McSweeney Donal M, McSweeney Sean M, Liu Qun
Biology Department, Brookhaven National Laboratory, Upton, NY 11973, USA.
Photon Science, NSLS-II, Brookhaven National Laboratory, Upton, NY 11973, USA.
IUCrJ. 2020 Jun 23;7(Pt 4):719-727. doi: 10.1107/S2052252520007241. eCollection 2020 Jul 1.
High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as , , , and .
高分辨率单颗粒冷冻电镜数据分析依赖于精确的颗粒挑选。为了促进颗粒挑选过程，已开发出一种自监督工作流程。这包括一种迭代策略，该策略使用二维类平均来改进训练颗粒，以及一个用于颗粒挑选的逐步改进的卷积神经网络。为了自动选择颗粒，使用类分布百分比与分辨率的比率作为截止值来定义一个阈值（%/Res）。此工作流程已使用六个具有不同颗粒大小和形状的公开可用数据集进行了测试，并且可以在用户输入最少的情况下自动挑选颗粒。挑选出的颗粒支持3.0 Å或更高分辨率的重建。此工作流程是朝着在颗粒挑选阶段实现自动化单颗粒冷冻电镜数据分析迈出的一步。它可以与常用的单颗粒分析软件包（如 、 、 、 和 ）结合使用。
