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    Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification

    Shaikh, T.R. and Trujillo, Ramon and le Barron, J.S. and Baxter, W.T. and Frank, J. (2008) Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification. Journal of Structural Biology 164 (1), pp. 41-48. ISSN 1047-8477.

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    Abstract

    As collection of electron microscopy data for single-particle reconstruction becomes more efficient, due to electronic image capture, one of the principal limiting steps in a reconstruction remains particle-verification, which is especially costly in terms of user input. Recently, some algorithms have been developed to window particles automatically, but the resulting particle sets typically need to be verified manually. Here we describe a procedure to speed up verification of windowed particles using multivariate data analysis and classification. In this procedure, the particle set is subjected to multi-reference alignment before the verification. The aligned particles are first binned according to orientation and are binned further by K-means classification. Rather than selection of particles individually, an entire class of particles can be selected, with an option to remove outliers. Since particles in the same class present the same view, distinction between good and bad images becomes more straightforward. We have also developed a graphical interface, written in Python/Tkinter, to facilitate this implementation of particle-verification. For the demonstration of the particle-verification scheme presented here, electron micrographs of ribosomes are used.

    Metadata

    Item Type: Article
    Keyword(s) / Subject(s): Particle selection, electron microscopy, automation, reference bias
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Natural Sciences
    Depositing User: Administrator
    Date Deposited: 29 Jul 2011 08:32
    Last Modified: 02 Aug 2023 16:55
    URI: https://eprints.bbk.ac.uk/id/eprint/3913

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