Blind estimation of DED camera gain in Electron Microscopy

Sorzano, Carlos Óscar S.; Fernández-Giménez, E.; Peredo-Robinson, V.; Vargas, Javier; Majtner, Tomáš; Caffarena, Gabriel; Otón, Joaquı́n; Vilas, José Luis; Rosa-Trevín, J.M. de la; Melero, Roberto; Gómez-Blanco, J.; Cuenca, J.; Caño, Laura del; Conesa, Pablo; Marabini, Roberto; Carazo, José Marı́a

doi:10.1016/j.jsb.2018.03.007

Cited by 7 publications

(6 citation statements)

References 7 publications

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“…The normalization normally sets the statistical properties of the ice to some prespecified values (Sorzano et al, 2004). However, this normalization is affected by outlying pixels, nearby particles, contaminations or carbon edges, illumination gradients, inhomogeneous camera gain images (Sorzano, Ferna ´ndez-Gime ´nez et al, 2018) etc. For this reason, the particle normalization must be refined in order to make the projection images maximally consistent with the reconstructed volume (Scheres et al, 2009;.…”

Section: Image Normalizationmentioning

confidence: 99%

On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy

Sorzano

Jiménez-Moreno

Maluenda

et al. 2022

Acta Crystallogr D Struct Biol

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Cryo-electron microscopy (cryoEM) has become a well established technique to elucidate the 3D structures of biological macromolecules. Projection images from thousands of macromolecules that are assumed to be structurally identical are combined into a single 3D map representing the Coulomb potential of the macromolecule under study. This article discusses possible caveats along the image-processing path and how to avoid them to obtain a reliable 3D structure. Some of these problems are very well known in the community. These may be referred to as sample-related (such as specimen denaturation at interfaces or non-uniform projection geometry leading to underrepresented projection directions). The rest are related to the algorithms used. While some have been discussed in depth in the literature, such as the use of an incorrect initial volume, others have received much less attention. However, they are fundamental in any data-analysis approach. Chiefly among them, instabilities in estimating many of the key parameters that are required for a correct 3D reconstruction that occur all along the processing workflow are referred to, which may significantly affect the reliability of the whole process. In the field, the term overfitting has been coined to refer to some particular kinds of artifacts. It is argued that overfitting is a statistical bias in key parameter-estimation steps in the 3D reconstruction process, including intrinsic algorithmic bias. It is also shown that common tools (Fourier shell correlation) and strategies (gold standard) that are normally used to detect or prevent overfitting do not fully protect against it. Alternatively, it is proposed that detecting the bias that leads to overfitting is much easier when addressed at the level of parameter estimation, rather than detecting it once the particle images have been combined into a 3D map. Comparing the results from multiple algorithms (or at least, independent executions of the same algorithm) can detect parameter bias. These multiple executions could then be averaged to give a lower variance estimate of the underlying parameters.

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Section: Image Normalizationmentioning

confidence: 99%

On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy

Sorzano

Jiménez-Moreno

Maluenda

et al. 2022

Acta Crystallogr D Struct Biol

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“…The workflow starts two independent protocols in parallel. The first protocol estimates the camera gain at each pixel for a few movies (Sorzano et al, 2018). The output of this protocol serves as verification of the validity of the experimental gain image for the whole data set.…”

Section: Image Processing Workflowsmentioning

confidence: 99%

Using Scipion for stream image processing at Cryo-EM facilities

Gómez-Blanco

Rosa-Trevín

Marabini

et al. 2018

Journal of Structural Biology

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Three dimensional electron microscopy is becoming a very data-intensive field in which vast amounts of experimental images are acquired at high speed. To manage such large-scale projects, we had previously developed a modular workflow system called Scipion (de la Rosa-Trevín et al., 2016). We present here a major extension of Scipion that allows processing of EM images while the data is being acquired. This approach helps to detect problems at early stages, saves computing time and provides users with a detailed evaluation of the data quality before the acquisition is finished. At present, Scipion has been deployed and is in production mode in seven Cryo-EM facilities throughout the world.

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“…Xmipp also provides several other utility protocols, for example, the Movie Maxshift protocol for movie rejection based on the maximum shift of the corrected frames, the Split frames protocol for extracting only odd/even frames, the Movie Average protocol for creation of a simple movie average, or the automatic Movie Gain detection protocol [7] that can identify cases of incorrectly calibrated cameras. In addition, Xmipp provides the Preprocess Micrograph protocol for micrograph preprocessing, such as filtering or normalization.…”

Section: Movie Acquisition and Frame Alignmentmentioning

confidence: 99%

Advances in Xmipp for Cryo–Electron Microscopy: From Xmipp to Scipion

et al. 2021

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Xmipp is an open-source software package consisting of multiple programs for processing data originating from electron microscopy and electron tomography, designed and managed by the Biocomputing Unit of the Spanish National Center for Biotechnology, although with contributions from many other developers over the world. During its 25 years of existence, Xmipp underwent multiple changes and updates. While there were many publications related to new programs and functionality added to Xmipp, there is no single publication on the Xmipp as a package since 2013. In this article, we give an overview of the changes and new work since 2013, describe technologies and techniques used during the development, and take a peek at the future of the package.

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Blind estimation of DED camera gain in Electron Microscopy

Cited by 7 publications

References 7 publications

On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy

On bias, variance, overfitting, gold standard and consensus in single-particle analysis by cryo-electron microscopy

Using Scipion for stream image processing at Cryo-EM facilities

Advances in Xmipp for Cryo–Electron Microscopy: From Xmipp to Scipion

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