ArticleNucleic acids research2022
Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Stoichiometry-preserving and stochasticity-aware identification of m6A from direct RNA sequencing.Briefings in bioinformatics · 2026Article
- m6AHD: a new framework for identifying abnormal N6-methyladenosine (m6A) in heart diseases based on sequencing features.Frontiers in genetics · 2026Article
- Direct circMAN1A2(2,3,4,5)-CENPB mRNA interaction regulates cell proliferation and cancer progression.Nature communications · 2025Article
- Multimodal zero-shot learning of previously unseen epitranscriptomes from RNA-seq data.Briefings in bioinformatics · 2025Article
- Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.Cell genomics · 2025Article
- Development of an individualized dementia risk prediction model using deep learning survival analysis incorporating genetic and environmental factors.Alzheimer's research & therapy · 2024Article
- AI techniques have facilitated the understanding of epitranscriptome distribution.Cell genomics · 2024Article
- Domain-knowledge enabled ensemble learning of 5-formylcytosine (f5C) modification sites.Computational and structural biotechnology journal · 2024Article
- [Advances in mapping analysis of ribonucleic acid modifications through sequencing].Se pu = Chinese journal of chromatography · 2024Review
- m6ACali: machine learning-powered calibration for accurate m6A detection in MeRIP-Seq.Nucleic acids research · 2024Article
- Neutrophil extracellular trap-associated risk index for predicting outcomes and response to Wnt signaling inhibitors in triple-negative breast cancer.Scientific reports · 2024Article
- m6A-Atlas v2.0: updated resources for unraveling the N6-methyladenosine (m6A) epitranscriptome among multiple species.Nucleic acids research · 2024Article
- m7GRegpred: substrate prediction of N7-methylguanosine (m7G) writers and readers based on sequencing features.Frontiers in genetics · 2024Article
- Comprehensive Profiling of Transcriptome and m6A Epitranscriptome Uncovers the Neurotoxic Effects of Yunaconitine on HT22 Cells.Evolutionary bioinformatics online · 2024Article
- Nm-Nano: a machine learning framework for transcriptome-wide single-molecule mapping of 2´-O-methylation (Nm) sites in nanopore direct RNA sequencing datasets.RNA biology · 2024Article
- A CNN based m5c RNA methylation predictor.Scientific reports · 2023Article
- Analysis approaches for the identification and prediction ofEpigenetics · 2023Review
- Article
- EMDL-ac4C: identifying N4-acetylcytidine based on ensemble two-branch residual connection DenseNet and attention.Frontiers in genetics · 2023Article
- DPred_3S: identifying dihydrouridine (D) modification on three species epitranscriptome based on multiple sequence-derived features.Frontiers in genetics · 2023Article
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine (m6A) RNA methylation regulates all stages of RNA life in various biological processes and disease mechanisms. Computational methods for deciphering RNA modification have achieved great success in recent years; nevertheless, their potential remains underexploited. One reason for this is that existing models usually consider only the sequence of transcripts, ignoring the various regions (or geography) of transcripts such as 3'UTR and intron, where the epigenetic mark forms and functions. Here, we developed three simple yet powerful encoding schemes for transcripts to capture the submolecular geographic information of RNA, which is largely independent from sequences. We show that m6A prediction models based on geographic information alone can achieve comparable performances to classic sequence-based methods. Importantly, geographic information substantially enhances the accuracy of sequence-based models, enables isoform- and tissue-specific prediction of m6A sites, and improves m6A signal detection from direct RNA sequencing data. The geographic encoding schemes we developed have exhibited strong interpretability, and are applicable to not only m6A but also N1-methyladenosine (m1A), and can serve as a general and effective complement to the widely used sequence encoding schemes in deep learning applications concerning RNA transcripts.
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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 Socratic graph.