朝着单颗粒冷冻电镜数据采集自动化的方向发展。
Towards automating single-particle cryo-EM data acquisition.
机构信息
Max Planck Institute for Multidisciplinary Sciences, Department of Molecular Biology, Am Fassberg 11, 37077 Göttingen, Germany.
出版信息
IUCrJ. 2023 Jan 1;10(Pt 1):4-5. doi: 10.1107/S2052252522012039.
Abstract
Target selection for single-particle cryo-EM data acquisition sessions is mostly done manually by human operators, which is time consuming and leads to the inefficient use of instruments. The software toolbox [Kim (2023). , , 90–102] provides solutions for automated target selection for cryo-EM imaging.
摘要
针对单颗粒冷冻电镜数据采集的靶标选择主要由人工操作人员手动完成，这既耗时又导致仪器使用效率低下。软件工具包 [Kim（2023 年），，90-102] 为冷冻电镜成像的自动靶标选择提供了解决方案。
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本文引用的文献
1
Learning to automate cryo-electron microscopy data collection with Ptolemy.
IUCrJ. 2023 Jan 1;10(Pt 1):90-102. doi: 10.1107/S2052252522010612.
2
Fully automated multi-grid cryoEM screening using Smart Leginon.
IUCrJ. 2023 Jan 1;10(Pt 1):77-89. doi: 10.1107/S2052252522010624.
3
Automated systematic evaluation of cryo-EM specimens with SmartScope.
Elife. 2022 Aug 23;11:e80047. doi: 10.7554/eLife.80047.
4
Understanding the invisible hands of sample preparation for cryo-EM.
Nat Methods. 2021 May;18(5):463-471. doi: 10.1038/s41592-021-01130-6. Epub 2021 May 7.
5
Real-time cryo-electron microscopy data preprocessing with Warp.
Nat Methods. 2019 Nov;16(11):1146-1152. doi: 10.1038/s41592-019-0580-y. Epub 2019 Oct 7.
6
Protein denaturation at the air-water interface and how to prevent it.
Elife. 2019 Apr 1;8:e42747. doi: 10.7554/eLife.42747.
7
Collection, pre-processing and on-the-fly analysis of data for high-resolution, single-particle cryo-electron microscopy.
Nat Protoc. 2019 Jan;14(1):100-118. doi: 10.1038/s41596-018-0084-8.
8
Routine single particle CryoEM sample and grid characterization by tomography.
Elife. 2018 May 29;7:e34257. doi: 10.7554/eLife.34257.
9
TEM, user-friendly software for single-particle image processing.
Elife. 2018 Mar 7;7:e35383. doi: 10.7554/eLife.35383.
10
cryoSPARC: algorithms for rapid unsupervised cryo-EM structure determination.
Nat Methods. 2017 Mar;14(3):290-296. doi: 10.1038/nmeth.4169. Epub 2017 Feb 6.
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