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  • 1.
    Jayawardena, Mahen
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    An e-Science Approach to Genetic Analysis of Quantitative Traits2010Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Many important traits in plants, animals and humans are quantitative, and most such traits are generally believed to be affected by multiple genetic loci. Standard computational tools for mapping of quantitative traits (i.e. for finding Quantitative Trait Loci, QTL, in the genome) use linear regression models for relating the observed phenotypes to the genetic composition of individuals in an experimental population. Using these tools to simultaneously search for multiple QTL is computationally demanding. The main reason for this is the complex optimization landscape for the multidimensional global optimization problems that must be solved. This thesis describes parallel algorithms, implementations and tools for simultaneous mapping of several QTL. These new computational tools enable genetic analysis exploiting new classes of multidimensional statistical models, potentially resulting in interesting results in genetics.

    We first describe how the standard, brute-force algorithm for global optimization in QTL analysis is parallelized and implemented on a grid system. Then, we also present a parallelized version of the more elaborate global optimization algorithm DIRECT and show how this can be efficiently deployed and used on grid systems and other loosely-coupled architectures. The parallel DIRECT scheme is further developed to exploit both coarse-grained parallelism in grid systems or clusters as well as fine-grained, tightly-coupled parallelism in multi-core nodes. The results show that excellent speedup and performance can be archived on grid systems and clusters, even when using a tightly-coupled algorithm such as DIRECT. Finally, we provide two distinctly different front-ends for our code. One is a grid portal providing a graphical front-end suitable for novice users and standard forms of QTL analysis. The other is a prototype of an R-based grid-enabled problem solving environment. Both of these front-ends can, after some further refinement, be utilized by geneticists for performing multidimensional genetic analysis of quantitative traits on a regular basis.

    List of papers
    1. Using parallel computing and grid systems for genetic mapping of quantitative traits
    Open this publication in new window or tab >>Using parallel computing and grid systems for genetic mapping of quantitative traits
    2007 (English)In: Applied Parallel Computing: State of the Art in Scientific Computing, Berlin: Springer-Verlag , 2007, p. 627-636Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Berlin: Springer-Verlag, 2007
    Series
    Lecture Notes in Computer Science ; 4699
    National Category
    Bioinformatics (Computational Biology)
    Identifiers
    urn:nbn:se:uu:diva-11546 (URN)10.1007/978-3-540-75755-9_76 (DOI)000250904900076 ()978-3-540-75754-2 (ISBN)
    Available from: 2007-09-26 Created: 2007-09-26 Last updated: 2018-01-12Bibliographically approved
    2. Grid-enabling an efficient algorithm for demanding global optimization problems in genetic analysis
    Open this publication in new window or tab >>Grid-enabling an efficient algorithm for demanding global optimization problems in genetic analysis
    2007 (English)In: Proc. 3rd International Conference on e-Science and Grid Computing, Los Alamitos, CA: IEEE Computer Society, 2007, p. 205-212Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Los Alamitos, CA: IEEE Computer Society, 2007
    National Category
    Computer Sciences Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-12617 (URN)10.1109/E-SCIENCE.2007.40 (DOI)000253614600025 ()978-0-7695-3064-2 (ISBN)
    Available from: 2008-01-08 Created: 2008-01-08 Last updated: 2018-01-12Bibliographically approved
    3. Efficient optimization algorithms and implementations for genetic analysis of complex traits on a grid system with multicore nodes
    Open this publication in new window or tab >>Efficient optimization algorithms and implementations for genetic analysis of complex traits on a grid system with multicore nodes
    2008 (English)Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Trondheim, Norway: Norwegian University of Science and Technology, 2008
    National Category
    Computer Sciences Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-111590 (URN)
    Conference
    PARA 2008: State of the Art in Scientific and Parallel Computing
    Projects
    UPMARC
    Available from: 2010-01-12 Created: 2009-12-17 Last updated: 2018-01-12Bibliographically approved
    4. Computational and visualization tools for genetic analysis of complex traits
    Open this publication in new window or tab >>Computational and visualization tools for genetic analysis of complex traits
    2010 (English)Report (Other academic)
    Series
    Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2010-001
    National Category
    Software Engineering Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-111593 (URN)
    Projects
    eSSENCE
    Available from: 2010-01-12 Created: 2009-12-17 Last updated: 2018-01-12Bibliographically approved
    5. A Grid-Enabled Problem Solving Environment for QTL Analysis in R
    Open this publication in new window or tab >>A Grid-Enabled Problem Solving Environment for QTL Analysis in R
    Show others...
    2010 (English)In: Proc. 2nd International Conference on Bioinformatics and Computational Biology, Cary, NC: ISCA , 2010, p. 202-209Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Cary, NC: ISCA, 2010
    National Category
    Software Engineering Genetics
    Identifiers
    urn:nbn:se:uu:diva-111594 (URN)978-1-880843-76-5 (ISBN)
    Projects
    eSSENCE
    Available from: 2010-01-12 Created: 2009-12-17 Last updated: 2018-01-12Bibliographically approved
  • 2.
    Jayawardena, Mahen
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Parallel algorithms and implementations for genetic analysis of quantitative traits2007Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Many important traits in plants, animals and humans are quantitative, and most such traits are generally believed to be regulated by multiple genetic loci. Standard computational tools for analysis of quantitative traits use linear regression models for relating the observed phenotypes to the genetic composition of individuals in a population. However, using these tools to simultaneously search for multiple genetic loci is very computationally demanding. The main reason for this is the complex nature of the optimization landscape for the multidimensional global optimization problems that must be solved. This thesis describes parallel algorithms and implementation techniques for such optimization problems. The new computational tools will eventually enable genetic analysis exploiting new classes of multidimensional statistical models, potentially resulting in interesting results in genetics.

