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- Jayawardena, Mahen, et al.
(author)
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Using parallel computing and grid systems for genetic mapping of multifactorial traits
- 2005
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Reports (other academic/artistic)abstract
- We present a flexible parallel implementation of the exhaustive grid search algorithm for multidimensional QTL mapping problems. A generic, parallel algorithm is presented and a two-level scheme is introduced for partitioning the work corresponding to the independent computational tasks in the algorithm. At the outer level, a static block-cyclic partitioning is used, and at the inner level a dynamic pool-of-tasks model is used. The implementation of the parallelism at the outer level is performed using scripts, while MPI is used at the inner level. By comparing to results from the SweGrid system to those obtained using a shared memory server, we show that this type of application is highly suitable for execution in a grid framework.
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| 4. |
- Ljungberg, Kajsa B, et al.
(author)
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Computational Modelling of Inhibitor Binding to Human Thrombin
- 2001
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In: Eur. J. Pharm. Sci.. ; 12:4, s. 441-446
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Journal article (peer-reviewed)abstract
- Thrombin is an essential protein involved in blood clot formation and an important clinical target, since disturbances of the coagulation process cause serious cardiovascular diseases such as thrombosis. Here we evaluate the performance of a molecular dynamics based method for predicting the binding affinities of different types ofhuman thrombin inhibitors. Far a series of eight ligands the method ranks their relative affinities reasonably well. The binding free energy difference between high and low affinity representatives in the test set is quantitatively reproduced, as well as the stereospecificity for a chiral inhibitor. The original parametrisation of this linear interaction energy method requires the addition of a constant energy term in the case of thrombin. This yields a mean unsigned error of 0.68 kcal/mol for the absolute binding free energies. This type of approach is also useful for elucidating three-dimensional structure-activity relationships in terms ofmicroscopic interactions of the ligands with the solvated enzyme.
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| 5. |
- Ljungberg, Kajsa, et al.
(author)
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Efficient Algorithms for Multi-Dimensional Global Optimization in Genetic Mapping of Complex Traits
- 2010
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In: Advances and Applications in Bioinformatics and Chemistry. - 1178-6949. ; 3:1, s. 75-88
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Journal article (peer-reviewed)abstract
- We present a two-phase strategy for optimizing a multidimensional, nonconvex function arising during genetic mapping of quantitative traits. Such traits are believed to be affected by multiple so called quantitative trait loci (QTL), and searching for d QTL results in a d-dimensional optimization problem with a large number of local optima. We combine the global algorithm DIRECT with a number of local optimization methods that accelerate the final convergence, and adapt the algorithms to problem-specific features. We also improve the evaluation of the QTL mapping objective function to enable exploitation of the smoothness properties of the optimization landscape. Our best two-phase method is demonstrated to be accurate in at least six dimensions and up to ten times faster than currently used QTL mapping algorithms.
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| 6. |
- Ljungberg, Kajsa, et al.
(author)
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Efficient algorithms for multi-dimensional global optimization in genetic mapping of complex traits
- 2005
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Reports (other academic/artistic)abstract
- We present a two-phase strategy for optimizing a multi-dimensional, non-convex function arising during genetic mapping of quantitative traits. Such traits are believed to be affected by multiple so called QTL, and searching for d QTL results in a d-dimensional optimization problem with a large number of local optima. We combine the global algorithm DIRECT of Jones et al. with a number of local optimization methods that accelerate the final convergence, and adapt the algorithms to problem-specific features. We also improve the evaluation of the QTL mapping objective function to enable exploitation of the smoothness properties of the optimization landscape. Our best two-phase method is demonstrated to be accurate in at least six dimensions and up to ten times faster than currently used QTL mapping algorithms.
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| 8. |
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| 9. |
- Ljungberg, Kajsa
(author)
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Efficient evaluation of the residual sum of squares for quantitative trait locus models in the case of complete marker genotype information
- 2005
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Reports (other academic/artistic)abstract
- Motivation: A core computation of many popular quantitative trait locus, QTL, mapping methods is determining the residual sum of squares, RSS, for a regression of trait values on (pseudo-)marker genotypes. A single evaluation is easily performed using the standard method QR factorization, but together the RSS computations take considerable time and often constitute the major part of the computational effort.Results: We present an algorithm for RSS evaluation that is mathematically equivalent to evaluation via QR factorization but 10-100 times faster depending on the model and data dimensions. It can be used for all standard QTL models. Our method opens the possibility for more detailed data analysis and more extensive model comparisons.Availability: C code, detailed derivations and general implementation strategies are available from the author on request.
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| 10. |
- Ljungberg, Kajsa, et al.
(author)
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Efficient kernel algorithms for QTL mapping problems
- 2002
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Reports (other academic/artistic)abstract
- The advent of sophisticated and powerful methods for molecular genetics pushes the need for efficient methods for data analysis. Advanced algorithms are necessary for extracting all possible information from laboriously obtained data sets. We present a general linear algebra framework for QTL mapping, applicable to many commonly used methods, using both linear regression and maximum likelihood estimation. The formulation simplifies future comparisons between and analyses of the methods. We show how the common structure of QTL analysis models can be used to improve the kernel algorithms, drastically reducing the computational effort while retaining the original analysis results. We have evaluated our new algorithms on data sets originating from two large F2 populations of domestic animals. Using an updating approach, we show that 1-3 orders of magnitude reduction in computational demand can be achieved for matrix factorizations. For interval mapping/composite interval mapping settings using a maximum likelihood model, we also show how to use the original EM algorithm instead of the ECM approximation, significantly improving the convergence and introducing an additional reduction in the computational time. The algorithmic improvements makes it feasible to perform analyses previously deemed impractical or even impossible. For example, using the new algorithms it is reasonable to perform permutation testing using exhaustive search on populations of 200 individuals for fully epistatic two-QTL models with a large number of parameters.
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