Single-particle cryoelectron microscopy (cryo-EM) continues to grow into a mainstream structural biology technique. Recent developments in data collection strategies alongside new sample preparation devices herald a future where users will collect multiple datasets per microscope session. To make cryo-EM data processing more automatic and user-friendly, we have developed an automatic pipeline for cryo-EM data preprocessing and assessment using a combination of deep-learning and image-analysis tools. We have verified the performance of this pipeline on a number of datasets and extended its scope to include sample screening by the user-free assessment of the qualities of a series of datasets under different conditions. We propose that our workflow provides a decision-free solution for cryo-EM, making data preprocessing more generalized and robust in the high-throughput era as well as more convenient for users from a range of backgrounds.
High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines.
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作者:Li Yilai, Cash Jennifer N, Tesmer John J G, Cianfrocco Michael A
| 期刊: | Structure | 影响因子: | 4.300 |
| 时间: | 2020 | 起止号: | 2020 Jul 7; 28(7):858-869 |
| doi: | 10.1016/j.str.2020.03.008 | ||
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