Literature DB >> 15065678

SLEUTH--a fast computer program for automatically detecting particles in electron microscope images.

Judith M Short1.   

Abstract

A method has been developed to locate biological complexes in a digitized electron micrograph by matching small windows to a set of reference images using a series of simple criteria. From the reference images, the program calculates parameters such as the radius of gyration, the density sum and variance. It compares them with corresponding values from a moving square window of densities extracted from the micrograph and records the coordinates of successfully matched candidate squares. Since the same particle is detected in a series of overlapping windows, candidates found to be within close proximity are grouped and the best-fitting one is selected from each cluster. The user is required only to select a small stack of boxed reference images and provide a few parameters, such as the particle radius and the minimum acceptable distance between particle centres. Micrograph labels and other areas that do not contain appropriate specimens are automatically ignored in order to minimize false positives. The program has been tested successfully on a variety of different biological structures, from both negatively stained and ice-embedded specimens.

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Year:  2004        PMID: 15065678     DOI: 10.1016/j.jsb.2003.09.011

Source DB:  PubMed          Journal:  J Struct Biol        ISSN: 1047-8477            Impact factor:   2.867


  3 in total

1.  Automatic particle selection from electron micrographs using machine learning techniques.

Authors:  C O S Sorzano; E Recarte; M Alcorlo; J R Bilbao-Castro; C San-Martín; R Marabini; J M Carazo
Journal:  J Struct Biol       Date:  2009-06-23       Impact factor: 2.867

2.  A clarification of the terms used in comparing semi-automated particle selection algorithms in cryo-EM.

Authors:  Robert Langlois; Joachim Frank
Journal:  J Struct Biol       Date:  2011-03-21       Impact factor: 2.867

3.  Avoiding the pitfalls of single particle cryo-electron microscopy: Einstein from noise.

Authors:  Richard Henderson
Journal:  Proc Natl Acad Sci U S A       Date:  2013-10-08       Impact factor: 11.205

  3 in total

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