Large-Scale Co-Phylogenetic Analysis on the Grid

Large-Scale Co-Phylogenetic Analysis on the Grid

Heinz Stockinger (Swiss Institute of Bioinformatics, Switzerland), Alexander Auch (University of Tuebingen, Germany), Markus Goeker (University of Tuebingen, Germany), Jan Meier-Kolthoff (University of Tuebingen, Germany) and Alexandros Stamatakis (Ludwig-Maximilians-University, Germany)
DOI: 10.4018/978-1-60566-374-6.ch035
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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 host-parasite 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 AxParafit 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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Introduction

The generation of novel insights in many scientific domains such as biology, physics, or chemistry increasingly relies on compute-intensive applications that require high-performance or large-scale, distributed high-throughput computing technology and infrastructure. In the discipline of bioinformatics, biological insight is typically generated via data analysis pipelines that use a plethora of distinct and highly specialized tools. Most commonly, bioinformaticians and biologists collaborate to analyze data extracted from large databases containing DNA and/or protein data in order to study, e.g., the function of living beings, the effect and influence of diseases and defects, or their evolutionary history. Early “classic” bioinformatics tools, such as CLUSTALW (Thompson et al., 1994) or BLAST (Altschul et al., 1997) that have been ported to Grid computing environments deal with biological sequence search, analysis, and comparison. Typically, these programs are embarrassingly parallel and therefore represent ideal candidate applications for Grid computing environments (Stockinger et al., 2006).

The study of the genome represents a way to obtain new insight and extract novel knowledge about living beings. In particular, stand-alone phylogenetic analyses have many important applications in biological and medical research. Applications range from predicting the development of emerging infectious diseases (Salzberg et al., 2007), over the study of Papillomavirus evolution that is associated with cervical cancer (Gottschling et al., 2007), to the determination of the common origin of Caribbean frogs (Heinicke et al., 2007).

Recent years have witnessed significant progress in the field of stand-alone phylogeny reconstruction algorithms, which represent an NP-complete optimization problem (Chor and Tuller, 2005), with the release of programs such as TNT (Goloboff, 1999), RAxML (Stamatakis, 2006), MrBayes (Ronquist and Huelsenbeck, 2003) or GARLI (Zwickl, 2006). Because of the continuous explosive accumulation and availability of molecular sequence data coupled with advances in phylogeny reconstruction methods, it has now become feasible to reconstruct and fully analyze large phylogenetic trees comprising hundreds or even thousands of sequences (organisms). However, current meta-analysis methods for phylogenetic trees such as programs that conduct co-phylogenetic tests can currently not handle such large datasets.

To alleviate this bottleneck in the meta-analysis pipeline, we recently parallelized, and released the highly optimized co-phylogenetic analysis program AxParafit (Axelerated Parafit - Stamatakis et al., 2007) that implements an elaborate statistical test of congruence between host and parasite trees (Legendre et al., 2002). AxParafit is a typical stand-alone Linux/Unix command line program. AxParafit has been integrated and can be invoked via a user-friendly graphical interface for co-phylogenetic analyses called CopyCat (Meier-Kolthoff et al., 2007). In this article, we present an enhanced version of this tool suite (henceforth denoted as CopyCat(AxParafit)) for co-phylogenetic analyses, that is packaged into a client tool which makes use of a world-wide Grid environment and thereby allows for large-scale data analysis. In the current version, the underlying Grid middleware is gLite (Laure et al., 2006) that is coupled with an efficient submission and execution model called Run Time Sensitive (RTS) scheduling and execution (Stockinger et al., 2006).

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