Wu Xiaorong, Wu Xiaoming
School of Computer Science & Engineering, South China University of Technology, Guangzhou 510641, China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2010 Oct;27(5):1178-82.
Advances in cryo-electron microscopy (Cryo-EM) and single-particle reconstruction have led to increasingly high resolutions of macromolecular three-dimensional reconstruction. However, for keeping up the continuing improvements in resolution, it is necessary to increase the number of particles included in performing reconstructions. Manual selection of particles, even assisted by computer, is a bottleneck of single-particle reconstruction. Cryo-EM image has low signal-to-noise ratio and low contrast, which leads to difficulty in particle picking. Various approaches have been developed to address the problem of automatic particle. This paper describes the application of template-based method, edge based method, feature-based method, neural network, DoG-based and simulated annealing approach in particle picking. The characteristics of various approaches are discussed, and the future development is presented.
冷冻电子显微镜（Cryo-EM）和单颗粒重构技术的进步使得大分子三维重构的分辨率越来越高。然而，为了跟上分辨率的持续提升，有必要增加用于重构的颗粒数量。即使借助计算机辅助，手动选择颗粒仍是单颗粒重构的一个瓶颈。冷冻电镜图像的信噪比低且对比度差，这导致颗粒挑选困难。人们已开发出各种方法来解决自动挑选颗粒的问题。本文介绍了基于模板的方法、基于边缘的方法、基于特征的方法、神经网络、基于高斯差分（DoG）的方法以及模拟退火方法在颗粒挑选中的应用。讨论了各种方法的特点，并展望了未来的发展。
