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      Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs

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          Abstract

          Cryo-electron microscopy is a popular method for protein structure determination. Identifying a sufficient number of particles for analysis can take months of manual effort. Current computational approaches find many false positives and require significant ad hoc post-processing, especially for unusually-shaped particles. To address these shortcomings, we develop Topaz, an efficient and accurate particle picking pipeline using neural networks trained with a general-purpose positive-unlabeled (PU) learning method. This framework enables particle detection models to be trained with few, sparsely labeled particles and no labeled negatives. Topaz retrieves many more real particles than conventional picking methods while maintaining low false positive rates, is capable of picking challenging unusually-shaped proteins (e.g. small, non-globular, and asymmetric), produces more representative particle sets, and does not require post hoc curation. We demonstrate the performance of Topaz on two difficult datasets and three conventional datasets. Topaz is modular, standalone, free, and open source ( http://topaz.csail.mit.edu)

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          Most cited references28

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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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            Scipion: A software framework toward integration, reproducibility and validation in 3D electron microscopy.

            In the past few years, 3D electron microscopy (3DEM) has undergone a revolution in instrumentation and methodology. One of the central players in this wide-reaching change is the continuous development of image processing software. Here we present Scipion, a software framework for integrating several 3DEM software packages through a workflow-based approach. Scipion allows the execution of reusable, standardized, traceable and reproducible image-processing protocols. These protocols incorporate tools from different programs while providing full interoperability among them. Scipion is an open-source project that can be downloaded from http://scipion.cnb.csic.es.
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              Convolutional Neural Networks for Automated Annotation of Cellular Cryo-Electron Tomograms

              Cellular Electron Cryotomography (CryoET) offers the ability to look inside cells and observe macromolecules frozen in action. A primary challenge for this technique is identifying and extracting the molecular components within the crowded cellular environment. We introduce a method using neural networks to dramatically reduce the time and human effort required for subcellular annotation and feature extraction. Subsequent subtomogram classification and averaging yields in-situ structures of molecular components of interest.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                15 August 2019
                07 October 2019
                November 2019
                07 April 2020
                : 16
                : 11
                : 1153-1160
                Affiliations
                [1 ]Computational and Systems Biology, MIT, Cambridge, MA, USA
                [2 ]Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
                [3 ]Department of Mathematics, MIT, Cambridge, MA, USA
                [4 ]Department of Biochemistry and Molecular Biophysics, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, NY, NY, USA
                [5 ]National Resource for Automated Molecular Microscopy, Simons Electron Microscopy Center, New York Structural Biology Center, NY, NY, USA
                Author notes

                Author contributions

                T.B., A.M., and B.B. conceived of this project. T.B. developed the PU learning methods and implemented Topaz, processed and analyzed single particle datasets, and carried out the computational experiments, under the guidance of B.B. M.R. prepared and collected the Toll receptor dataset. J.B. prepared and collected the clustered protocadherin dataset. A.J.N. analyzed the single particle cryoEM reconstructions. A.J.N. developed the Topaz GUI based on VIA. T.B., A.M., M.R., J.B., L.S., A.J.N., and B.B. designed the experiments. T.B., M.R., A.J.N., and B.B. wrote the manuscript.

                [* ]Corresponding authors: bab@ 123456mit.edu and anoble@ 123456nysbc.org
                Article
                NIHMS1537546
                10.1038/s41592-019-0575-8
                6858545
                31591578
                ef2f10e7-f31d-4e91-939d-bf53a20ad158

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                Life sciences
                Life sciences

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