Analysis of Ribosome Stalling and Translation Elongation Dynamics by Deep Learning
Analysis of Ribosome Stalling and Translation Elongation Dynamics by Deep Learning
复制标题
通过深度学习分析核糖体停滞和翻译延伸动力学
DOI：
10.1016/j.cels.2017.08.004
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发表时间：
2017
期刊：
影响因子：
9.3
通讯作者：
Zeng JY
中科院分区：
文献类型：
--
作者：
Zhang Sai;He Xuan;Zeng Jianyang;Hu Hailin;Zhou Jingtian;Jiang Tao;Jiang Tao;Jiang Tao;Jiang Tao;Zeng JY
核糖体停滞表现为核糖体在mRNAs特定密码子位置的局部聚集。在这里，我们介绍了ROSE，一个深度学习框架，用于分析高通量核糖体图谱数据，并估计在每个基因组位置发生核糖体停滞事件的概率。对独立数据的广泛验证测试表明，ROSE模型比传统预测模型具有更高的预测精度，接收器工作特性曲线下面积增加高达18.4%。此外，全基因组的统计分析表明，ROSE预测可以与核糖体停滞的各种假定调节因素很好地相关。此外，ROSE计算的人类和酵母全基因组核糖体停滞景观恢复了蛋白质生物发生中核糖体停滞和共翻译事件之间的功能相互作用，包括信号识别颗粒对蛋白质的靶向和蛋白质二级结构的形成。总之，我们的研究提供了一种新的方法来补充核糖体图谱技术，并进一步破译在mRNA序列中编码的翻译延伸动态背后的复杂调控机制。
Ribosome stalling is manifested by the local accumulation of ribosomes at specific codon positions of mRNAs. Here, we present ROSE, a deep learning framework to analyze high-throughput ribosome profiling data and estimate the probability of a ribosome stalling event occurring at each genomic location. Extensive validation tests on independent data demonstrated that ROSE possessed higher prediction accuracy than conventional prediction models, with an increase in the area under the receiver operating characteristic curve by up to 18.4%. In addition, genome-wide statistical analyses showed that ROSE predictions can be well correlated with diverse putative regulatory factors of ribosome stalling. Moreover, the genome-wide ribosome stalling landscapes of both human and yeast computed by ROSE recovered the functional interplays between ribosome stalling and cotranslational events in protein biogenesis, including protein targeting by the signal recognition particles and protein secondary structure formation. Overall, our study provides a novel method to complement the ribosome profiling techniques and further decipher the complex regulatory mechanisms underlying translation elongation dynamics encoded in the mRNA sequence.