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dc.contributor.authorZhang, Jingrong
dc.contributor.authorWang, Zihao
dc.contributor.authorChen, Yu
dc.contributor.authorHan, Renmin
dc.contributor.authorLiu, Zhiyong
dc.contributor.authorSun, Fei
dc.contributor.authorZhang, Fa
dc.date.accessioned2019-02-14T08:19:30Z
dc.date.available2019-02-14T08:19:30Z
dc.date.issued2019-01-18
dc.identifier.citationZhang J, Wang Z, Chen Y, Han R, Liu Z, et al. (2019) PIXER: an automated particle-selection method based on segmentation using a deep neural network. BMC Bioinformatics 20. Available: http://dx.doi.org/10.1186/s12859-019-2614-y.
dc.identifier.issn1471-2105
dc.identifier.doi10.1186/s12859-019-2614-y
dc.identifier.urihttp://hdl.handle.net/10754/631040
dc.description.abstractBACKGROUND:Cryo-electron microscopy (cryo-EM) has become a widely used tool for determining the structures of proteins and macromolecular complexes. To acquire the input for single-particle cryo-EM reconstruction, researchers must select hundreds of thousands of particles from micrographs. As the signal-to-noise ratio (SNR) of micrographs is extremely low, the performance of automated particle-selection methods is still unable to meet research requirements. To free researchers from this laborious work and to acquire a large number of high-quality particles, we propose an automated particle-selection method (PIXER) based on the idea of segmentation using a deep neural network. RESULTS:First, to accommodate low-SNR conditions, we convert micrographs into probability density maps using a segmentation network. These probability density maps indicate the likelihood that each pixel of a micrograph is part of a particle instead of just background noise. Particles selected from density maps have a more robust signal than do those directly selected from the original noisy micrographs. Second, at present, there is no segmentation-training dataset for cryo-EM. To enable our plan, we present an automated method to generate a training dataset for segmentation using real-world data. Third, we propose a grid-based, local-maximum method to locate the particles from the probability density maps. We tested our method on simulated and real-world experimental datasets and compared PIXER with the mainstream methods RELION, DeepEM and DeepPicker to demonstrate its performance. The results indicate that, as a fully automated method, PIXER can acquire results as good as the semi-automated methods RELION and DeepEM. CONCLUSION:To our knowledge, our work is the first to address the particle-selection problem using the segmentation network concept. As a fully automated particle-selection method, PIXER can free researchers from laborious particle-selection work. Based on the results of experiments, PIXER can acquire accurate results under low-SNR conditions within minutes.
dc.description.sponsorshipAcknowledgments: The authors thank the National Supercomputer Center in Guangzhou (NSCC-GZ, China) for providing the Tianhe-2 supercomputer to support some of the intensive computations. Funding: This research was supported by the National Key Research and Development Program of China (2017YFE0103900 and 2017YFA0504702), NSFC grant nos. U1611263, U1611261, 61472397, 61502455, and 61672493 and the Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (second phase). The funding body did not play any role in the study design and collection, analysis, and interpretation of data and in writing the manuscript.
dc.publisherSpringer Nature
dc.relation.urlhttps://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2614-y
dc.rightsThis 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.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSegmentation
dc.subjectSingle-particle Analysis
dc.subjectDeep Learning
dc.subjectParticle Selection
dc.subjectCryo-electron Microscope
dc.titlePIXER: an automated particle-selection method based on segmentation using a deep neural network
dc.typeArticle
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.identifier.journalBMC Bioinformatics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionUniversity of Chinese Academy of Sciences, Beijing, China.
dc.contributor.institutionHigh Performance Computer Research Center, Institute of Computing Technology Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Haidian District, Beijing, 100190, China.
dc.contributor.institutionCenter for Biological Imaging, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing 100101, China.
dc.contributor.institutionNational Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing 100101, China.
kaust.personHan, Renmin
refterms.dateFOA2019-02-17T06:59:47Z
dc.date.published-online2019-01-18
dc.date.published-print2019-12


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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 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.