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Classification of polyhedral shapes from individual anisotropically resolved cryo-electron tomography reconstructions

Overview of attention for article published in BMC Bioinformatics, June 2016
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Title
Classification of polyhedral shapes from individual anisotropically resolved cryo-electron tomography reconstructions
Published in
BMC Bioinformatics, June 2016
DOI 10.1186/s12859-016-1107-5
Pubmed ID
Authors

Sukantadev Bag, Michael B Prentice, Mingzhi Liang, Martin J Warren, Kingshuk Roy Choudhury

Abstract

Cryo-electron tomography (cryo-ET) enables 3D imaging of macromolecular structures. Reconstructed cryo-ET images have a "missing wedge" of data loss due to limitations in rotation of the mounting stage. Most current approaches for structure determination improve cryo-ET resolution either by some form of sub-tomogram averaging or template matching, respectively precluding detection of shapes that vary across objects or are a priori unknown. Various macromolecular structures possess polyhedral structure. We propose a classification method for polyhedral shapes from incomplete individual cryo-ET reconstructions, based on topological features of an extracted polyhedral graph (PG). We outline a pipeline for extracting PG from 3-D cryo-ET reconstructions. For classification, we construct a reference library of regular polyhedra. Using geometric simulation, we construct a non-parametric estimate of the distribution of possible incomplete PGs. In studies with simulated data, a Bayes classifier constructed using these distributions has an average test set misclassification error of < 5 % with upto 30 % of the object missing, suggesting accurate polyhedral shape classification is possible from individual incomplete cryo-ET reconstructions. We also demonstrate how the method can be made robust to mis-specification of the PG using an SVM based classifier. The methodology is applied to cryo-ET reconstructions of 30 micro-compartments isolated from E. coli bacteria. The predicted shapes aren't unique, but all belong to the non-symmetric Johnson solid family, illustrating the potential of this approach to study variation in polyhedral macromolecular structures.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 33%
Researcher 2 17%
Lecturer 1 8%
Student > Bachelor 1 8%
Other 1 8%
Other 0 0%
Unknown 3 25%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 25%
Agricultural and Biological Sciences 3 25%
Computer Science 2 17%
Neuroscience 1 8%
Unknown 3 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 June 2016.
All research outputs
#18,463,662
of 22,877,793 outputs
Outputs from BMC Bioinformatics
#6,330
of 7,298 outputs
Outputs of similar age
#267,258
of 352,763 outputs
Outputs of similar age from BMC Bioinformatics
#76
of 93 outputs
Altmetric has tracked 22,877,793 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
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