Abstract
The potential for Grid technologies in applied bioinformatics is largely unexplored. We have developed a model for solving computationally demanding bioinformatics tasks in distributed Grid environments, designed to ease the usability for scientists unfamiliar with Grid computing. With a script-based implementation that uses a strategy of temporary installations of databases and existing executables on remote nodes at submission, we propose a generic solution that do not rely on predefined Grid runtime environments and that can easily be adapted to other bioinformatics tasks suitable for parallelization. This implementation has been successfully applied to whole proteome sequence similarity analyses and to genome-wide genotype simulations, where computation time was reduced from years to weeks. We conclude that computational Grid technology is a useful resource for solving high compute tasks in genetics and proteomics using existing algorithms.
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Andrade, J., Andersen, M., Berglund, L., Odeberg, J. (2007). Applications of Grid Computing in Genetics and Proteomics. In: Kågström, B., Elmroth, E., Dongarra, J., Waśniewski, J. (eds) Applied Parallel Computing. State of the Art in Scientific Computing. PARA 2006. Lecture Notes in Computer Science, vol 4699. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75755-9_96
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DOI: https://doi.org/10.1007/978-3-540-75755-9_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-75754-2
Online ISBN: 978-3-540-75755-9
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