A novel constrained reconstruction model towards high-resolution subtomogram averaging | Zendy

Renmin Han | Zendy; Lun Li | Zendy; Peng Yang | Zendy; Fa Zhang | Zendy; Xin Gao | Zendy

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A novel constrained reconstruction model towards high-resolution subtomogram averaging

Author(s) -

Renmin Han,

Lun Li,

Peng Yang,

Fa Zhang,

Xin Gao

Publication year - 2019

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btz787

Subject(s) - algorithm , iterative reconstruction , computer science , tomography , artificial intelligence , wedge (geometry) , reconstruction algorithm , missing data , computer vision , pattern recognition (psychology) , mathematics , machine learning , optics , physics , geometry

Electron tomography (ET) offers a unique capacity to image biological structures in situ. However, the resolution of ET reconstructed tomograms is not comparable to that of the single-particle cryo-EM. If many copies of the object of interest are present in the tomograms, their structures can be reconstructed in the tomogram, picked, aligned and averaged to increase the signal-to-noise ratio and improve the resolution, which is known as the subtomogram averaging. To date, the resolution improvement of the subtomogram averaging is still limited because each reconstructed subtomogram is of low reconstruction quality due to the missing wedge issue.

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