uu.seUppsala University Publications
Change search
Link to record
Permanent link

Direct link
BETA
Jayawardena, Mahen
Publications (9 of 9) Show all publications
Jayawardena, M., Nettelblad, C., Toor, S., Östberg, P.-O., Elmroth, E. & Holmgren, S. (2010). A Grid-Enabled Problem Solving Environment for QTL Analysis in R. In: Proc. 2nd International Conference on Bioinformatics and Computational Biology (pp. 202-209). Cary, NC: ISCA
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
Jayawardena, M. (2010). An e-Science Approach to Genetic Analysis of Quantitative Traits. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
Open this publication in new window or tab >>An e-Science Approach to Genetic Analysis of Quantitative Traits
2010 (English)Doctoral 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.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2010. p. 40
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 708
Keywords
QTL Analysis, Grid Computing, Global Optimization, e-Science
National Category
Software Engineering Computational Mathematics
Research subject
Scientific Computing
Identifiers
urn:nbn:se:uu:diva-111597 (URN)978-91-554-7706-6 (ISBN)
Public defence
2010-02-25, Room 2446, Polacksbacken, Lägerhyddsvägen 2D, Uppsala, 10:15 (English)
Opponent
Supervisors
Projects
eSSENCE
Available from: 2010-02-02 Created: 2009-12-17 Last updated: 2018-01-12Bibliographically approved
Jayawardena, M., Toor, S. & Holmgren, S. (2010). 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
Jayawardena, M., Toor, S. & Holmgren, S. (2009). A grid portal for genetic analysis of complex traits. In: Proc. 32nd International Convention on Information and Communication Technology, Electronics and Microelectronics: Volume I (pp. 281-284). Rijeka, Croatia: MIPRO
Open this publication in new window or tab >>A grid portal for genetic analysis of complex traits
2009 (English)In: Proc. 32nd International Convention on Information and Communication Technology, Electronics and Microelectronics: Volume I, Rijeka, Croatia: MIPRO , 2009, p. 281-284Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Rijeka, Croatia: MIPRO, 2009
National Category
Software Engineering
Identifiers
urn:nbn:se:uu:diva-117772 (URN)978-953-233-044-1 (ISBN)
Available from: 2010-02-22 Created: 2010-02-22 Last updated: 2018-01-12Bibliographically approved
Jayawardena, M., Löf, H. & Holmgren, S. (2008). Efficient optimization algorithms and implementations for genetic analysis of complex traits on a grid system with multicore nodes. Paper presented at PARA 2008: State of the Art in Scientific and Parallel Computing. Trondheim, Norway: Norwegian University of Science and Technology
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
Jayawardena, M. & Holmgren, S. (2007). Grid-enabling an efficient algorithm for demanding global optimization problems in genetic analysis. In: Proc. 3rd International Conference on e-Science and Grid Computing: (pp. 205-212). Los Alamitos, CA: IEEE Computer Society
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
Jayawardena, M. (2007). Parallel algorithms and implementations for genetic analysis of quantitative traits. (Licentiate dissertation). Uppsala University
Open this publication in new window or tab >>Parallel algorithms and implementations for genetic analysis of quantitative traits
2007 (English)Licentiate 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.

Place, publisher, year, edition, pages
Uppsala University, 2007
Series
Information technology licentiate theses: Licentiate theses from the Department of Information Technology, ISSN 1404-5117 ; 2007-005
National Category
Software Engineering Computational Mathematics
Research subject
Scientific Computing
Identifiers
urn:nbn:se:uu:diva-85815 (URN)
Supervisors
Available from: 2007-09-28 Created: 2007-09-11 Last updated: 2018-01-13Bibliographically approved
Jayawardena, M., Ljungberg, K. & Holmgren, S. (2007). Using parallel computing and grid systems for genetic mapping of quantitative traits. In: Applied Parallel Computing: State of the Art in Scientific Computing (pp. 627-636). Berlin: Springer-Verlag
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
Jayawardena, M., Ljungberg, K. & Holmgren, S. (2005). Using parallel computing and grid systems for genetic mapping of multifactorial traits.
Open this publication in new window or tab >>Using parallel computing and grid systems for genetic mapping of multifactorial traits
2005 (English)Report (Other academic)
Series
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2005-036
National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:uu:diva-76703 (URN)
Available from: 2007-02-05 Created: 2007-02-05 Last updated: 2018-01-13Bibliographically approved
Organisations

Search in DiVA

Show all publications