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Engineering Algorithms for Computational Biology

2002; Bader, Moret, Warnow

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Encyclopedia of Algorithms
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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....

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Recommended Reading

  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)

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  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)

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  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)

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  4. 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 

  5. Felsenstein, J.: Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 17, 368–376 (1981)

    Article  Google Scholar 

  6. 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 2001

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  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'01

    Article  MATH  Google Scholar 

  8. 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 2001

    Google Scholar 

  9. 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 

  10. Sankoff, D., Blanchette, M.: Multiple genome rearrangement and breakpoint phylogeny. J. Comp. Biol. 5, 555–570 (1998)

    Article  Google Scholar 

  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 2004

    Google Scholar 

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© 2008 Springer-Verlag

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Bader, D.A. (2008). Engineering Algorithms for Computational Biology. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30162-4_124

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