    We first describe how the algorithm used for global optimization in the standard, serial software is parallelized and implemented on a grid system. Then, we also describe a parallelized version of the more elaborate global optimization algorithm DIRECT and show how this can be deployed on grid systems and other loosely-coupled architectures. The parallel DIRECT scheme is further developed to exploit both coarse-grained parallelism in grid or clusters as well as fine-grained, tightly-coupled parallelism in multi-core nodes. The results show that excellent speedup and performance can be archived on grid systems and clusters, even when using a tightly-coupled algorithms such as DIRECT. Finally, a pilot implementation of a grid portal providing a graphical front-end for our code is implemented. After some further development, this portal can be utilized by geneticists for performing multidimensional genetic analysis of quantitative traits on a regular basis.

  • 3.
    Jayawardena, Mahen
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Grid-enabling an efficient algorithm for demanding global optimization problems in genetic analysis2007In: Proc. 3rd International Conference on e-Science and Grid Computing, Los Alamitos, CA: IEEE Computer Society, 2007, p. 205-212Conference paper (Refereed)
  • 4.
    Jayawardena, Mahen
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Ljungberg, Kajsa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Using parallel computing and grid systems for genetic mapping of multifactorial traits2005Report (Other academic)
  • 5.
    Jayawardena, Mahen
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Ljungberg, Kajsa
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Using parallel computing and grid systems for genetic mapping of quantitative traits2007In: Applied Parallel Computing: State of the Art in Scientific Computing, Berlin: Springer-Verlag , 2007, p. 627-636Conference paper (Refereed)
  • 6.
    Jayawardena, Mahen
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Löf, Henrik
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Efficient optimization algorithms and implementations for genetic analysis of complex traits on a grid system with multicore nodes2008Conference paper (Refereed)
  • 7.
    Jayawardena, Mahen
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Nettelblad, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Östberg, Per-Olov
    Elmroth, Erik
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    A Grid-Enabled Problem Solving Environment for QTL Analysis in R2010In: Proc. 2nd International Conference on Bioinformatics and Computational Biology, Cary, NC: ISCA , 2010, p. 202-209Conference paper (Refereed)
  • 8.
    Jayawardena, Mahen
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    A grid portal for genetic analysis of complex traits2009In: Proc. 32nd International Convention on Information and Communication Technology, Electronics and Microelectronics: Volume I, Rijeka, Croatia: MIPRO , 2009, p. 281-284Conference paper (Refereed)
  • 9.
    Jayawardena, Mahen
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Computational and visualization tools for genetic analysis of complex traits2010Report (Other academic)
1 - 9 of 9
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  • ieee
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More styles
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
More languages
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