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

As collection of electron inicroscopy data for single-particle recontruction becomes more efficient, due to electronic image capature, 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 manaully. Here we describe a procedure to speed up verification of windowed particdles 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 orinentation 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 ribsomes are used. (c) 2008 Elsevier Inc. All rights reserved.