计算机科学
Python(编程语言)
计算科学
算法
曲率
人工智能
绘图
计算机工程
计算机图形学(图像)
数学
几何学
程序设计语言
作者
Jasenko Zivanov,Takanori Nakane,Björn Forsberg,Dari Kimanius,Wim J. H. Hagen,Erik Lindahl,Sjors H. W. Scheres
出处
期刊:eLife
[eLife Sciences Publications Ltd]
日期:2018-11-09
卷期号:7
被引量:5019
摘要
Here, we describe the third major release of RELION. CPU-based vector acceleration has been added in addition to GPU support, which provides flexibility in use of resources and avoids memory limitations. Reference-free autopicking with Laplacian-of-Gaussian filtering and execution of jobs from python allows non-interactive processing during acquisition, including 2D-classification, de novo model generation and 3D-classification. Per-particle refinement of CTF parameters and correction of estimated beam tilt provides higher resolution reconstructions when particles are at different heights in the ice, and/or coma-free alignment has not been optimal. Ewald sphere curvature correction improves resolution for large particles. We illustrate these developments with publicly available data sets: together with a Bayesian approach to beam-induced motion correction it leads to resolution improvements of 0.2–0.7 Å compared to previous RELION versions.
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