Literature DB >> 15065681

A binary segmentation approach for boxing ribosome particles in cryo EM micrographs.

P S Umesh Adiga1, Ravi Malladi, William Baxter, Robert M Glaeser.   

Abstract

Three-dimensional reconstruction of ribosome particles from electron micrographs requires selection of many single-particle images. Roughly 100,000 particles are required to achieve approximately 10 A resolution. Manual selection of particles, by visual observation of the micrographs on a computer screen, is recognized as a bottleneck in automated single-particle reconstruction. This paper describes an efficient approach for automated boxing of ribosome particles in micrographs. Use of a fast, anisotropic non-linear reaction-diffusion method to pre-process micrographs and rank-leveling to enhance the contrast between particles and the background, followed by binary and morphological segmentation constitute the core of this technique. Modifying the shape of the particles to facilitate segmentation of individual particles within clusters and boxing the isolated particles is successfully attempted. Tests on a limited number of micrographs have shown that over 80% success is achieved in automatic particle picking.

Mesh:

Year:  2004        PMID: 15065681     DOI: 10.1016/j.jsb.2003.10.026

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


  9 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.  Detection of viruses by counting single fluorescent genetically biotinylated reporter immunophage using a lateral flow assay.

Authors:  Jinsu Kim; Meena Adhikari; Sagar Dhamane; Anna E V Hagström; Katerina Kourentzi; Ulrich Strych; Richard C Willson; Jacinta C Conrad
Journal:  ACS Appl Mater Interfaces       Date:  2015-01-23       Impact factor: 9.229

4.  Reference-free particle selection enhanced with semi-supervised machine learning for cryo-electron microscopy.

Authors:  Robert Langlois; Jesper Pallesen; Joachim Frank
Journal:  J Struct Biol       Date:  2011-06-17       Impact factor: 2.867

5.  A 3D image filter for parameter-free segmentation of macromolecular structures from electron tomograms.

Authors:  Rubbiya A Ali; Michael J Landsberg; Emily Knauth; Garry P Morgan; Brad J Marsh; Ben Hankamer
Journal:  PLoS One       Date:  2012-03-29       Impact factor: 3.240

6.  AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM images.

Authors:  Adil Al-Azzawi; Anes Ouadou; John J Tanner; Jianlin Cheng
Journal:  BMC Bioinformatics       Date:  2019-06-13       Impact factor: 3.169

7.  A Super-Clustering Approach for Fully Automated Single Particle Picking in Cryo-EM.

Authors:  Adil Al-Azzawi; Anes Ouadou; John J Tanner; Jianlin Cheng
Journal:  Genes (Basel)       Date:  2019-08-30       Impact factor: 4.096

Review 8.  Research journey of respirasome.

Authors:  Meng Wu; Jinke Gu; Shuai Zong; Runyu Guo; Tianya Liu; Maojun Yang
Journal:  Protein Cell       Date:  2020-01-09       Impact factor: 14.870

Review 9.  A primer to single-particle cryo-electron microscopy.

Authors:  Yifan Cheng; Nikolaus Grigorieff; Pawel A Penczek; Thomas Walz
Journal:  Cell       Date:  2015-04-23       Impact factor: 41.582

  9 in total

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