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

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dc.contributor.author Bag, Sukantadev
dc.contributor.author Prentice, Michael B.
dc.contributor.author Liang, Mingzhi
dc.contributor.author Warren, Martin J.
dc.contributor.author Roy Choudhury, Kingshuk
dc.date.accessioned 2017-06-21T11:01:25Z
dc.date.available 2017-06-21T11:01:25Z
dc.date.issued 2016-06-13
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 en
dc.identifier.volume 17
dc.identifier.startpage 1
dc.identifier.endpage 14
dc.identifier.issn 1471-2105
dc.identifier.uri http://hdl.handle.net/10468/4127
dc.identifier.doi 10.1186/s12859-016-1107-5
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. en
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) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher BioMed Central en
dc.relation.uri https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-016-1107-5
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. en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Polyhedron graph en
dc.subject Incomplete polyhedral en
dc.subject Classification from incomplete data en
dc.subject Cryo electron tomography en
dc.subject Bacterial microcompartment en
dc.title Classification of polyhedral shapes from individual anisotropically resolved cryo-electron tomography reconstructions en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Michael B Prentice, Microbiology, University College Cork, Cork, Ireland. +353-21-490-3000 Email: m.prentice@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.contributor.funder Science Foundation Ireland
dc.contributor.funder Health Research Board
dc.contributor.funder National Institutes of Health
dc.description.status Peer reviewed en
dc.identifier.journaltitle BMC Bioinformatics en
dc.internal.IRISemailaddress m.prentice@ucc.ie en
dc.identifier.articleid 234


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© 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. Except where otherwise noted, this item's license is described as © 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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