预测哺乳动物细胞中信使RNA的翻译效率
Dinghai Zheng1, Logan Persyn2, Jun Wang1
1mRNA Center of Excellence, Sanofi, Waltham, MA 02451, USA.
bioRxiv : the preprint server for biology
|August 16, 2024
概括
研究人员开发了一种深度学习模型RiboNN,用于从哺乳动物细胞中的mRNA序列中预测mRNA翻译效率 (TE). RiboNN揭示了整个mRNA中控制翻译的关键序列特征,进步了我们对基因表达调节的理解.
科学领域:
- 分子生物学分子生物学
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 在哺乳动物中,mRNA序列的转化控制尚未得到充分理解.
- 现有的模型往往仅限于5' UTR 序列.
研究的目的:
- 建立跨不同哺乳动物细胞类型的翻译效率 (TE) 综合图谱.
- 使用mRNA序列特征开发TE的预测模型.
- 阐明哺乳动物TE的序列决定因素.
主要方法:
- 编制了3,819个核糖体分析数据集,用于转录组全方位的TE图谱 (>140种细胞类型).
- 开发了RiboNN,一个多任务深层卷积神经网络,以及经典的机器学习模型.
- 分析了序列编码的mRNA特征 (5' UTR,CDS,3' UTR) 用于TE预测.
主要成果:
- 实现了最先进的TE预测性能 (r=0.79人类,r=0.78小鼠).
- 确定了5' UTR (~67%),CDS (~31%) 和3' UTR (~2%) 对 TE 的贡献.
- 揭示了包括编码子在内的核酸模式通过反映核糖体过程性和tRNA丰度来决定TE.
结论:
- RiboNN从完整的mRNA序列准确地预测TE,超过了以前的模型.
- 该模型强调了5' UTR和CDS在哺乳动物TE中的重要作用.
- 研究结果提供了对mRNA调节控制,进化和翻译,稳定性和定位的相互作用的见解.
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