| Literature DB >> 32294468 |
Yilai Li1, Jennifer N Cash1, John J G Tesmer2, Michael A Cianfrocco3.
Abstract
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.Entities:
Keywords: automatic; cryo-EM; deep learning;; pipeline
Year: 2020 PMID: 32294468 PMCID: PMC7347462 DOI: 10.1016/j.str.2020.03.008
Source DB: PubMed Journal: Structure ISSN: 0969-2126 Impact factor: 5.006