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Jayawardena, Mahen
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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
Öppna denna publikation i ny flik eller fönster >>A Grid-Enabled Problem Solving Environment for QTL Analysis in R
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2010 (Engelska)Ingår i: Proc. 2nd International Conference on Bioinformatics and Computational Biology, Cary, NC: ISCA , 2010, s. 202-209Konferensbidrag, Publicerat paper (Refereegranskat)
Ort, förlag, år, upplaga, sidor
Cary, NC: ISCA, 2010
Nationell ämneskategori
Programvaruteknik Genetik
Identifikatorer
urn:nbn:se:uu:diva-111594 (URN)978-1-880843-76-5 (ISBN)
Projekt
eSSENCE
Tillgänglig från: 2010-01-12 Skapad: 2009-12-17 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
Jayawardena, M. (2010). An e-Science Approach to Genetic Analysis of Quantitative Traits. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
Öppna denna publikation i ny flik eller fönster >>An e-Science Approach to Genetic Analysis of Quantitative Traits
2010 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Uppsala: Acta Universitatis Upsaliensis, 2010. s. 40
Serie
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 708
Nyckelord
QTL Analysis, Grid Computing, Global Optimization, e-Science
Nationell ämneskategori
Programvaruteknik Beräkningsmatematik
Forskningsämne
Beräkningsvetenskap
Identifikatorer
urn:nbn:se:uu:diva-111597 (URN)978-91-554-7706-6 (ISBN)
Disputation
2010-02-25, Room 2446, Polacksbacken, Lägerhyddsvägen 2D, Uppsala, 10:15 (Engelska)
Opponent
Handledare
Projekt
eSSENCE
Tillgänglig från: 2010-02-02 Skapad: 2009-12-17 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
Jayawardena, M., Toor, S. & Holmgren, S. (2010). Computational and visualization tools for genetic analysis of complex traits.
Öppna denna publikation i ny flik eller fönster >>Computational and visualization tools for genetic analysis of complex traits
2010 (Engelska)Rapport (Övrigt vetenskapligt)
Serie
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2010-001
Nationell ämneskategori
Programvaruteknik Beräkningsmatematik
Identifikatorer
urn:nbn:se:uu:diva-111593 (URN)
Projekt
eSSENCE
Tillgänglig från: 2010-01-12 Skapad: 2009-12-17 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>A grid portal for genetic analysis of complex traits
2009 (Engelska)Ingår i: Proc. 32nd International Convention on Information and Communication Technology, Electronics and Microelectronics: Volume I, Rijeka, Croatia: MIPRO , 2009, s. 281-284Konferensbidrag, Publicerat paper (Refereegranskat)
Ort, förlag, år, upplaga, sidor
Rijeka, Croatia: MIPRO, 2009
Nationell ämneskategori
Programvaruteknik
Identifikatorer
urn:nbn:se:uu:diva-117772 (URN)978-953-233-044-1 (ISBN)
Tillgänglig från: 2010-02-22 Skapad: 2010-02-22 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Efficient optimization algorithms and implementations for genetic analysis of complex traits on a grid system with multicore nodes
2008 (Engelska)Konferensbidrag, Publicerat paper (Refereegranskat)
Ort, förlag, år, upplaga, sidor
Trondheim, Norway: Norwegian University of Science and Technology, 2008
Nationell ämneskategori
Datavetenskap (datalogi) Beräkningsmatematik
Identifikatorer
urn:nbn:se:uu:diva-111590 (URN)
Konferens
PARA 2008: State of the Art in Scientific and Parallel Computing
Projekt
UPMARC
Tillgänglig från: 2010-01-12 Skapad: 2009-12-17 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Grid-enabling an efficient algorithm for demanding global optimization problems in genetic analysis
2007 (Engelska)Ingår i: Proc. 3rd International Conference on e-Science and Grid Computing, Los Alamitos, CA: IEEE Computer Society, 2007, s. 205-212Konferensbidrag, Publicerat paper (Refereegranskat)
Ort, förlag, år, upplaga, sidor
Los Alamitos, CA: IEEE Computer Society, 2007
Nationell ämneskategori
Datavetenskap (datalogi) Beräkningsmatematik
Identifikatorer
urn:nbn:se:uu:diva-12617 (URN)10.1109/E-SCIENCE.2007.40 (DOI)000253614600025 ()978-0-7695-3064-2 (ISBN)
Tillgänglig från: 2008-01-08 Skapad: 2008-01-08 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
Jayawardena, M. (2007). Parallel algorithms and implementations for genetic analysis of quantitative traits. (Licentiate dissertation). Uppsala University
Öppna denna publikation i ny flik eller fönster >>Parallel algorithms and implementations for genetic analysis of quantitative traits
2007 (Engelska)Licentiatavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Uppsala University, 2007
Serie
IT licentiate theses / Uppsala University, Department of Information Technology, ISSN 1404-5117 ; 2007-005
Nationell ämneskategori
Programvaruteknik Beräkningsmatematik
Forskningsämne
Beräkningsvetenskap
Identifikatorer
urn:nbn:se:uu:diva-85815 (URN)
Handledare
Tillgänglig från: 2007-09-28 Skapad: 2007-09-11 Senast uppdaterad: 2018-01-13Bibliografiskt granskad
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
Öppna denna publikation i ny flik eller fönster >>Using parallel computing and grid systems for genetic mapping of quantitative traits
2007 (Engelska)Ingår i: Applied Parallel Computing: State of the Art in Scientific Computing, Berlin: Springer-Verlag , 2007, s. 627-636Konferensbidrag, Publicerat paper (Refereegranskat)
Ort, förlag, år, upplaga, sidor
Berlin: Springer-Verlag, 2007
Serie
Lecture Notes in Computer Science ; 4699
Nationell ämneskategori
Bioinformatik (beräkningsbiologi)
Identifikatorer
urn:nbn:se:uu:diva-11546 (URN)10.1007/978-3-540-75755-9_76 (DOI)000250904900076 ()978-3-540-75754-2 (ISBN)
Tillgänglig från: 2007-09-26 Skapad: 2007-09-26 Senast uppdaterad: 2018-01-12Bibliografiskt granskad
Jayawardena, M., Ljungberg, K. & Holmgren, S. (2005). Using parallel computing and grid systems for genetic mapping of multifactorial traits.
Öppna denna publikation i ny flik eller fönster >>Using parallel computing and grid systems for genetic mapping of multifactorial traits
2005 (Engelska)Rapport (Övrigt vetenskapligt)
Serie
Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2005-036
Nationell ämneskategori
Bioinformatik (beräkningsbiologi)
Identifikatorer
urn:nbn:se:uu:diva-76703 (URN)
Tillgänglig från: 2007-02-05 Skapad: 2007-02-05 Senast uppdaterad: 2018-01-13Bibliografiskt granskad
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