@article {2114, title = {Exploration of parameters in cryo-EM leading to an improved density map of the E. coli ribosome.}, journal = {J Struct Biol}, volume = {164}, year = {2008}, month = {10/2008}, pages = {24-32}, abstract = {

A number of image processing parameters in the 3D reconstruction of a ribosome complex from a cryo-EM data set were varied to test their effects on the final resolution. The parameters examined were pixel size, window size, and mode of Fourier amplitude enhancement at high spatial frequencies. In addition, the strategy of switching from large to small pixel size during angular refinement was explored. The relationship between resolution (in Fourier space) and the number of particles was observed to follow a lin-log dependence, a relationship that appears to hold for other data, as well. By optimizing the above parameters, and using a lin-log extrapolation to the full data set in the estimation of resolution from half-sets, we obtained a 3D map from 131,599 ribosome particles at 6.7A resolution (FSC=0.5).

}, keywords = {Cryoelectron Microscopy, Escherichia coli, Image Enhancement, Image Processing, Computer-Assisted, Ribosomes}, issn = {1095-8657}, doi = {10.1016/j.jsb.2008.05.007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18606549}, author = {LeBarron, Jamie and Grassucci, Robert A and Shaikh, Tanvir R and Baxter, Bill and Sengupta, Jayati and Frank, Joachim} } @article {2113, title = {Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification.}, journal = {J Struct Biol}, volume = {164}, year = {2008}, month = {10/2008}, pages = {41-8}, 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.

}, keywords = {Algorithms, Artificial Intelligence, Classification, Image Enhancement, Image Processing, Computer-Assisted, Microscopy, Electron, Multivariate Analysis, Ribosomes}, issn = {1095-8657}, doi = {10.1016/j.jsb.2008.06.006}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18619547}, author = {Shaikh, Tanvir R and Trujillo, Ramon and LeBarron, Jamie and Baxter, Bill and Frank, Joachim} }