ArticleNucleic acids research2025
Analysis of RNA translation with a deep learning architecture provides new insight into translation control.
Article in Nucleic acids research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- MetaTIS: a tool to predict cognate and near-cognate translation initiation sites in human.NAR genomics and bioinformatics · 2026Article
- predNMD: prediction of nonsense-mediated mRNA decay for improved clinical variant pathogenicity classification.bioRxiv : the preprint server for biology · 2026Article
- Microproteins in Human Physiology and Pathology.Biochemistry · 2026Review
- Single-cell profiling reveals pervasive heterogeneity in subcellular RNA localization.bioRxiv : the preprint server for biology · 2026Article
- A foundation language model to decipher diverse regulation of RNAs.Genome biology · 2025Article
- Machine learning-augmented m6A-Seq analysis without a reference genome.Briefings in bioinformatics · 2025Article
- Micropeptides Encoded by Noncoding RNAs: Biological Functions and Roles in Diseases.Research (Washington, D.C.) · 2025Review
- TRANSAID: a hybrid deep learning framework for translation site prediction with integrated biological feature scoring.Frontiers in bioinformatics · 2025Article
- Comprehensive translational profiling and STE AI uncover rapid control of protein biosynthesis during cell stress.Nucleic acids research · 2024Article
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5 authors.
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Abstract
Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict and analyze translation initiation and termination sites from RNA sequences. Trained with human transcripts, our model learned hidden rules of translation control and achieved a near perfect prediction of canonical translation sites across entire human transcriptome. Surprisingly, this model revealed a new role of codon usage in regulating translation termination, which was experimentally validated. We also identified thousands of new open reading frames in mRNAs or lncRNAs, some of which were confirmed experimentally. The model trained with human mRNAs achieved high prediction accuracy of canonical translation sites in all eukaryotes and good prediction in polycistronic transcripts from prokaryotes or RNA viruses, suggesting a high degree of conservation in translation control. Collectively, we present TranslationAI (https://www.biosino.org/TranslationAI/), a general and efficient deep learning model for RNA translation that generates new insights into the complexity of translation regulation.
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