The Experts below are selected from a list of 76419 Experts worldwide ranked by ideXlab platform
Joachim Frank - One of the best experts on this subject based on the ideXlab platform.
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Automated particle picking for low-contrast macromolecules in cryo-electron microscopy.
Journal of Structural Biology, 2014Co-Authors: Robert Langlois, Jesper Pallesen, Danny N. Ho, John L Rubinstein, Joachim FrankAbstract:Cryo-electron microscopy is an increasingly popular tool for studying the structure and dynamics of biological macromolecules at high resolution. A crucial step in automating single-particle reconstruction of a biological sample is the selection of particle images from a micrograph. We present a novel algorithm for selecting particle images in low-contrast conditions; it proves more effective than the human eye on close-to-focus Micrographs, yielding improved or comparable resolution in reconstructions of two macromolecular complexes.
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reference free particle selection enhanced with semi supervised machine learning for cryo electron microscopy
Journal of Structural Biology, 2011Co-Authors: Robert Langlois, Jesper Pallesen, Joachim FrankAbstract:Abstract Reference-based methods have dominated the approaches to the particle selection problem, proving fast, and accurate on even the most challenging Micrographs. A reference volume, however, is not always available and compiling a set of reference projections from the Micrographs themselves requires significant effort to attain the same level of accuracy. We propose a reference-free method to quickly extract particles from the micrograph. The method is augmented with a new semi-supervised machine-learning algorithm to accurately discriminate particles from contaminants and noise.
Gregory R. Johnson - One of the best experts on this subject based on the ideXlab platform.
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label free prediction of three dimensional fluorescence images from transmitted light microscopy
Nature Methods, 2018Co-Authors: Chawin Ounkomol, Sharmishtaa Seshamani, Mary M Maleckar, Forrest Collman, Gregory R. JohnsonAbstract:Understanding cells as integrated systems is central to modern biology. Although fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, is slow, and can damage cells. We present a label-free method for predicting three-dimensional fluorescence directly from transmitted-light images and demonstrate that it can be used to generate multi-structure, integrated images. The method can also predict immunofluorescence (IF) from electron micrograph (EM) inputs, extending the potential applications. Convolutional neural networks enable prediction of fluorescently labeled structures from three-dimensional time-lapse transmitted-light images. Applications include multiplexed long time-lapse imaging and prediction of fluorescence in electron Micrographs.
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label free prediction of three dimensional fluorescence images from transmitted light microscopy
Nature Methods, 2018Co-Authors: Chawin Ounkomol, Sharmishtaa Seshamani, Mary M Maleckar, Forrest Collman, Gregory R. JohnsonAbstract:Understanding cells as integrated systems is central to modern biology. Although fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, is slow, and can damage cells. We present a label-free method for predicting three-dimensional fluorescence directly from transmitted-light images and demonstrate that it can be used to generate multi-structure, integrated images. The method can also predict immunofluorescence (IF) from electron micrograph (EM) inputs, extending the potential applications. Convolutional neural networks enable prediction of fluorescently labeled structures from three-dimensional time-lapse transmitted-light images. Applications include multiplexed long time-lapse imaging and prediction of fluorescence in electron Micrographs.
Patrick Cramer - One of the best experts on this subject based on the ideXlab platform.
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real time cryo electron microscopy data preprocessing with warp
Nature Methods, 2019Co-Authors: Dimitry Tegunov, Patrick CramerAbstract:The acquisition of cryo-electron microscopy (cryo-EM) data from biological specimens must be tightly coupled to data preprocessing to ensure the best data quality and microscope usage. Here we describe Warp, a software that automates all preprocessing steps of cryo-EM data acquisition and enables real-time evaluation. Warp corrects Micrographs for global and local motion, estimates the local defocus 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, which went from 3.9 A to 3.2 A, of a published cryo-EM data set for influenza virus hemagglutinin. Warp is easy to install from http://github.com/cramerlab/warp and computationally inexpensive, and has an intuitive, streamlined user interface. The user-friendly software tool Warp enables automated, on-the-fly preprocessing of cryo-EM data, including motion correction, defocus estimation, particle picking and image denoising.
