More Web Proxy on the site http://driver.im/

research-article

A Two-Phase Improved Correlation Method for Automatic Particle Selection in Cryo-EM

Authors:

Xiaohua WanAuthors Info & Claims

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB), Volume 14, Issue 2

Pages 316 - 325

https://doi.org/10.1109/TCBB.2015.2415787

Published: 01 March 2017 Publication History

Abstract

Particle selection from cryo-electron microscopy Cryo-EM images is very important for high-resolution reconstruction of macromolecular structure. The methods of particle selection can be roughly grouped into two classes, template-matching methods and feature-based methods. In general, template-matching methods usually generate better results than feature-based methods. However, the accuracy of template-matching methods is restricted by the noise and low contrast of Cryo-EM images. Moreover, the processing speed of template-matching methods, restricted by the random orientation of particles, further limits their practical applications. In this paper, combining the advantages of feature-based methods and template-matching methods, we present a two-phase improved correlation method for automatic, fast particle selection. In Phase I, we generate a preliminary particle set using rotation-invariant features of particles. In Phase II, we filter the preliminary particle set using a correlation method to reduce the interference of the high noise background and improve the precision of particle selection. We apply several optimization strategies, including a modified adaboost algorithm, Divide and Conquer technique, cascade strategy and graphics processing unit parallel technique, to improve feature recognition ability and reduce processing time. In addition, we developed two correlation score functions for different correlation situations. Experimental results on the benchmark of Cryo-EM images show that our method can improve the accuracy and processing speed of particle selection significantly.

References

[1]

X. Yan, G. Cardone, X. Zhang, Z. H. Zhou, and T. S. Baker, "Single particle analysis integrated with microscopy: A high-throughput approach for reconstructing icosahedral particles," J. Struct. Biol., vol. 186, no. 1, pp. 8-18, 2014.

[2]

P. W. Hawkes, "The electron microscope as a structure projector," in Electron Tomography. New York, NY, USA: Springer, 2006, pp. 83-111.

[3]

Z. H. Zhou, "Towards atomic resolution structural determination by single-particle cryo-electron microscopy," Current Opinion Struct. Biol., vol. 18, no. 2, pp. 218-228, 2008.

[4]

F. Joachim, Three-Dimensional Electron Microscopy of Macromolecular Assemblies. San Diego, CA, USA: Academic, 1996.

[5]

A. Sali, R. Glaeser, T. Earnest, and W. Baumeister, "From words to literature in structural proteomics," Nature, vol. 422, no. 6928, pp. 216-225, 2003.

[6]

Z. Yu and C. Bajaj, "Detecting circular and rectangular particles based on geometric feature detection in electron micrographs," J. Struct. Biol., vol. 145, no. 1, pp. 168-180, 2004.

[7]

Y. Zhu, B. Carragher, R. M. Glaeser, D. Fellmann, C. Bajaj, M. Bern, F. Mouche, F. d. Haas, R. J. Hall, D. J. Kriegman, S. J. Ludtke, S. P. Mallick, P. A. Penczek, A. M. Roseman, F. J. Sigworth, N. Volkmann, and C. S. Potter, "Automatic particle selection: Results of a comparative study," J. Struct. Biol., vol. 145, no. 1, pp. 3-14, 2004.

[8]

R. J. Hall and A. Patwardhan, "A two step approach for semi-automated particle selection from low contrast cryo-electron micrographs," J. Struct. Biol., vol. 145, no. 1, pp. 19-28, 2004.

[9]

S. P. Mallick, Y. Zhu, and D. Kriegman, "Detecting particles in cryo-EM micrographs using learned features," J. Struct. Biol., vol. 145, no. 1, pp. 52-62, 2004.

[10]

Y. Zhu, B. Carragher, F. Mouche, and C. S. Potter, "Automatic particle detection through efficient hough transforms," IEEE Trans. Med. Imaging, vol. 22, no. 9, pp. 1053-1062, Sep. 2003.

[11]

C. Sorzano, E. Recarte, M. Alcorlo, J. Bilbao-Castro, C. San-Martín, R. Marabini, and J. Carazo, "Automatic particle selection from electron micrographs using machine learning techniques," J. Struct. Biol., vol. 167, no. 3, pp. 252-260, 2009.

[12]

V. Abrishami, A. Zaldívar-Peraza, J. de la Rosa-Trevín, J. Vargas, J. Otón, R. Marabini, Y. Shkolnisky, J. Carazo, and C. Sorzano, "A pattern matching approach to the automatic selection of particles from low-contrast electron micrographs," Bioinformatics, vol. 29, no. 19, pp. 2460-2468, 2013.

