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洞察力
Xiaojuan Fan1,2, Tiangen Chang3, Chuyun Chen1,4
1Bio-med Big Data Center, CAS Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Nutrition and Health.
bioRxiv : the preprint server for biology
|July 15, 2024
概括
我们开发了一个深度学习模型,以准确预测RNA翻译站点. 该工具揭示了对翻译控制,编码子使用的新见解,并确定了RNA序列中的新编码区域.
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.
- 现有的方法可能无法完全捕捉翻译启动和终止的复杂性.
研究的目的:
- 开发一个深度的神经网络,直接预测和分析从RNA序列的翻译启动和终止站点.
- 在RNA转录中发现新的调节机制和编码元素.
主要方法:
- 使用人类RNA序列的深度神经网络的开发和培训.
- 在人类转录组中预测正典翻译站点.
- 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:
- 在人类转录组中实现了对正典翻译站点的近乎完美的预测.
- 在调节翻译终结方面发现了先前未被认可的编码子使用的作用,并经过实验验证.
- 在mRNA和lncRNA中确定了成千上万个新的开放式读取框架 (ORF),其中一些通过实验证实.
- 证明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:
- 开发的深度学习模型为RNA翻译分析提供了一种高效和通用的方法.
- 这项研究为翻译监管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的流行程度.
- 这些发现凸显了跨多种生物体的翻译控制机制的保存性质.
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