TY - JOUR
T1 - Spark-based parallel calculation of 3D fourier shell correlation for macromolecule structure local resolution estimation
AU - Lü, Yongchun
AU - Zeng, Xiangrui
AU - Tian, Xinhui
AU - Shi, Xiao
AU - Wang, Hui
AU - Zheng, Xiaohui
AU - Liu, Xiaodong
AU - Zhao, Xiaofang
AU - Gao, Xin
AU - Xu, Min
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: We thank Dr. Fei Sun for fruitful discussions and suggestions. We thank Dr. Haiyang Li and Mr. Shuguang Zhao for technical assistance. We thank Wenlin Liu and Chengrui Wang for code discussions. We thank Dr. Yan Jin and Alex Singh for revising the English writing.
PY - 2020/9/17
Y1 - 2020/9/17
N2 - Abstract
Background
Resolution estimation is the main evaluation criteria for the reconstruction of macromolecular 3D structure in the field of cryoelectron microscopy (cryo-EM). At present, there are many methods to evaluate the 3D resolution for reconstructed macromolecular structures from Single Particle Analysis (SPA) in cryo-EM and subtomogram averaging (SA) in electron cryotomography (cryo-ET). As global methods, they measure the resolution of the structure as a whole, but they are inaccurate in detecting subtle local changes of reconstruction. In order to detect the subtle changes of reconstruction of SPA and SA, a few local resolution methods are proposed. The mainstream local resolution evaluation methods are based on local Fourier shell correlation (FSC), which is computationally intensive. However, the existing resolution evaluation methods are based on multi-threading implementation on a single computer with very poor scalability.
Results
This paper proposes a new fine-grained 3D array partition method by key-value format in Spark. Our method first converts 3D images to key-value data (K-V). Then the K-V data is used for 3D array partitioning and data exchange in parallel. So Spark-based distributed parallel computing framework can solve the above scalability problem. In this distributed computing framework, all 3D local FSC tasks are simultaneously calculated across multiple nodes in a computer cluster. Through the calculation of experimental data, 3D local resolution evaluation algorithm based on Spark fine-grained 3D array partition has a magnitude change in computing speed compared with the mainstream FSC algorithm under the condition that the accuracy remains unchanged, and has better fault tolerance and scalability.
Conclusions
In this paper, we proposed a K-V format based fine-grained 3D array partition method in Spark to parallel calculating 3D FSC for getting a 3D local resolution density map. 3D local resolution density map evaluates the three-dimensional density maps reconstructed from single particle analysis and subtomogram averaging. Our proposed method can significantly increase the speed of the 3D local resolution evaluation, which is important for the efficient detection of subtle variations among reconstructed macromolecular structures.
AB - Abstract
Background
Resolution estimation is the main evaluation criteria for the reconstruction of macromolecular 3D structure in the field of cryoelectron microscopy (cryo-EM). At present, there are many methods to evaluate the 3D resolution for reconstructed macromolecular structures from Single Particle Analysis (SPA) in cryo-EM and subtomogram averaging (SA) in electron cryotomography (cryo-ET). As global methods, they measure the resolution of the structure as a whole, but they are inaccurate in detecting subtle local changes of reconstruction. In order to detect the subtle changes of reconstruction of SPA and SA, a few local resolution methods are proposed. The mainstream local resolution evaluation methods are based on local Fourier shell correlation (FSC), which is computationally intensive. However, the existing resolution evaluation methods are based on multi-threading implementation on a single computer with very poor scalability.
Results
This paper proposes a new fine-grained 3D array partition method by key-value format in Spark. Our method first converts 3D images to key-value data (K-V). Then the K-V data is used for 3D array partitioning and data exchange in parallel. So Spark-based distributed parallel computing framework can solve the above scalability problem. In this distributed computing framework, all 3D local FSC tasks are simultaneously calculated across multiple nodes in a computer cluster. Through the calculation of experimental data, 3D local resolution evaluation algorithm based on Spark fine-grained 3D array partition has a magnitude change in computing speed compared with the mainstream FSC algorithm under the condition that the accuracy remains unchanged, and has better fault tolerance and scalability.
Conclusions
In this paper, we proposed a K-V format based fine-grained 3D array partition method in Spark to parallel calculating 3D FSC for getting a 3D local resolution density map. 3D local resolution density map evaluates the three-dimensional density maps reconstructed from single particle analysis and subtomogram averaging. Our proposed method can significantly increase the speed of the 3D local resolution evaluation, which is important for the efficient detection of subtle variations among reconstructed macromolecular structures.
UR - http://hdl.handle.net/10754/665343
UR - https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-03680-6
UR - http://www.scopus.com/inward/record.url?scp=85091127533&partnerID=8YFLogxK
U2 - 10.1186/s12859-020-03680-6
DO - 10.1186/s12859-020-03680-6
M3 - Article
C2 - 32938398
SN - 1471-2105
VL - 21
JO - BMC Bioinformatics
JF - BMC Bioinformatics
IS - S13
ER -