Deep learning to decode sites of RNA translation in normal and cancerous tissues
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 introd...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-56543-0 |
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| author | Jim Clauwaert Zahra McVey Ramneek Gupta Ian Yannuzzi Venkatesha Basrur Alexey I. Nesvizhskii Gerben Menschaert John R. Prensner |
| author_facet | Jim Clauwaert Zahra McVey Ramneek Gupta Ian Yannuzzi Venkatesha Basrur Alexey I. Nesvizhskii Gerben Menschaert John R. Prensner |
| author_sort | Jim Clauwaert |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-052ada0fde7740adaeb8cd63e065d973 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-052ada0fde7740adaeb8cd63e065d9732025-02-02T12:32:59ZengNature PortfolioNature Communications2041-17232025-02-0116111010.1038/s41467-025-56543-0Deep learning to decode sites of RNA translation in normal and cancerous tissuesJim Clauwaert0Zahra McVey1Ramneek Gupta2Ian Yannuzzi3Venkatesha Basrur4Alexey I. Nesvizhskii5Gerben Menschaert6John R. Prensner7Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of MichiganNovo Nordisk Research Centre Oxford, Novo Nordisk LtdNovo Nordisk Research Centre Oxford, Novo Nordisk LtdCancer Program, Broad Institute of MIT and HarvardDepartment of Pathology, University of MichiganDepartment of Pathology, University of MichiganDepartment of Data Analysis and Mathematical Modelling, Ghent UniversityDivision of Pediatric Hematology/Oncology, Department of Pediatrics, University of MichiganAbstract 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.https://doi.org/10.1038/s41467-025-56543-0 |
| spellingShingle | Jim Clauwaert Zahra McVey Ramneek Gupta Ian Yannuzzi Venkatesha Basrur Alexey I. Nesvizhskii Gerben Menschaert John R. Prensner Deep learning to decode sites of RNA translation in normal and cancerous tissues Nature Communications |
| title | Deep learning to decode sites of RNA translation in normal and cancerous tissues |
| title_full | Deep learning to decode sites of RNA translation in normal and cancerous tissues |
| title_fullStr | Deep learning to decode sites of RNA translation in normal and cancerous tissues |
| title_full_unstemmed | Deep learning to decode sites of RNA translation in normal and cancerous tissues |
| title_short | Deep learning to decode sites of RNA translation in normal and cancerous tissues |
| title_sort | deep learning to decode sites of rna translation in normal and cancerous tissues |
| url | https://doi.org/10.1038/s41467-025-56543-0 |
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