High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines
High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines
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DOI：
10.1016/j.str.2020.03.008
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发表时间：
2020-07-07
期刊：
影响因子：
5.7
通讯作者：
Cianfrocco, Michael A.
中科院分区：
文献类型：
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作者：
Li, Yilai;Cash, Jennifer N.;Cianfrocco, Michael A.
单粒子冷冻电子显微镜（cryo-EM）继续发展成为主流的结构生物学技术。数据收集策略的最新发展以及新的样品制备设备预示着未来用户将在每个显微镜会话中收集多个数据集。为了使冷冻EM数据处理更加自动化和用户友好，我们开发了一个自动管道，用于使用深度学习和图像分析工具的组合进行冷冻EM数据预处理和评估。我们已经验证了该管道在一些数据集上的性能，并将其范围扩展到包括在不同条件下对一系列数据集的质量进行无用户评估的样本筛选。我们建议我们的工作流程为cryo-EM提供了一个无决策的解决方案，使数据预处理在高通量时代更加通用和强大，并且更方便来自各种背景的用户。
Single-particle cryoelectron microscopy (cryo-EM) continues to grow into a mainstream structural biology technique. Recent developments in data collection strategies alongside new sample preparation devices herald a future where users will collect multiple datasets per microscope session. To make cryo-EM data processing more automatic and user-friendly, we have developed an automatic pipeline for cryo-EM data preprocessing and assessment using a combination of deep-learning and image-analysis tools. We have verified the performance of this pipeline on a number of datasets and extended its scope to include sample screening by the user-free assessment of the qualities of a series of datasets under different conditions. We propose that our workflow provides a decision-free solution for cryo-EM, making data preprocessing more generalized and robust in the high-throughput era as well as more convenient for users from a range of backgrounds.