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- Title
Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization.
- Authors
Lü, Yongchun; Zeng, Xiangrui; Zhao, Xiaofang; Li, Shirui; Li, Hua; Gao, Xin; Xu, Min
- Abstract
Background: Cryo-electron tomography (Cryo-ET) is an imaging technique used to generate three-dimensional structures of cellular macromolecule complexes in their native environment. Due to developing cryo-electron microscopy technology, the image quality of three-dimensional reconstruction of cryo-electron tomography has greatly improved. However, cryo-ET images are characterized by low resolution, partial data loss and low signal-to-noise ratio (SNR). In order to tackle these challenges and improve resolution, a large number of subtomograms containing the same structure needs to be aligned and averaged. Existing methods for refining and aligning subtomograms are still highly time-consuming, requiring many computationally intensive processing steps (i.e. the rotations and translations of subtomograms in three-dimensional space). Results: In this article, we propose a Stochastic Average Gradient (SAG) fine-grained alignment method for optimizing the sum of dissimilarity measure in real space. We introduce a Message Passing Interface (MPI) parallel programming model in order to explore further speedup. Conclusions: We compare our stochastic average gradient fine-grained alignment algorithm with two baseline methods, high-precision alignment and fast alignment. Our SAG fine-grained alignment algorithm is much faster than the two baseline methods. Results on simulated data of GroEL from the Protein Data Bank (PDB ID:1KP8) showed that our parallel SAG-based fine-grained alignment method could achieve close-to-optimal rigid transformations with higher precision than both high-precision alignment and fast alignment at a low SNR (SNR=0.003) with tilt angle range ±60∘ or ±40∘. For the experimental subtomograms data structures of GroEL and GroEL/GroES complexes, our parallel SAG-based fine-grained alignment can achieve higher precision and fewer iterations to converge than the two baseline methods.
- Subjects
SCREEN Actors Guild; PARALLEL programming; MESSAGE passing (Computer science); DATABASES; DATA structures; CELL anatomy; SIGNAL-to-noise ratio
- Publication
BMC Bioinformatics, 2019, Vol 20, Issue 1, pN.PAG
- ISSN
1471-2105
- Publication type
Article
- DOI
10.1186/s12859-019-3003-2