ArticleScientific reports2018
Accurate identification of RNA editing sites from primitive sequence with deep neural networks.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Structure-aware graph learning predicts RNA editability across tissues and species.Research square · 2026Article
- ADAR-GPT: Learning the language of the epitranscriptome.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- A Global Assessment of the Transcription-Dependent Single Nucleotide Variants Relies on the Characteristics of RNA-Sequencing Technologies.Biomolecules · 2026Article
- ADAR-GPT: A continually fine-tuned language model for predicting A-to-I RNA editing sites.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- REDInet: a temporal convolutional network-based classifier for A-to-I RNA editing detection harnessing million known events.Briefings in bioinformatics · 2025Article
- ncRNA Editing: Functional Characterization and Computational Resources.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Bioinformatics for Inosine: Tools and Approaches to Trace This Elusive RNA Modification.Genes · 2024Review
- Harnessing ADAR-Mediated Site-Specific RNA Editing in Immune-Related Disease: Prediction and Therapeutic Implications.International journal of molecular sciences · 2023Review
- Benchmarking RNA Editing Detection Tools.Biotech (Basel (Switzerland)) · 2023Review
- ATTIC is an integrated approach for predicting A-to-I RNA editing sites in three species.Briefings in bioinformatics · 2023Article
- RDDSVM: accurate prediction of A-to-I RNA editing sites from sequence using support vector machines.Functional & integrative genomics · 2021Article
- Article
- The epitranscriptome beyond mNature reviews. Genetics · 2021Review
- Chemical Profiling of A-to-I RNA Editing Using a Click-Compatible Phenylacrylamide.Chemistry (Weinheim an der Bergstrasse, Germany) · 2020Article
- Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal.Nature protocols · 2020Article
- A-to-I RNA Editing in Cancer: From Evaluating the Editing Level to Exploring the Editing Effects.Frontiers in oncology · 2020Review
- RNA editing in the forefront of epitranscriptomics and human health.Journal of translational medicine · 2019Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA editing is a post-transcriptional RNA sequence alteration. Current methods have identified editing sites and facilitated research but require sufficient genomic annotations and prior-knowledge-based filtering steps, resulting in a cumbersome, time-consuming identification process. Moreover, these methods have limited generalizability and applicability in species with insufficient genomic annotations or in conditions of limited prior knowledge. We developed DeepRed, a deep learning-based method that identifies RNA editing from primitive RNA sequences without prior-knowledge-based filtering steps or genomic annotations. DeepRed achieved 98.1% and 97.9% area under the curve (AUC) in training and test sets, respectively. We further validated DeepRed using experimentally verified U87 cell RNA-seq data, achieving 97.9% positive predictive value (PPV). We demonstrated that DeepRed offers better prediction accuracy and computational efficiency than current methods with large-scale, mass RNA-seq data. We used DeepRed to assess the impact of multiple factors on editing identification with RNA-seq data from the Association of Biomolecular Resource Facilities and Sequencing Quality Control projects. We explored developmental RNA editing pattern changes during human early embryogenesis and evolutionary patterns in Drosophila species and the primate lineage using DeepRed. Our work illustrates DeepRed's state-of-the-art performance; it may decipher the hidden principles behind RNA editing, making editing detection convenient and effective.
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Registered trials
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