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Towards parallel large-scale genomic prediction by coupling sparse and dense matrix algebra

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Bioinformatics: from nucleotids to networks (N2N)
Project
HPC-UGent: the central High Performance Computing infrastructure of Ghent University
Abstract
Genomic prediction for plant breeding requires taking into account environmental effects and variations of genetic effects across environments. The latter can be modelled by estimating the effect of each genetic marker in every possible environmental condition, which leads to a huge amount of effects to be estimated. Nonetheless, the information about these effects is only sparsely present, due to the fact that plants are only tested in a limited number of environmental conditions. In contrast, the genotypes of the plants are a dense source of information and thus the estimation of both types of effects in one single step would require as well dense as sparse matrix formalisms. This paper presents a way to efficiently apply a high performance computing infrastructure for dealing with large-scale genomic prediction settings, relying on the coupling of dense and sparse matrix algebra.
Keywords
biology computing, data analysis, parallel processing, dense matrix algebra, genomics, sparse matrices, coupling sparse, environmental condition, sparse matrix algebra, high performance computing infrastructure, Bioinformatics, sparse matrix formalism, Genomics, parallel large-scale genomic prediction, Computational modeling, Sparse matrices, Mathematical model, Predictive models, plant breeding, sparse matrix algebra, distributed computing, data analysis, genomic prediction

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Citation

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Chicago
De Coninck, Arne, Drosos Kourounis, Fabio Verbosio, Olaf Schenk, Bernard De Baets, Steven Maenhout, and Jan Fostier. 2015. “Towards Parallel Large-scale Genomic Prediction by Coupling Sparse and Dense Matrix Algebra.” In 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING (PDP 2015) , ed. Masoud Daneshtalab, Marco Aldinucci, Ville Leppänen, Johan Lilius, and Mats Brorsson, 747–750. New York, NY, USA: IEEE.
APA
De Coninck, A., Kourounis, D., Verbosio, F., Schenk, O., De Baets, B., Maenhout, S., & Fostier, J. (2015). Towards parallel large-scale genomic prediction by coupling sparse and dense matrix algebra. In M. Daneshtalab, M. Aldinucci, V. Leppänen, J. Lilius, & M. Brorsson (Eds.), 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING (PDP 2015) (pp. 747–750). Presented at the 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), New York, NY, USA: IEEE.
Vancouver
1.
De Coninck A, Kourounis D, Verbosio F, Schenk O, De Baets B, Maenhout S, et al. Towards parallel large-scale genomic prediction by coupling sparse and dense matrix algebra. In: Daneshtalab M, Aldinucci M, Leppänen V, Lilius J, Brorsson M, editors. 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING (PDP 2015) . New York, NY, USA: IEEE; 2015. p. 747–50.
MLA
De Coninck, Arne, Drosos Kourounis, Fabio Verbosio, et al. “Towards Parallel Large-scale Genomic Prediction by Coupling Sparse and Dense Matrix Algebra.” 23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING (PDP 2015) . Ed. Masoud Daneshtalab et al. New York, NY, USA: IEEE, 2015. 747–750. Print.
@inproceedings{6934376,
  abstract     = {Genomic prediction for plant breeding requires taking into account environmental effects and variations of genetic effects across environments. The latter can be modelled by estimating the effect of each genetic marker in every possible environmental condition, which leads to a huge amount of effects to be estimated. Nonetheless, the information about these effects is only sparsely present, due to the fact that plants are only tested in a limited number of environmental conditions. In contrast, the genotypes of the plants are a dense source of information and thus the estimation of both types of effects in one single step would require as well dense as sparse matrix formalisms. This paper presents a way to efficiently apply a high performance computing infrastructure for dealing with large-scale genomic prediction settings, relying on the coupling of dense and sparse matrix algebra.},
  author       = {De Coninck, Arne and Kourounis, Drosos and Verbosio, Fabio and Schenk, Olaf and De Baets, Bernard and Maenhout, Steven and Fostier, Jan},
  booktitle    = {23RD EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING (PDP 2015) },
  editor       = {Daneshtalab, Masoud and Aldinucci, Marco and Lepp{\"a}nen, Ville and Lilius, Johan and Brorsson, Mats},
  isbn         = {978-1-4799-8490-9},
  keyword      = {biology computing,data analysis,parallel processing,dense matrix algebra,genomics,sparse matrices,coupling sparse,environmental condition,sparse matrix algebra,high performance computing infrastructure,Bioinformatics,sparse matrix formalism,Genomics,parallel large-scale genomic prediction,Computational modeling,Sparse matrices,Mathematical model,Predictive models,plant breeding,sparse matrix algebra,distributed computing,data analysis,genomic prediction},
  language     = {eng},
  location     = {Turku, Finland},
  pages        = {747--750},
  publisher    = {IEEE},
  title        = {Towards parallel large-scale genomic prediction by coupling sparse and dense matrix algebra},
  url          = {http://dx.doi.org/10.1109/PDP.2015.94},
  year         = {2015},
}

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