A novel constrained reconstruction model towards high-resolution subtomogram averaging

doi:10.1093/bioinformatics/btz787

	A novel constrained reconstruction model towards high-resolution subtomogram averaging
	Han, Renmin 1; Li, Lun 2,3; Yang, Peng 1; Zhang, Fa 2; Gao, Xin 1
	2021-06-01
发表期刊	BIOINFORMATICS
ISSN	1367-4803
卷号	37 期号:11 页码:1616-1626
摘要	Motivation: 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. Results: In this article, we propose a novel computational model, the constrained reconstruction model (CRM), to better recover the information from the multiple subtomograms and compensate for the missing wedge issue in each of them. CRM is supposed to produce a refined reconstruction in the final turn of subtomogram averaging after alignment, instead of directly taking the average. We first formulate the averaging method and our CRM as linear systems, and prove that the solution space of CRM is no larger, and in practice much smaller, than that of the averaging method. We then propose a sparse Kaczmarz algorithm to solve the formulated CRM, and further extend the solution to the simultaneous algebraic reconstruction technique (SART). Experimental results demonstrate that CRM can significantly alleviate the missing wedge issue and improve the final reconstruction quality. In addition, our model is robust to the number of images in each tilt series, the tilt range and the noise level.
DOI	10.1093/bioinformatics/btz787
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFE0103900] ; National Key Research and Development Program of China[2017YFA0504702] ; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR)[FCC/1/1976-18-01] ; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR)[FCC/1/1976-23-01] ; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR)[FCC/1/1976-25-01] ; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR)[FCC/1/1976-26-01] ; King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR)[FCS/1/4102-02-01] ; NSFC[U1611263] ; NSFC[U1611261] ; NSFC[61932018] ; NSFC[61672493]
WOS研究方向	Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS类目	Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS记录号	WOS:000703906200023
出版者	OXFORD UNIV PRESS
引用统计	被引频次：2[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/17080
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Fa; Gao, Xin
作者单位	1.King Abdullah Univ Sci & Technol KAUST, Computat Biosci Res Ctr CBRC, Comp Elect & Math Sci & Engn CEMSE Div, Thuwal 239556900, Saudi Arabia 2.Chinese Acad Sci, High Performance Comp Res Ctr, Inst Comp Technol, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Han, Renmin,Li, Lun,Yang, Peng,et al. A novel constrained reconstruction model towards high-resolution subtomogram averaging[J]. BIOINFORMATICS,2021,37(11):1616-1626.
APA	Han, Renmin,Li, Lun,Yang, Peng,Zhang, Fa,&Gao, Xin.(2021).A novel constrained reconstruction model towards high-resolution subtomogram averaging.BIOINFORMATICS,37(11),1616-1626.
MLA	Han, Renmin,et al."A novel constrained reconstruction model towards high-resolution subtomogram averaging".BIOINFORMATICS 37.11(2021):1616-1626.