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      Real-time cryo–EM data pre-processing with Warp

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      Nature methods

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          Abstract

          The acquisition of cryo-electron microscopy (cryo-EM) data from biological specimen must be tightly coupled to data pre-processing to ensure best data quality and microscope usage. Here we provide Warp, a software for real-time evaluation and pre-processing of cryo-EM data during their acquisition. Warp corrects micrographs for global and local motion, estimates the local defocus with the use of novel algorithms, and monitors key parameters for each recorded micrograph or tomographic tilt series in real time. The software further includes deep learning-based models for accurate particle picking and image denoising. The output from Warp can be fed into established programs for particle classification and 3D map refinement. Our benchmarks show improvement in the nominal resolution from 3.9 Å to 3.2 Å through fully automated processing of a published cryo-EM data set for influenza virus hemagglutinin. Warp is easy to install, computationally inexpensive, and has an intuitive and streamlined user interface.

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          Most cited references40

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          SPHIRE-crYOLO is a fast and accurate fully automated particle picker for cryo-EM

          Selecting particles from digital micrographs is an essential step in single-particle electron cryomicroscopy (cryo-EM). As manual selection of complete datasets—typically comprising thousands of particles—is a tedious and time-consuming process, numerous automatic particle pickers have been developed. However, non-ideal datasets pose a challenge to particle picking. Here we present the particle picking software crYOLO which is based on the deep-learning object detection system You Only Look Once (YOLO). After training the network with 200–2500 particles per dataset it automatically recognizes particles with high recall and precision while reaching a speed of up to five micrographs per second. Further, we present a general crYOLO network able to pick from previously unseen datasets, allowing for completely automated on-the-fly cryo-EM data preprocessing during data acquisition. crYOLO is available as a standalone program under http://sphire.mpg.de/ and is distributed as part of the image processing workflow in SPHIRE.
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            Imagenet classification with deep convolutional neural networks

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              Updating quasi-Newton matrices with limited storage

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                Author and article information

                Journal
                101215604
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                22 August 2019
                07 October 2019
                November 2019
                07 April 2020
                : 16
                : 11
                : 1146-1152
                Affiliations
                [1 ]Max Planck Institute for Biophysical Chemistry, Department of Molecular Biology, Am Fassberg 11, 37077 Göttingen, Germany
                Author notes
                Correspondence should be addressed to D.T. ( dteguno@ 123456mpibpc.mpg.de ) and P.C. ( patrick.cramer@ 123456mpibpc.mpg.de ).
                Article
                EMS84178
                10.1038/s41592-019-0580-y
                6858868
                31591575
                51a7fba7-500f-46a2-8833-2aad76ff3710

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                Life sciences
                Life sciences

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