A novel constrained reconstruction model towards high-resolution subtomogram averaging
文献类型：期刊论文
| 作者 | Han, Renmin1; Li, Lun2,3; Yang, Peng1; Zhang, Fa2; Gao, Xin1 |
| 刊名 | BIOINFORMATICS
 |
| 出版日期 | 2021-06-01 |
| 卷号 | 37期号:11页码:1616-1626 |
| ISSN号 | 1367-4803 |
| DOI | 10.1093/bioinformatics/btz787 |
| 英文摘要 | 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. |
| 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 |
| 语种 | 英语 |
| WOS记录号 | WOS:000703906200023 |
| 出版者 | OXFORD UNIV PRESS |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/17079]  |
| 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. |
入库方式： OAI收割
来源：计算技术研究所
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