[13]

A. Roseman, "Findemla fast, efficient program for automatic selection of particles from electron micrographs," J. Struct. Biol., vol. 145, no. 1, pp. 91-99, 2004.

[14]

F. J. Sigworth, "Classical detection theory and the cryo-EM particle selection problem," J. Struct. Biol., vol. 145, no. 1, pp. 111-122, 2004.

[15]

Y. Freund and R. E. Schapire, "A desicion-theoretic generalization of on-line learning and an application to boosting," in Computational Learning Theory. New York, NY, USA: Springer, 1995, pp. 23-37.

[16]

Z. Tu, "Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering," in Proc. 10th IEEE Int. Conf. Comput. Vision, 2005, vol. 2, pp. 1589-1596.

[17]

R. Langlois and J. Frank, "A clarification of the terms used in comparing semi-automated particle selection algorithms in cryo-EM," J. Struct. Biol., vol. 175, no. 3, pp. 348-352, 2011.

[18]

T. V. Hoang, X. Cavin, P. Schultz, and D. W. Ritchie, "gEMpicker: A highly parallel GPU-accelerated particle picking tool for cryo-electron microscopy," BMC Struct. Biol., vol. 13, no. 1, p. 25, 2013.

[19]

E. V. Orlova, P. Dube, J. R. Harris, E. Beckman, F. Zemlin, J. Markl, and M. van Heel, "Structure of keyhole limpet hemocyanin type 1 (KLH1) at 15 Å resolution by electron cryomicroscopy and angular reconstitution," J. Molecular Biol., vol. 271, no. 3, pp. 417-437, 1997.

[20]

R. M. Glaeser, "Historical background: Why is it important to improve automated particle selection methods?" J. Struct. Biol., vol. 145, no. 1, pp. 15-18, 2004.

[21]

R. Langlois, J. Pallesen, and J. Frank, "Reference-free particle selection enhanced with semi-supervised machine learning for cryo-electron microscopy," J. Struct. Biol., vol. 175, no. 3, pp. 353- 361, 2011.

[22]

D. Woolford, G. Ericksson, R. Rothnagel, D. Muller, M. J. Landsberg, R. S. Pantelic, A. McDowall, B. Pailthorpe, P. R. Young, B. Hankamer, and J. Banks, "SwarmPS: Rapid, semi-automated single particle selection software," J. Struct. Biol., vol. 157, no. 1, pp. 174-188, 2007.

[23]

A. M. Roseman, "Particle finding in electron micrographs using a fast local correlation algorithm," Ultramicroscopy, vol. 94, no. 3, pp. 225-236, 2003.

Cited By

Li HChen GGao SLi JZhang F(2021)PickerOptimizer: A Deep Learning-Based Particle Optimizer for Cryo-Electron Microscopy Particle-Picking AlgorithmsBioinformatics Research and Applications10.1007/978-3-030-91415-8_46(549-560)Online publication date: 26-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-030-91415-8_46

A Two-Phase Improved Correlation Method for Automatic Particle Selection in Cryo-EM
1. Computing methodologies
  1. Computer graphics
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

Recommendations

An improved cooperative quantum-behaved particle swarm optimization

Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm ...
Improved binary particle swarm optimization using catfish effect for feature selection
Highlights
► K-NN with LOOCV is used to evaluate the fitness function. ► CatfishBPSO simplified feature selection. ► CatfishBPSO reduced number of necessary features. ► CatfishBPSO yielded the best values of all methods tested.
...
Abstract
The feature selection process constitutes a commonly encountered problem of global combinatorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable ...
Improved particle swarm optimization algorithm and its application in text feature selection

We proposed three improved Particle swarm optimization models based on a common PSO model and two improved PSO models.Selecting Reuters-21578 as the corpus, six experiments are conducted respectively using improved PSO models.Combining asynchronously ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image IEEE/ACM Transactions on Computational Biology and Bioinformatics

IEEE/ACM Transactions on Computational Biology and Bioinformatics Volume 14, Issue 2

March 2017

250 pages

ISSN:1545-5963

Issue’s Table of Contents

Copyright © 2017.

Publisher

IEEE Computer Society Press

Washington, DC, United States

Publication History

Published: 01 March 2017

Published in TCBB Volume 14, Issue 2

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
40
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 12 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Li HChen GGao SLi JZhang F(2021)PickerOptimizer: A Deep Learning-Based Particle Optimizer for Cryo-Electron Microscopy Particle-Picking AlgorithmsBioinformatics Research and Applications10.1007/978-3-030-91415-8_46(549-560)Online publication date: 26-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-030-91415-8_46

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents