V Abrishami1, A Zaldívar-Peraza, J M de la Rosa-Trevín, J Vargas, J Otón, R Marabini, Y Shkolnisky, J M Carazo, C O S Sorzano. 1. Biocomputing Unit, National Center of Biotechnology (CSIC), Department of Computer Science, University Autonoma de Madrid, Campus Universidad Autonoma s/n, 28049 Cantoblanco, Madrid, Spain, Department Applied Mathematics, Tel Aviv University, Ramat Aviv, Tel Aviv 69978 Israel and Bioengineering Lab, Escuela Politecnica Superior, University San Pablo CEU, 28668 Boadilla del Monte, Madrid, Spain.
Abstract
MOTIVATION: Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. Achieving high-resolution structural details with electron microscopy requires the identification of a large number (up to hundreds of thousands) of single particles from electron micrographs, which is a laborious task if it has to be manually done and constitutes a hurdle towards high-throughput. Automatic particle selection in micrographs is far from being settled and new and more robust algorithms are required to reduce the number of false positives and false negatives. RESULTS: In this article, we introduce an automatic particle picker that learns from the user the kind of particles he is interested in. Particle candidates are quickly and robustly classified as particles or non-particles. A number of new discriminative shape-related features as well as some statistical description of the image grey intensities are used to train two support vector machine classifiers. Experimental results demonstrate that the proposed method: (i) has a considerably low computational complexity and (ii) provides results better or comparable with previously reported methods at a fraction of their computing time. AVAILABILITY: The algorithm is fully implemented in the open-source Xmipp package and downloadable from http://xmipp.cnb.csic.es.
MOTIVATION: Structural information of macromolecular complexes provides key insights into the way they carry out their biological functions. Achieving high-resolution structural details with electron microscopy requires the identification of a large number (up to hundreds of thousands) of single particles from electron micrographs, which is a laborious task if it has to be manually done and constitutes a hurdle towards high-throughput. Automatic particle selection in micrographs is far from being settled and new and more robust algorithms are required to reduce the number of false positives and false negatives. RESULTS: In this article, we introduce an automatic particle picker that learns from the user the kind of particles he is interested in. Particle candidates are quickly and robustly classified as particles or non-particles. A number of new discriminative shape-related features as well as some statistical description of the image grey intensities are used to train two support vector machine classifiers. Experimental results demonstrate that the proposed method: (i) has a considerably low computational complexity and (ii) provides results better or comparable with previously reported methods at a fraction of their computing time. AVAILABILITY: The algorithm is fully implemented in the open-source Xmipp package and downloadable from http://xmipp.cnb.csic.es.
Authors: Carlos Oscar S Sorzano; Amaya Jiménez-Moreno; David Maluenda; Erney Ramírez-Aportela; Marta Martínez; Ana Cuervo; Robert Melero; Jose Javier Conesa; Ruben Sánchez-García; David Strelak; Jiri Filipovic; Estrella Fernández-Giménez; Federico de Isidro-Gómez; David Herreros; Pablo Conesa; Laura Del Caño; Yunior Fonseca; Jorge Jiménez de la Morena; Jose Ramon Macías; Patricia Losana; Roberto Marabini; Jose-Maria Carazo Journal: Methods Mol Biol Date: 2021
Authors: Jörg Votteler; Cassandra Ogohara; Sue Yi; Yang Hsia; Una Nattermann; David M Belnap; Neil P King; Wesley I Sundquist Journal: Nature Date: 2016-11-30 Impact factor: 49.962