High-Performance Algorithm Engineering for Computational Phylogenetics

A phylogeny is the evolutionary history of a group of organisms; systematists (and other biologists) attempt to reconstruct this history from various forms of data about contemporary organisms. Phylogeny reconstruction is a crucial step in the understanding of evolution as well as an important tool...

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Vydáno v:The Journal of supercomputing Ročník 22; číslo 1; s. 99 - 111
Hlavní autoři: Moret, Bernard M. E., Bader, David A., Warnow, Tandy
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer Nature B.V 01.05.2002
Témata:
Algorithms
Biologists
Combinatorial analysis
Computer simulation
Data reduction
Datasets
Engineering
Genetic algorithms
Genetic engineering
Medical research
Molecular dynamics
Optimization
Phylogenetics
Reconstruction
ISSN:0920-8542, 1573-0484
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Shrnutí:A phylogeny is the evolutionary history of a group of organisms; systematists (and other biologists) attempt to reconstruct this history from various forms of data about contemporary organisms. Phylogeny reconstruction is a crucial step in the understanding of evolution as well as an important tool in biological, pharmaceutical, and medical research. Phylogeny reconstruction from molecular data is very difficult: almost all optimization models give rise to NP-hard (and thus computationally intractable) problems. Yet approximations must be of very high quality in order to avoid outright biological nonsense. Thus many biologists have been willing to run farms of processors for many months in order to analyze just one dataset. High-performance algorithm engineering offers a battery of tools that can reduce, sometimes spectacularly, the running time of existing phylogenetic algorithms, as well as help designers produce better algorithms. We present an overview of algorithm engineering techniques, illustrating them with an application to the “breakpoint analysis” method of Sankoff et al., which resulted in the GRAPPA software suite. GRAPPA demonstrated a speedup in running time by over eight orders of magnitude over the original implementation on a variety of real and simulated datasets. We show how these algorithmic engineering techniques are directly applicable to a large variety of challenging combinatorial problems in computational biology.
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ISSN:0920-8542
1573-0484
DOI:10.1023/A:1014362705613