Summary: Inferring population admixture from genetic data and quantifying it is a difficult but crucial task in evolutionary and conservation biology. Unfortunately state-of-the-art probabilistic approaches are computationally demanding. Effectively exploiting the computational power of modern multiprocessor systems can thus have a positive impact to Monte-Carlo-based simulation of admixture modeling. A novel parallel approach is briefly described and promising results on its MPI-based C++ implementation are reported. Availability: The software package ParLEA is freely available at http://dm.unife.it/parlea. Contact: ambra.giovannini@unife.it Supplementary information: Additional information, including instructions for installation/use the original sequential LEA code and the data used in this paper, are also available in the web site.

A novel parallel approach to the likelihood-based estimation of admixture in population genetics

GIOVANNINI, Ambra;ZANGHIRATI, Gaetano;BARBUJANI, Guido
2009

Abstract

Summary: Inferring population admixture from genetic data and quantifying it is a difficult but crucial task in evolutionary and conservation biology. Unfortunately state-of-the-art probabilistic approaches are computationally demanding. Effectively exploiting the computational power of modern multiprocessor systems can thus have a positive impact to Monte-Carlo-based simulation of admixture modeling. A novel parallel approach is briefly described and promising results on its MPI-based C++ implementation are reported. Availability: The software package ParLEA is freely available at http://dm.unife.it/parlea. Contact: ambra.giovannini@unife.it Supplementary information: Additional information, including instructions for installation/use the original sequential LEA code and the data used in this paper, are also available in the web site.
Giovannini, Ambra; Zanghirati, Gaetano; M. A., Beaumont; L., Chikhi; Barbujani, Guido
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/534766
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