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    AuTom: a novel automatic platform for electron tomography reconstruction

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    1-s2.0-S1047847717301284-main.pdf
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    Description:
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    Type
    Article
    Authors
    Han, Renmin
    Wan, Xiaohua
    Wang, Zihao
    Hao, Yu
    Zhang, Jingrong
    Chen, Yu
    Gao, Xin cc
    Liu, Zhiyong
    Ren, Fei
    Sun, Fei
    Zhang, Fa
    KAUST Department
    Computational Bioscience Research Center (CBRC)
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-07-26
    Permanent link to this record
    http://hdl.handle.net/10754/625269
    
    Metadata
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    Abstract
    We 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.
    Citation
    Han 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.
    Sponsors
    Thanks 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).
    Publisher
    Elsevier BV
    Journal
    Journal of Structural Biology
    ISSN
    1047-8477
    DOI
    10.1016/j.jsb.2017.07.008
    PubMed ID
    28756247
    Additional Links
    http://www.sciencedirect.com/science/article/pii/S1047847717301284
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.jsb.2017.07.008
    Scopus Count
    Collections
    Articles; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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