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An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification
MOTIVATION: Cellular Electron CryoTomography (CECT) is an emerging 3D imaging technique that visualizes subcellular organization of single cells at sub-molecular resolution and in near-native state. CECT captures large numbers of macromolecular complexes of highly diverse structures and abundances....
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022576/ https://www.ncbi.nlm.nih.gov/pubmed/29949977 http://dx.doi.org/10.1093/bioinformatics/bty267 |
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author | Zhao, Yixiu Zeng, Xiangrui Guo, Qiang Xu, Min |
author_facet | Zhao, Yixiu Zeng, Xiangrui Guo, Qiang Xu, Min |
author_sort | Zhao, Yixiu |
collection | PubMed |
description | MOTIVATION: Cellular Electron CryoTomography (CECT) is an emerging 3D imaging technique that visualizes subcellular organization of single cells at sub-molecular resolution and in near-native state. CECT captures large numbers of macromolecular complexes of highly diverse structures and abundances. However, the structural complexity and imaging limits complicate the systematic de novo structural recovery and recognition of these macromolecular complexes. Efficient and accurate reference-free subtomogram averaging and classification represent the most critical tasks for such analysis. Existing subtomogram alignment based methods are prone to the missing wedge effects and low signal-to-noise ratio (SNR). Moreover, existing maximum-likelihood based methods rely on integration operations, which are in principle computationally infeasible for accurate calculation. RESULTS: Built on existing works, we propose an integrated method, Fast Alignment Maximum Likelihood method (FAML), which uses fast subtomogram alignment to sample sub-optimal rigid transformations. The transformations are then used to approximate integrals for maximum-likelihood update of subtomogram averages through expectation–maximization algorithm. Our tests on simulated and experimental subtomograms showed that, compared to our previously developed fast alignment method (FA), FAML is significantly more robust to noise and missing wedge effects with moderate increases of computation cost. Besides, FAML performs well with significantly fewer input subtomograms when the FA method fails. Therefore, FAML can serve as a key component for improved construction of initial structural models from macromolecules captured by CECT. AVAILABILITY AND IMPLEMENTATION: http://www.cs.cmu.edu/mxu1 |
format | Online Article Text |
id | pubmed-6022576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-60225762018-07-10 An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification Zhao, Yixiu Zeng, Xiangrui Guo, Qiang Xu, Min Bioinformatics Ismb 2018–Intelligent Systems for Molecular Biology Proceedings MOTIVATION: Cellular Electron CryoTomography (CECT) is an emerging 3D imaging technique that visualizes subcellular organization of single cells at sub-molecular resolution and in near-native state. CECT captures large numbers of macromolecular complexes of highly diverse structures and abundances. However, the structural complexity and imaging limits complicate the systematic de novo structural recovery and recognition of these macromolecular complexes. Efficient and accurate reference-free subtomogram averaging and classification represent the most critical tasks for such analysis. Existing subtomogram alignment based methods are prone to the missing wedge effects and low signal-to-noise ratio (SNR). Moreover, existing maximum-likelihood based methods rely on integration operations, which are in principle computationally infeasible for accurate calculation. RESULTS: Built on existing works, we propose an integrated method, Fast Alignment Maximum Likelihood method (FAML), which uses fast subtomogram alignment to sample sub-optimal rigid transformations. The transformations are then used to approximate integrals for maximum-likelihood update of subtomogram averages through expectation–maximization algorithm. Our tests on simulated and experimental subtomograms showed that, compared to our previously developed fast alignment method (FA), FAML is significantly more robust to noise and missing wedge effects with moderate increases of computation cost. Besides, FAML performs well with significantly fewer input subtomograms when the FA method fails. Therefore, FAML can serve as a key component for improved construction of initial structural models from macromolecules captured by CECT. AVAILABILITY AND IMPLEMENTATION: http://www.cs.cmu.edu/mxu1 Oxford University Press 2018-07-01 2018-06-27 /pmc/articles/PMC6022576/ /pubmed/29949977 http://dx.doi.org/10.1093/bioinformatics/bty267 Text en © The Author(s) 2018. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Ismb 2018–Intelligent Systems for Molecular Biology Proceedings Zhao, Yixiu Zeng, Xiangrui Guo, Qiang Xu, Min An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification |
title | An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification |
title_full | An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification |
title_fullStr | An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification |
title_full_unstemmed | An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification |
title_short | An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification |
title_sort | integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification |
topic | Ismb 2018–Intelligent Systems for Molecular Biology Proceedings |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6022576/ https://www.ncbi.nlm.nih.gov/pubmed/29949977 http://dx.doi.org/10.1093/bioinformatics/bty267 |
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