Journal
JOURNAL OF STRUCTURAL BIOLOGY
Volume 183, Issue 3, Pages 342-353Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jsb.2013.07.015
Keywords
Electron microscopy; Particle picking; Machine learning; Single particle analysis
Funding
- Spanish Ministry of Economy and Competitiveness [AIC-A-2011-0638, BFU2009-09331, BIO2010-16566, ACI2009-1022, ACI2010-1088]
- Juan de la Cierva [JCI-2011-10185]
- Ramon y Cajal fellowship
- CSIC
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Three-dimensional reconstruction of biological specimens using electron microscopy by single particle methodologies requires the identification and extraction of the imaged particles from the acquired micrographs. Automatic and semiautomatic particle selection approaches can localize these particles, minimizing the user interaction, but at the cost of selecting a non-negligible number of incorrect particles, which can corrupt the final three-dimensional reconstruction. In this work, we present a novel particle quality assessment and sorting method that can separate most erroneously picked particles from correct ones. The proposed method is based on multivariate statistical analysis of a particle set that has been picked previously using any automatic or manual approach. The new method uses different sets of particle descriptors, which are morphology-based, histogram-based and signal to noise analysis based. We have tested our proposed algorithm with experimental data obtaining very satisfactory results. The algorithm is freely available as a part of the Xmipp 3.0 package [http://xmipp.cnb.csic.es]. (c) 2013 Elsevier Inc. All rights reserved.
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