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real time cryo em data pre processing with warp
bioRxiv, 2018Co-Authors: Dimitry Tegunov, Patrick CramerAbstract:The acquisition of cryo-electron microscopy (cryo-EM) data from biological specimens is currently largely uncoupled from subsequent data evaluation, correction and processing. Therefore, the acquisition strategy is difficult to optimize during data collection, often leading to suboptimal microscope usage and disappointing results. Here we provide Warp, a software for real-time evaluation, correction, and processing of cryo-EM data during their acquisition. Warp evaluates and monitors key parameters for each recorded micrograph or tomographic tilt series in real time. Warp also rapidly corrects Micrographs for global and local motion, and estimates the local defocus with the use of novel algorithms. The software further includes a deep learning-based particle picking algorithm that rivals human accuracy to make the pre-processing pipeline truly automated. The output from Warp can be directly fed into established tools for particle classification and 3D image reconstruction. In a benchmarking study we show that Warp automatically processed a published cryo-EM data set for influenza virus hemagglutinin, leading to an improvement of the nominal resolution from 3.9 A to 3.2 A. Warp is easy to install, computationally inexpensive, and has an intuitive and streamlined user interface.
Chawin Ounkomol - One of the best experts on this subject based on the ideXlab platform.
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label free prediction of three dimensional fluorescence images from transmitted light microscopy
Nature Methods, 2018Co-Authors: Chawin Ounkomol, Sharmishtaa Seshamani, Mary M Maleckar, Forrest Collman, Gregory R. JohnsonAbstract:Understanding cells as integrated systems is central to modern biology. Although fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, is slow, and can damage cells. We present a label-free method for predicting three-dimensional fluorescence directly from transmitted-light images and demonstrate that it can be used to generate multi-structure, integrated images. The method can also predict immunofluorescence (IF) from electron micrograph (EM) inputs, extending the potential applications. Convolutional neural networks enable prediction of fluorescently labeled structures from three-dimensional time-lapse transmitted-light images. Applications include multiplexed long time-lapse imaging and prediction of fluorescence in electron Micrographs.
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label free prediction of three dimensional fluorescence images from transmitted light microscopy
Nature Methods, 2018Co-Authors: Chawin Ounkomol, Sharmishtaa Seshamani, Mary M Maleckar, Forrest Collman, Gregory R. JohnsonAbstract:Understanding cells as integrated systems is central to modern biology. Although fluorescence microscopy can resolve subcellular structure in living cells, it is expensive, is slow, and can damage cells. We present a label-free method for predicting three-dimensional fluorescence directly from transmitted-light images and demonstrate that it can be used to generate multi-structure, integrated images. The method can also predict immunofluorescence (IF) from electron micrograph (EM) inputs, extending the potential applications. Convolutional neural networks enable prediction of fluorescently labeled structures from three-dimensional time-lapse transmitted-light images. Applications include multiplexed long time-lapse imaging and prediction of fluorescence in electron Micrographs.
Dimitry Tegunov - One of the best experts on this subject based on the ideXlab platform.
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real time cryo electron microscopy data preprocessing with warp
Nature Methods, 2019Co-Authors: Dimitry Tegunov, Patrick CramerAbstract:The acquisition of cryo-electron microscopy (cryo-EM) data from biological specimens must be tightly coupled to data preprocessing to ensure the best data quality and microscope usage. Here we describe Warp, a software that automates all preprocessing steps of cryo-EM data acquisition and enables real-time evaluation. Warp corrects Micrographs for global and local motion, estimates the local defocus 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, which went from 3.9 A to 3.2 A, of a published cryo-EM data set for influenza virus hemagglutinin. Warp is easy to install from http://github.com/cramerlab/warp and computationally inexpensive, and has an intuitive, streamlined user interface. The user-friendly software tool Warp enables automated, on-the-fly preprocessing of cryo-EM data, including motion correction, defocus estimation, particle picking and image denoising.
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real time cryo em data pre processing with warp
bioRxiv, 2018Co-Authors: Dimitry Tegunov, Patrick CramerAbstract:The acquisition of cryo-electron microscopy (cryo-EM) data from biological specimens is currently largely uncoupled from subsequent data evaluation, correction and processing. Therefore, the acquisition strategy is difficult to optimize during data collection, often leading to suboptimal microscope usage and disappointing results. Here we provide Warp, a software for real-time evaluation, correction, and processing of cryo-EM data during their acquisition. Warp evaluates and monitors key parameters for each recorded micrograph or tomographic tilt series in real time. Warp also rapidly corrects Micrographs for global and local motion, and estimates the local defocus with the use of novel algorithms. The software further includes a deep learning-based particle picking algorithm that rivals human accuracy to make the pre-processing pipeline truly automated. The output from Warp can be directly fed into established tools for particle classification and 3D image reconstruction. In a benchmarking study we show that Warp automatically processed a published cryo-EM data set for influenza virus hemagglutinin, leading to an improvement of the nominal resolution from 3.9 A to 3.2 A. Warp is easy to install, computationally inexpensive, and has an intuitive and streamlined user interface.
