使用深度学习架构对RNA翻译的分析为翻译控制提供了新的见解
Xiaojuan Fan1,2, Tiangen Chang3, Chuyun Chen1
1Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Nucleic acids research
|April 12, 2025
概括
一个新的深度学习模型,TranslationAI,准确地预测RNA翻译站点,并揭示了代码子使用情况.
科学领域:
- 分子生物学分子生物学
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 在RNA中对编码区域的准确注释对于理解基因翻译至关重要.
- 目前用于识别翻译网站的方法在范围和准确性方面可能受到限制.
研究的目的:
- 开发一种深度学习模型,用于从RNA序列中直接预测和分析翻译启动和终止位点.
- 揭示RNA翻译中的新型调节机制,并确定新的开放阅读框架.
主要方法:
- 开发一个深度神经网络 (TranslationAI),以人体成绩单进行训练.
- 该模型的应用用于预测人类转录组中的正典翻译站点.
- 新发现的实验验证,包括在翻译终止和新开放阅读框架的识别中使用编码的作用.
主要成果:
- 深度学习模型在人类转录组中的正规翻译站点实现了近乎完美的预测准确性.
- 在调节翻译终结方面,编码子使用的新型作用被发现并经过实验验证.
- 在mRNA和lncRNA中发现了数千个新的开放式读取 (ORF),其中一些经过实验证实.
- 该模型显示了对真核细胞翻译部位的高预测准确性和对 prokaryotic 和病毒转录的良好表现,表明保存的翻译控制机制.
结论:
- 翻译AI是用于RNA翻译分析的高效深度学习工具.
- 这项研究为翻译调节的复杂性提供了新的见解,包括编码子使用的作用和新型ORF的发现.
- 这些发现突出了跨多种生物体的翻译控制的保存性质.
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