02306nas a2200289   4500008004100000022001400041245013100055210006900186260001200255300000900267490000800276520139700284653001501681653002801696653001901724653002201743653004001765653002501805653002601830653001401856100002201870700002001892700002001912700001701932700001901949856004801968       2008                        eng d  a1095-865700aParticle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification.0 aParticleverification for singleparticle referencebased reconstru  c10/2008  a41-80 v1643 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">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.</span></p>10aAlgorithms10aArtificial Intelligence10aClassification10aImage Enhancement10aImage Processing, Computer-Assisted10aMicroscopy, Electron10aMultivariate Analysis10aRibosomes1 aShaikh, Tanvir, R1 aTrujillo, Ramon1 aLeBarron, Jamie1 aBaxter, Bill1 aFrank, Joachim  uhttp://www.ncbi.nlm.nih.gov/pubmed/18619547