Fine-grained alignment of cryo-electron subtomograms based on MPI parallel optimization

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-el...

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Vydané v:BMC bioinformatics Ročník 20; číslo 1; s. 443 - 13
Hlavní autori: Lü, Yongchun, Zeng, Xiangrui, Zhao, Xiaofang, Li, Shirui, Li, Hua, Gao, Xin, Xu, Min
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London BioMed Central 28.08.2019
BioMed Central Ltd
Springer Nature B.V
BMC
Predmet:
Algorithms
Alignment
Banks (Finance)
Bioinformatics
Biomedical and Life Sciences
Cellular structure
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Computer simulation
Cryo-ET
Cryoelectron microscopy
Data banks
Data loss
Data structures
Electron microscopy
Electrons
Fine-grained alignment
Image processing
Image quality
Image reconstruction
Imaging and image analysis
Life Sciences
Message passing
Methodology
Methodology Article
Methods
Microarrays
Microscopy
MPI
Optimization
Parallel processing
Parallel programming
Sag
Signal to noise ratio
Stochastic average gradient
Stochastic processes
Technology
Three-dimensional imaging
Time
Tomography
Translations
China
Cryo-ET
Fine-grained alignment
Stochastic average gradient
MPI
ISSN:1471-2105, 1471-2105
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Popis
Shrnutí: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.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-3003-2