification unveiled from foundation-model-based predictor.Molecular therapy. Nucleic acids · 2026Article
- mBiomolecules · 2026Review
- The Future of Epigenetics: Emerging Technologies and Clinical Applications.ACS pharmacology & translational science · 2026Review
- Domain-derived knowledge enabled machine learning and functional characterization of cancer-associated RNA methylation sites.BMC bioinformatics · 2026Article
- Epitranscriptomic control of cancer hallmarks: Functions, mechanisms, and therapeutics of RNA modifications.Cancer cell · 2026Review
- Multimodal zero-shot learning of previously unseen epitranscriptomes from RNA-seq data.Briefings in bioinformatics · 2025Article
- Statistical modeling of immunoprecipitation efficiency of MeRIP-seq data enabled accurate detection and quantification of epitranscriptome.Computational and structural biotechnology journal · 2025Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a fundamental mechanism for gene expression regulation, post-transcriptional RNA methylation plays versatile roles in various biological processes and disease mechanisms. Recent advances in single-cell technology have enabled simultaneous profiling of transcriptome-wide RNA methylation in thousands of cells, holding the promise to provide deeper insights into the dynamics, functions, and regulation of RNA methylation. However, it remains a major challenge to determine how to best analyze single-cell epitranscriptomics data. In this study, we developed SigRM, a computational framework for effectively mining single-cell epitranscriptomics datasets with a large cell number, such as those produced by the scDART-seq technique from the SMART-seq2 platform. SigRM not only outperforms state-of-the-art models in RNA methylation site detection on both simulated and real datasets but also provides rigorous quantification metrics of RNA methylation levels. This facilitates various downstream analyses, including trajectory inference and regulatory network reconstruction concerning the dynamics of RNA methylation.
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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.