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Applications of deep learning in electron microscopy
2022
Microscopy
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We review the growing use of machine learning in electron microscopy (EM) driven in part by the availability of fast detectors operating at kiloHertz frame rates leading to large data sets that cannot ...
We then provide a review of the application of these in both physical and life sciences, highlighting how conventional networks and training data have been specifically modified for EM. ...
[53] have used a transfer learning approach applied on an AlexNet-based network architecture to transfer manually labelled images to the entire datastore of the National Center for Electron Microscopy ...
doi:10.1093/jmicro/dfab043
pmid:35275181
fatcat:3rncxrklt5atratggpunhdxsam
A flexible framework for multi-particle refinement in cryo-electron tomography
2021
PLoS Biology
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Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) are increasingly used for macromolecular structure determination in situ. ...
The guide is hosted on https://teamtomo.org/, a collaborative online platform we establish for sharing knowledge about cryo-ET. ...
Acknowledgments We acknowledge Diamond Light Source for access and support of the cryo-EM facilities at the UK's national Electron Bio-imaging Centre (eBIC), funded by the Wellcome Trust, MRC, and BBRSC ...
doi:10.1371/journal.pbio.3001319
pmid:34437530
pmcid:PMC8389456
fatcat:u7zkacubvbe7taqyvxqq7g6kh4
Single-particle cryo-electron microscopy: Mathematical theory, computational challenges, and opportunities
[article]
2019
arXiv
pre-print
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In recent years, an abundance of new molecular structures have been elucidated using cryo-electron microscopy (cryo-EM), largely due to advances in hardware technology and data processing techniques. ...
Based on these abstractions, we discuss some recent intriguing results in the mathematical theory of cryo-EM, and delineate relations with group theory, invariant theory, and information theory. ...
ACKNOWLEDGMENT We thank the anonymous reviewers and the editor for their valuable comments and suggestions. ...
arXiv:1908.00574v2
fatcat:lwbbis37e5ctjngflw3opjl4sy
CryoDRGN: Reconstruction of heterogeneous structures from cryo-electron micrographs using neural networks
[article]
2020
bioRxiv
pre-print
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Cryo-EM single-particle analysis has proven powerful in determining the structures of rigid macromolecules. ...
However, many protein complexes are flexible and can change conformation and composition as a result of functionally-associated dynamics. ...
Principles of cryo-EM single-particle image processing. Microscopy (Oxf) 65, 57-67 (2016). 13. Scheres, S. H. W. et al. Maximum-likelihood Multi-reference Refinement for Electron Microscopy Images. ...
doi:10.1101/2020.03.27.003871
fatcat:dhm65gstofg43feb64iymgkpgi
TomoTwin: Generalized 3D Localization of Macromolecules in Cryo-electron Tomograms with Structural Data Mining
[article]
2022
bioRxiv
pre-print
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To assist in this crucial particle picking step, we present TomoTwin: a robust, first in class general picking model for cryo-electron tomograms based on deep metric learning. ...
Detailed analysis of macromolecules through subtomogram averaging requires particles to first be localized within the tomogram volume, a task complicated by several factors including a low signal to noise ...
To assist in this crucial particle picking step, we developed TomoTwin, a robust, first in class general picking model for cryo-electron tomograms based on deep metric learning. ...
doi:10.1101/2022.06.24.497279
fatcat:e55uck2u3bewxer2uxg3jgvhxa
Nanofluidic chips for cryo-EM structure determination from picoliter sample volumes
[article]
2021
bioRxiv
pre-print
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Cryogenic electron microscopy has become an essential tool for structure determination of biological macromolecules. ...
In practice, the difficulty to reliably prepare samples with uniform ice thickness still represents a barrier for routine high-resolution imaging and limits the current throughput of the technique. ...
our cryo-EM facility. ...
doi:10.1101/2021.05.25.444805
fatcat:l4zmr5epzzgetprfgwf4ohepeu
Tools enabling flexible approaches to high-resolution subtomogram averaging
[article]
2021
bioRxiv
pre-print
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Cryo-electron tomography and subtomogram averaging are increasingly used for macromolecular structure determination in situ. ...
Additionally, we establish an open and collaborative online platform for sharing knowledge and tools related to cryo-electron tomography data processing. ...
We acknowledge Diamond Light Source for access and support of the cryo-EM facilities at the UK's national Electron Bio-imaging Centre (eBIC), funded by the Wellcome Trust, MRC and BBRSC. ...
doi:10.1101/2021.01.31.428990
fatcat:kzo2iv3xjjdstbuwjqugnf4o34
Towards unsupervised classification of macromolecular complexes in cryo electron tomography: challenges and opportunities
[article]
2022
bioRxiv
pre-print
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Methods: We propose an unsupervised sub-tomogram classification method based on transfer learning. ...
In this paper, we provide an overview of unsupervised deep learning techniques, discuss the challenges to analyze cryo-ET data, and provide a proof-of-concept on real data. ...
