Abrishami Vahid, Vargas Javier, Li Xueming, Cheng Yifan, Marabini Roberto, Sorzano Carlos Óscar Sánchez, Carazo José María
Biocomputing Unit, Centro Nacional de Biotecnología-CSIC, C/ Darwin 3, 28049 Madrid, Spain.
School of Life Sciences, Tsinghua University, Beijing 100084, China.
J Struct Biol. 2015 Mar;189(3):163-76. doi: 10.1016/j.jsb.2015.02.001. Epub 2015 Feb 12.
The introduction of direct detection devices in cryo-EM has shown that specimens present beam-induced motion (BIM). Consequently, in this work, we develop a BIM correction method at the image level, resulting in an integrated image in which the in-plane BIM blurring is compensated prior to particle picking. The methodology is based on a robust Optical Flow (OF) approach that can efficiently correct for local movements in a rapid manner. The OF works particularly well if the BIM pattern presents a substantial degree of local movements, which occurs in our data sets for Falcon II data. However, for those cases in which the BIM pattern corresponds to global movements, we have found it advantageous to first run a global motion correction approach and to subsequently apply OF. Additionally, spatial analysis of the Optical Flow allows for quantitative analysis of the BIM pattern. The software that incorporates the new approach is available in XMIPP (http://xmipp.cnb.csic.es).
冷冻电镜中直接检测设备的引入表明,样本存在束流诱导运动(BIM)。因此,在本研究中,我们开发了一种图像层面的BIM校正方法,从而得到一幅整合图像,其中面内BIM模糊在颗粒挑选之前就得到了补偿。该方法基于一种强大的光流(OF)方法,它能够快速有效地校正局部运动。如果BIM模式呈现出相当程度的局部运动,光流方法的效果特别好,在我们针对Falcon II数据的数据集里就是这种情况。然而,对于那些BIM模式对应全局运动的情况,我们发现先运行全局运动校正方法,随后应用光流方法是有利的。此外,光流的空间分析允许对BIM模式进行定量分析。纳入新方法的软件可在XMIPP(http://xmipp.cnb.csic.es)中获取。