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dc.contributor.authorHan, Renmin
dc.contributor.authorWan, Xiaohua
dc.contributor.authorWang, Zihao
dc.contributor.authorHao, Yu
dc.contributor.authorZhang, Jingrong
dc.contributor.authorChen, Yu
dc.contributor.authorGao, Xin
dc.contributor.authorLiu, Zhiyong
dc.contributor.authorRen, Fei
dc.contributor.authorSun, Fei
dc.contributor.authorZhang, Fa
dc.date.accessioned2017-07-27T11:21:47Z
dc.date.available2017-07-27T11:21:47Z
dc.date.issued2017-07-26
dc.identifier.citationHan R, Wan X, Wang Z, Hao Y, Zhang J, et al. (2017) AuTom: a novel automatic platform for electron tomography reconstruction. Journal of Structural Biology. Available: http://dx.doi.org/10.1016/j.jsb.2017.07.008.
dc.identifier.issn1047-8477
dc.identifier.pmid28756247
dc.identifier.doi10.1016/j.jsb.2017.07.008
dc.identifier.urihttp://hdl.handle.net/10754/625269
dc.description.abstractWe have developed a software package towards automatic electron tomography (ET): Automatic Tomography (AuTom). The presented package has the following characteristics: accurate alignment modules for marker-free datasets containing substantial biological structures; fully automatic alignment modules for datasets with fiducial markers; wide coverage of reconstruction methods including a new iterative method based on the compressed-sensing theory that suppresses the “missing wedge” effect; and multi-platform acceleration solutions that support faster iterative algebraic reconstruction. AuTom aims to achieve fully automatic alignment and reconstruction for electron tomography and has already been successful for a variety of datasets. AuTom also offers user-friendly interface and auxiliary designs for file management and workflow management, in which fiducial marker-based datasets and marker-free datasets are addressed with totally different subprocesses. With all of these features, AuTom can serve as a convenient and effective tool for processing in electron tomography.
dc.description.sponsorshipThanks Jose-Jesus Fernandez for opening the CTF module to us. Thanks Ce Liu and Shuangbo Zhang for the works to improve the quality of AuTom. This work was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No.XDB08030202), the National Natural Science Foundation of China (Grant No. 61232001, 61232991, 61472397, 61502455, 61672493, U1611263, U1611261), the National Key Research and Development Program of China2017YFA0504702), the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Awards No. URF/1/1976-04, URF/1/2602-01, and URF/1/3007-01, Special Program for Applied Research on Super Computation of the NSFC-Guangdong Joint Fund (the second phase), and the National Basic Research Program (973 Program) of Ministry of Science and Technology of China (2014CB910700).
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S1047847717301284
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Structural Biology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Structural Biology, [, , (2017-07-26)] DOI: 10.1016/j.jsb.2017.07.008 . © 2017. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectElectron tomography
dc.subjectAlignment
dc.subjectReconstruction
dc.subjectImage processing workflow
dc.titleAuTom: a novel automatic platform for electron tomography reconstruction
dc.typeArticle
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalJournal of Structural Biology
dc.eprint.versionPost-print
dc.contributor.institutionKey Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
dc.contributor.institutionUniversity of Chinese Academy of Sciences, Beijing, China
dc.contributor.institutionState Key Lab for Computer Architecture, Institute of Computing Technology, Chinese Academy of Sciences, 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
kaust.personGao, Xin
refterms.dateFOA2018-07-26T00:00:00Z
dc.date.published-online2017-07-26
dc.date.published-print2017-09


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