Literature DB >> 32294468

High-Throughput Cryo-EM Enabled by User-Free Preprocessing Routines.

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.
Copyright © 2020 Elsevier Ltd. All rights reserved.

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


  29 in total

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