Baumesiter for helping design research. We thank A. Martinez for his critical feedback and valuable suggestions about machine learning with synthetic datasets in cryo-ET. ...
doi:10.1101/2022.03.10.483789
fatcat:y6kxovji5vc4jmriqcpm6ssyia
Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks
[article]
2021
arXiv
pre-print
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2019 pre-print arXiv:1911.08121v1 fatcat:sj7o2tgmcraytoningj64ukssy
Cryo-electron microscopy (cryo-EM) is capable of producing reconstructed 3D images of biomolecules at near-atomic resolution. ...
In this paper, we combine variational autoencoders (VAEs) and generative adversarial networks (GANs) to learn a low-dimensional latent representation of cryo-EM images. ...
Acknowledgments We thank Khanh Dao Duc and TJ Lane for very helpful comments on an earlier draft of the manuscript, and Daniel Ratner and Cornelius Gati for stimulating discussions and support throughout ...
arXiv:1911.08121v2
fatcat:l2snznfj6bgoznvkbotzyx4c6y
Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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Cryo-electron microscopy (cryo-EM) is capable of producing reconstructed 3D images of biomolecules at nearatomic resolution. ...
In this paper, we combine variational autoencoders (VAEs) and generative adversarial networks (GANs) to learn a low-dimensional latent representation of cryo-EM images. ...
Introduction Cryo-electron microscopy (cryo-EM) is one of the most promising imaging techniques in biology, as it produces 3D reconstructions of biomolecules at near-atomic resolution. ...
doi:10.1109/cvprw50498.2020.00493
dblp:conf/cvpr/MiolanePLH20
fatcat:zewmzsbe7bgvjb3kyzz4wgqid4
Nanofluidic chips for cryo-EM structure determination from picoliter sample volumes
2022
eLife
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The file type is text/html.
Cryogenic electron microscopy has become an essential tool for structure determination of biological macromolecules. ...
In practice, the difficulty to reliably prepare samples with uniform ice thickness still represents a barrier for routine high-resolution imaging and limits the current throughput of the technique. ...
Electron Microscopy Public Image ArchiveID EMPIAR-10200. New tools for automated high-resolution cryo-EM structure determination in RELION-3. Electron Microscopy Public Image ArchiveID EMPIAR-10272. ...
doi:10.7554/elife.72629
pmid:35060902
pmcid:PMC8786315
fatcat:lsmt4ebbojgcrpzrnka2jesiq4
A Generalizable Scaffold-Based Approach for Structure Determination of RNAs by Cryo-EM
[article]
2023
biorxiv/medrxiv
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ABSTRACTSingle-particle cryo-electron microscopy (cryo-EM) can reveal the structures of large and often dynamic molecules, but smaller biomolecules remain challenging targets due to their intrinsic low ...
Here, we present a scaffold-based approach that we used to recover maps of sub-25 kDa RNA domains to 4.5 - 5.0 Å. ...
A portion of this research was supported by NIH grant U24GM129547 and performed at the PNCC at OHSU and accessed through EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Office ...
doi:10.1101/2023.07.06.547879
pmid:37461535
pmcid:PMC10350027
fatcat:c6ic74rysffhxfeoqfmadigapm
Review: Deep Learning in Electron Microscopy
[article]
2020
arXiv
pre-print
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For context, we review popular applications of deep learning in electron microscopy. ...
Finally, we discuss future directions of deep learning in electron microscopy. ...
In addition, part of the text in section 1.2 is adapted from our earlier work with permission 201 under a creative commons 4.0 73 license. ...
arXiv:2009.08328v4
fatcat:umocfp5dgvfqzck4ontlflh5ca
Structural basis for assembly and lipid-mediated gating of LRRC8A:C volume-regulated anion channels
[article]
2022
bioRxiv
pre-print
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Here, we leverage a fiducial-tagging strategy to determine single-particle cryo-electron microscopy structures of heterohexameric LRRC8A:C channels in detergent micelles and lipid nanodiscs in three conformations ...
Channels of different subunit composition have distinct properties that explain the functional diversity of LRRC8 currents implicated in a broad range of physiology. ...
Tobias for microscope and computational support at the Cal-Cryo facility. We thank the labs of A. Patapoutian and T.J. Jentsch for gifts of HeLa and HEK293 LRRC8 knockout cell lines. ...
doi:10.1101/2022.07.31.502239
fatcat:yj7h5iemvbaplgag4n6v55wh5y
2021 Index IEEE Transactions on Computational Imaging Vol. 7
2021
IEEE Transactions on Computational Imaging
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
Song, R., +, TCI 2021 689-699 Electron microscopy CryoGAN: A New Reconstruction Paradigm for Single-Particle Cryo-EM Via Deep Adversarial Learning. ...
doi:10.1109/tci.2022.3151176
fatcat:slyirmc7c5egfjjjyfswassh24
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