ArticleNature communications2025
Single-molecule direct RNA sequencing reveals the shaping of epitranscriptome across multiple species.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Review
- How Epitranscriptomic Machinery Senses Environmental Cues.Advanced biology · 2026Review
- Single-molecule mbioRxiv : the preprint server for biology · 2026Article
- ModiCal: A Targeted Calibration Workflow for Site-Specific mACS chemical biology · 2026Article
- mBiomolecules · 2026Review
- Nanopore direct RNA sequencing and the epitranscriptome: Advances in mapping native RNA landscapes.iMeta · 2026Review
- RNA N6-methyladenosine (m6A) regulates cell cycle progression in diffuse midline glioma (DMG) and confers sensitivity to FTO inhibition.Cell death & disease · 2026Article
- Nanopore direct RNA sequencing for RNA modification analysis: workflow assessment and computational tool benchmarking.Advanced biotechnology · 2026Article
- Review
- Dynamic modulation of N6-methyladenosine by ionizing radiation in human cells.Biochemistry and biophysics reports · 2026Article
- Systematic evaluation of computational tools for multitype RNA modification detection using nanopore direct RNA sequencing.Nature methods · 2026Article
- NNature reviews. Cancer · 2026Review
- Emerging roles of circular RNAs and enhancer RNAs: new insights into the development and management of neurodegenerative disorders.Biomarker research · 2026Review
- RMPore: a comprehensive database of single-molecule RNA modifications detected by Nanopore direct RNA sequencing.Nucleic acids research · 2026Article
- RNA modifications: roles in immune cell biology and tumor regulation.Cancer cell international · 2025Review
- Comprehensive discovery of mNature communications · 2025Article
- Advances in Detecting RNA Modifications Using Direct RNA Nanopore Sequencing.Advanced genetics (Hoboken, N.J.) · 2025Review
- Review
- Disruption of the mRNA m6A writer complex triggers autoimmunity in Arabidopsis.PLoS genetics · 2025Article
- FTO depletion does not alter mbioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
N6-methyladenosine (m6A) is an essential RNA modification that regulates gene expression and influences diverse cellular processes. Yet, fully characterizing its transcriptome-wide landscape and biogenesis mechanisms remains challenging. Traditional next-generation sequencing (NGS) methods rely on short-reads aggregation, overlooking the inherent heterogeneity of RNA transcripts. Third-generation sequencing (TGS) platforms offer direct RNA sequencing (DRS) at the resolution of individual RNA molecules, enabling simultaneous detection of RNA modifications and RNA processing events. In this study, we introduce SingleMod, a deep learning model tailored for precise detection of m6A modification on individual RNA molecules from DRS data. SingleMod innovatively employs a multiple instance regression (MIR) framework, leveraging extensive methylation-rate labels provided by the quantitative NGS-based method, and achieves ROC AUC and PR AUC of ~0.95 for single-molecule m6A prediction. Applying SingleMod to human cell lines, we systematically dissect the transcriptome-wide m6A landscape at single-molecule and single-base resolution, characterizing m6A heterogeneity in RNA molecules from the same transcript. Through comparative analyzes across eight diverse species, we quantitatively elucidate three distinct m6A distribution patterns correlated with phylogenetic relationships and suggest divergent regulatory mechanisms. This study provides a framework for understanding the shaping of epitranscriptome in a single-molecule perspective.
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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.