dc.contributor.author |
Bag, Sukantadev |
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dc.contributor.author |
Prentice, Michael B. |
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dc.contributor.author |
Liang, Mingzhi |
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dc.contributor.author |
Warren, Martin J. |
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dc.contributor.author |
Roy Choudhury, Kingshuk |
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dc.date.accessioned |
2017-06-21T11:01:25Z |
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dc.date.available |
2017-06-21T11:01:25Z |
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dc.date.issued |
2016-06-13 |
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dc.identifier.citation |
Bag, S., Prentice, M. B., Liang, M., Warren, M. J. and Roy Choudhury, K. (2016) 'Classification of polyhedral shapes from individual anisotropically resolved cryo-electron tomography reconstructions', BMC Bioinformatics, 17, 234 (14pp). doi: 10.1186/s12859-016-1107-5 |
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dc.identifier.volume |
17 |
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dc.identifier.startpage |
1 |
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dc.identifier.endpage |
14 |
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dc.identifier.issn |
1471-2105 |
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dc.identifier.uri |
http://hdl.handle.net/10468/4127 |
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dc.identifier.doi |
10.1186/s12859-016-1107-5 |
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dc.description.abstract |
Background: 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). Results: 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. Conclusions: 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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dc.description.sponsorship |
Science Foundation Ireland (Short Term Travel Fellowship 06/RFP/GEN053 STTF 08, Research Frontiers Program (grant 07/REF/MA7F543), SFI Math Initiative); Health Research Board (HRA_POR/2011/111); NIH (Duke CTSA UL1TR001117) |
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dc.format.mimetype |
application/pdf |
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dc.language.iso |
en |
en |
dc.publisher |
BioMed Central |
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dc.relation.uri |
https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1107-5 |
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dc.rights |
© 2016, Bag et al. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
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dc.rights.uri |
http://creativecommons.org/licenses/by/4.0/ |
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dc.subject |
Polyhedron graph |
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dc.subject |
Incomplete polyhedral |
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dc.subject |
Classification from incomplete data |
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dc.subject |
Cryo electron tomography |
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dc.subject |
Bacterial microcompartment |
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dc.title |
Classification of polyhedral shapes from individual anisotropically resolved cryo-electron tomography reconstructions |
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dc.type |
Article (peer-reviewed) |
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dc.internal.authorcontactother |
Michael B Prentice, Microbiology, University College Cork, Cork, Ireland. +353-21-490-3000 Email: m.prentice@ucc.ie |
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dc.internal.availability |
Full text available |
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dc.description.version |
Published Version |
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dc.contributor.funder |
Science Foundation Ireland
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dc.contributor.funder |
Health Research Board
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dc.contributor.funder |
National Institutes of Health
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dc.description.status |
Peer reviewed |
en |
dc.identifier.journaltitle |
BMC Bioinformatics |
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dc.internal.IRISemailaddress |
m.prentice@ucc.ie |
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dc.identifier.articleid |
234 |
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