Deep learning to decode sites of RNA translation in normal and cancerous tissues.
Deep learning to decode sites of RNA translation in normal and cancerous tissues.
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深度学习解码正常组织和癌组织中的 RNA 翻译位点。
DOI：
10.1101/2024.03.21.586110
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Prensner,JohnR
中科院分区：
文献类型：
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作者：
Clauwaert,Jim;McVey,Zahra;Gupta,Ramneek;Yannuzzi,Ian;Menschaert,Gerben;Prensner,JohnR
RNA 翻译的生物学过程是细胞生命的基础，并且对人类疾病具有广泛的影响。由于过程的复杂性和技术限制，准确描述 RNA 翻译变异是一项重大挑战。在这里，我们介绍 RiboTIE，一种基于转换器模型的方法，旨在增强核糖体分析数据的分析。与现有方法不同，RiboTIE 直接利用原始核糖体分析计数，以高精度和高灵敏度稳健地检测翻译的开放阅读框 (ORF)，并在不同的数据集上进行评估。我们证明 RiboTIE 成功地概括了已知的发现，并为正常脑和髓母细胞瘤癌症样本中 RNA 翻译的调节提供了新的见解。我们的结果表明，RiboTIE 是一种多功能工具，可以显着提高 Ribo-Seq 数据分析的准确性和深度，从而增进我们对蛋白质合成及其对疾病影响的理解。
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.