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  BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images

Cossio, P., Rohr, D., Baruffa, F., Rampp, M., Lindenstruth, V., & Hummer, G. (2017). BioEM: GPU-accelerated computing of Bayesian inference of electron microscopy images. Computer Physics Communications, 210(01), 163-171. doi:10.1016/j.cpc.2016.09.014.

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Genre: Journal Article

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 Creators:
Cossio, Pilar1, Author              
Rohr, David2, Author
Baruffa, Fabio3, 4, Author              
Rampp, Markus3, Author              
Lindenstruth, Volker2, Author
Hummer, Gerhard1, Author              
Affiliations:
1Department of Theoretical Biophysics, Max Planck Institute of Biophysics, Max Planck Society, ou_2068292              
2Frankfurt Institute for Advanced Studies, Goethe University Frankfurt, Ruth-Moufang-Str. 1, 60438 Frankfurt, Germany, ou_persistent22              
3Max Planck Computing and Data Facility, Max Planck Society, ou_2364734              
4Leibniz Supercomputing Centre of the Bavarian Academy of Sciences and Humanities, Boltzmann Str. 1, 85748 Garching, Germany, ou_persistent22              

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Free keywords: Image analysis; Electron microscopy; Bayesian inference; Dynamic; Heterogeneous
 Abstract: In cryo-electron microscopy (EM), molecular structures are determined from large numbers of projection images of individual particles. To harness the full power of this single-molecule information, we use the Bayesian inference of EM (BioEM) formalism. By ranking structural models using posterior probabilities calculated for individual images, BioEM in principle addresses the challenge of working with highly dynamic or heterogeneous systems not easily handled in traditional EM reconstruction. However, the calculation of these posteriors for large numbers of particles and models is computationally demanding. Here we present highly parallelized, GPU-accelerated computer software that performs this task efficiently. Our flexible formulation employs CUDA, OpenMP, and MPI parallelization combined with both CPU and GPU computing. The resulting BioEM software scales nearly ideally both on pure CPU and on CPU+GPU architectures, thus enabling Bayesian analysis of tens of thousands of images in a reasonable time. The general mathematical framework and robust algorithms are not limited to cryo-electron microscopy but can be generalized for electron tomography and other imaging experiments.

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Language(s): eng - English
 Dates: 2016-06-212016-09-192016-10-032017-01
 Publication Status: Published in print
 Pages: 9
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.cpc.2016.09.014
 Degree: -

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Title: Computer Physics Communications
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier B.V.
Pages: - Volume / Issue: 210 (01) Sequence Number: - Start / End Page: 163 - 171 Identifier: ISSN: 0010-4655
CoNE: https://pure.mpg.de/cone/journals/resource/954925392326