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Large-Scale Co-Phylogenetic Analysis on the Grid

Large-Scale Co-Phylogenetic Analysis on the Grid
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Author(s): Heinz Stockinger (Swiss Institute of Bioinformatics, Switzerland), Alexander F. Auch (University of Tübingen, Germany), Markus Göker (University of Tübingen, Germany), Jan Meier-Kolthoff (University of Tübingen, Germany) and Alexandros Stamatakis (Ludwig-Maximilians-University Munich, Germany)
Copyright: 2009
Volume: 1
Issue: 1
Pages: 16
Source title: International Journal of Grid and High Performance Computing (IJGHPC)
Editor(s)-in-Chief: Emmanuel Udoh (Sullivan University, USA), Ching-Hsien Hsu (Chung Hua University, Taiwan) and Mohammad Khan (Sullivan University, USA)
DOI: 10.4018/jghpc.2009010104

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Abstract

Phylogenetic data analysis represents an extremely compute-intensive area of Bioinformatics and thus requires high-performance technologies. Another compute- and memory-intensive problem is that of hostparasite co-phylogenetic analysis: given two phylogenetic trees, one for the hosts (e.g., mammals) and one for their respective parasites (e.g., lice) the question arises whether host and parasite trees are more similar to each other than expected by chance alone. CopyCat is an easy-to-use tool that allows biologists to conduct such co-phylogenetic studies within an elaborate statistical framework based on the highly optimized sequential and parallel A xParafit program. We have developed enhanced versions of these tools that efficiently exploit a Grid environment and therefore facilitate large-scale data analyses. Furthermore, we developed a freely accessible client tool that provides co-phylogenetic analysis capabilities. Since the computational bulk of the problem is embarrassingly parallel, it fits well to a computational Grid and reduces the response time of large scale analyses.

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