Department of Structural Biochemistry, Max Planck Institute of Molecular Physiology, Otto-Hahn-Strasse 11, 44227 Dortmund, Germany.
Commun Biol. 2019 Jun 19;2:218. doi: 10.1038/s42003-019-0437-z. eCollection 2019.
Selecting particles from digital micrographs is an essential step in single-particle electron cryomicroscopy (cryo-EM). As manual selection of complete datasets-typically comprising thousands of particles-is a tedious and time-consuming process, numerous automatic particle pickers have been developed. However, non-ideal datasets pose a challenge to particle picking. Here we present the particle picking software crYOLO which is based on the deep-learning object detection system You Only Look Once (YOLO). After training the network with 200-2500 particles per dataset it automatically recognizes particles with high recall and precision while reaching a speed of up to five micrographs per second. Further, we present a general crYOLO network able to pick from previously unseen datasets, allowing for completely automated on-the-fly cryo-EM data preprocessing during data acquisition. crYOLO is available as a standalone program under http://sphire.mpg.de/ and is distributed as part of the image processing workflow in SPHIRE.
从数字显微镜照片中选择粒子是单颗粒电子低温显微镜（cryo-EM）的一个基本步骤。由于手动选择完整的数据集（通常包含数千个粒子）是一项繁琐且耗时的过程，因此已经开发了许多自动粒子挑选器。然而，非理想数据集对粒子挑选构成了挑战。在这里，我们介绍了基于深度学习目标检测系统 You Only Look Once（YOLO）的粒子挑选软件 crYOLO。在对每个数据集使用 200-2500 个粒子进行训练后，该网络能够自动识别具有高召回率和准确率的粒子，同时达到每秒五张显微镜照片的速度。此外，我们还提出了一种通用的 crYOLO 网络，能够从以前未见的数据集进行挑选，从而允许在数据采集期间完全自动进行实时 cryo-EM 数据预处理。crYOLO 可作为 standalone 程序在 http://sphire.mpg.de/ 上获得，并作为 SPHIRE 图像处理工作流程的一部分分发。
