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.I documenti in SFERA sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
