Deep learning to decode sites of RNA translation in normal and cancerous tissues

Jim Clauwaert (), Zahra McVey, Ramneek Gupta, Ian Yannuzzi, Venkatesha Basrur, Alexey I. Nesvizhskii, Gerben Menschaert () and John R. Prensner ()
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Jim Clauwaert: University of Michigan
Zahra McVey: Novo Nordisk Research Centre Oxford, Novo Nordisk Ltd
Ramneek Gupta: Novo Nordisk Research Centre Oxford, Novo Nordisk Ltd
Ian Yannuzzi: Broad Institute of MIT and Harvard
Venkatesha Basrur: University of Michigan
Alexey I. Nesvizhskii: University of Michigan
Gerben Menschaert: Ghent University
John R. Prensner: University of Michigan

Nature Communications, 2025, vol. 16, issue 1, 1-10

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-56543-0

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