David A. Bader
IEEE Fellow
AAAS Fellow
Professor
College of Computing
Georgia Tech
Atlanta, GA 30332


 
 

 

Industrial Applications of High-Performance Computing for Phylogeny Reconstruction

Phylogenies (that is, tree-of-life relationships) derived from gene order data may prove crucial in answering some fundamental open questions in biomolecular evolution. Real-world interest is strong in determining these relationships. For example, pharmaceutical companies may use phylogeny reconstruction in drug discovery for discovering synthetic pathways unique to organisms that they wish to target. Health organizations study the phylogenies of organisms such as HIV in order to understand their epidemiologies and to aid in predicting the behaviors of future outbreaks. And governments are interested in aiding the production of such foodstuffs as rice, wheat and potatoes via genetics through understanding of the phylogenetic distribution of genetic variation in wild populations. Yet few techniques are available for difficult phylogenetic reconstruction problems. Appropriate tools for analysis of such data may aid in resolving some of the phylogenetic problems that have been analyzed without much resolution for decades.

With the rapid accumulation of whole genome sequences for a wide diversity of taxa, especially microbial taxa, phylogenetic reconstruction based on changes in gene order and gene content is showing promise, particularly for resolving deep (i.e., ancient) branch splits. However, reconstruction from gene-order data is even more computationally expensive than reconstruction from sequence data, particularly in groups with large numbers of genes and highly-rearranged genomes. We have developed a software suite, GRAPPA, that extends the breakpoint analysis (BPAnalysis) method of Sankoff and Blanchette while running much faster: in a recent analysis of chloroplast genome data for species of Campanulaceae on a 512-processor Linux supercluster with Myrinet, we achieved a one-million-fold speedup over BPAnalysis. GRAPPA can use either breakpoint or inversion distance (computed exactly) for its computation and runs on single-processor machines as well as parallel and high-performance computers.

Publication History

Versions of this paper appeared as:
  1. D.A. Bader, B.M.E. Moret, and L. Vawter, ``Industrial Applications of High-Performance Computing for Phylogeny Reconstruction,'' presented at the SPIE ITCom: Commercial Applications for High-Performance Computing (SPIE ITCom2001), Denver, CO, August 2001.

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Last updated: July 25, 2004

 




Computational Biology



Parallel Computing



Combinatorics