Encyclopedia of Algorithms

2008 Edition
| Editors: Ming-Yang Kao

Engineering Algorithms for Computational Biology

2002; Bader, Moret, Warnow
  • David A. Bader
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30162-4_124

Keywords and Synonyms

High-performance computational biology    

Problem Definition

In the 50 years since the discovery of the structure of DNA, and with new techniques for sequencing the entire genome of organisms, biology is rapidly moving towards a data-intensive, computational science. Many of the newly faced challenges require high-performance computing, either due to the massive-parallelism required by the problem, or the difficult optimization problems that are often combinatoric and NP-hard. Unlike the traditional uses of supercomputers for regular, numerical computing, many problems in biology are irregular in structure, significantly more challenging to parallelize, and integer-based using abstract data structures.

Biologists are in search of biomolecular sequence data, for its comparison with other genomes, and because its structure determines function and leads to the understanding of biochemical pathways, disease prevention and cure, and the mechanisms of life itself....

Keywords

Computational Biology Phylogenetic Reconstruction Signed Permutation Amino Acid Data Perfect Phylogeny 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Recommended Reading

  1. 1.
    Bader, D.A., Moret, B.M.E., Warnow, T., Wyman, S.K., Yan, M.: High-performance algorithm engineering for gene-order phylogenies. In: DIMACS Workshop on Whole Genome Comparison, Rutgers University, Piscataway, NJ (2001)Google Scholar
  2. 2.
    Bader, D.A., Moret, B.M.E., Vawter, L.: Industrial applications of high-performance computing for phylogeny reconstruction. In: Siegel, H.J. (ed.) Proc. SPIE Commercial Applications for High-Performance Computing, vol. 4528, pp. 159–168, Denver, CO (2001)Google Scholar
  3. 3.
    Bader, D.A., Moret, B.M.E., Yan, M.: A linear-time algorithm for computing inversion distance between signed permutations with an experimental study. J. Comp. Biol. 8(5), 483–491 (2001)CrossRefGoogle Scholar
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    Farris, J.S.: The logical basis of phylogenetic analysis. In: Platnick, N.I., Funk, V.A. (eds.) Advances in Cladistics, pp. 1–36. Columbia Univ. Press, New York (1983)Google Scholar
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    Felsenstein, J.: Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 17, 368–376 (1981)CrossRefGoogle Scholar
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    Moret, B.M.E., Bader, D.A., Warnow, T., Wyman, S.K., Yan, M.: GRAPPA: a highperformance computational tool for phylogeny reconstruction from gene-order data. In: Proc. Botany, Albuquerque, August 2001Google Scholar
  7. 7.
    Moret, B.M.E., Bader, D.A., Warnow, T.: High-performance algorithm engineering for computational phylogenetics. J. Supercomp. 22, 99–111 (2002) Special issue on the best papers from ICCS'01zbMATHCrossRefGoogle Scholar
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    Moret, B.M.E., Wyman, S., Bader, D.A., Warnow, T., Yan, M.: A new implementation and detailed study of breakpoint analysis. In: Proc. 6th Pacific Symp. Biocomputing (PSB 2001), pp. 583–594, Hawaii, January 2001Google Scholar
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    Saitou, N., Nei, M.: The neighbor-joining method: A new method for reconstruction of phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987)Google Scholar
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    Sankoff, D., Blanchette, M.: Multiple genome rearrangement and breakpoint phylogeny. J. Comp. Biol. 5, 555–570 (1998)CrossRefGoogle Scholar
  11. 11.
    Yan, M.: High Performance Algorithms for Phylogeny Reconstruction with Maximum Parsimony. Ph. D. thesis, Electrical and Computer Engineering Department, University of New Mexico, Albuquerque, January 2004Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • David A. Bader
    • 1
  1. 1.College of ComputingGeorgia Institute of TechnologyAtlantaUSA