ArticleNature communications2025
Deep learning to decode sites of RNA translation in normal and cancerous tissues.
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 12 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Mapping the functional dark proteome in cancer.Nature reviews. Cancer · 2026Review
- A systematic assessment of single-cell language model configurations.NAR genomics and bioinformatics · 2026Article
- Article
- Microproteins in Human Physiology and Pathology.Biochemistry · 2026Review
- An expanded reference catalog of translated open reading frames for biomedical research.Nucleic acids research · 2026Article
- The 'non-conventional' peptidome: A new layer in plant regulatory mechanisms.Plant communications · 2025Review
- Deep generative optimization of mRNA codon sequences for enhanced mRNA translation and therapeutic efficacy.Nature communications · 2025Article
- Cancer and Aging Biomarkers: Classification, Early Detection Technologies and Emerging Research Trends.Biosensors · 2025Review
- Upstream open reading frame translation enhances immunogenic peptide presentation in mitotically arrested cancer cells.Nature communications · 2025Article
- High-quality peptide evidence for annotating non-canonical open reading frames as human proteins.bioRxiv : the preprint server for biology · 2025Article
- Deep learning to decode sites of RNA translation in normal and cancerous tissues.Nature communications · 2025Article
- Pan-cancer analysis of co-inhibitory molecules revealing their potential prognostic and clinical values in immunotherapy.Frontiers in immunology · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
The biological process of RNA translation is fundamental to cellular life and has wide-ranging implications for human disease. Accurate delineation of RNA translation variation represents a significant challenge due to the complexity of the process and technical limitations. Here, we introduce RiboTIE, a transformer model-based approach designed to enhance the analysis of ribosome profiling data. Unlike existing methods, RiboTIE leverages raw ribosome profiling counts directly to robustly detect translated open reading frames (ORFs) with high precision and sensitivity, evaluated on a diverse set of datasets. We demonstrate that RiboTIE successfully recapitulates known findings and provides novel insights into the regulation of RNA translation in both normal brain and medulloblastoma cancer samples. Our results suggest that RiboTIE is a versatile tool that can significantly improve the accuracy and depth of Ribo-Seq data analysis, thereby advancing our understanding of protein synthesis and its implications in disease.
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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 OpenQuestion graph.