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.

单粒子冷冻电子显微镜（cryo-EM）继续发展成为主流结构生物学技术。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。为了使冷冻电镜数据处理更加自动化和用户友好，我们结合深度学习和图像分析工具开发了一种用于冷冻电镜数据预处理和评估的自动管道。我们已经在许多数据集上验证了该管道的性能，并将其范围扩展到包括通过在不同条件下对一系列数据集的质量进行无用户评估来进行样本筛选。我们建议，我们的工作流程为冷冻电镜提供了一种无需决策的解决方案，使数据预处理在高通量时代更加通用和稳健，并且对于来自不同背景的用户来说更加方便。