Parallel processing of biological sequence comparison algorithms (original) (raw)

Abstract

Comparison of biological (DNA or protein) sequences provides insight into molecular structure, function, and homology, and is increasingly important as the available databases become larger and more numerous. One method of increasing the speed of the calculations is to perform them in parallel. We present the results of initial investigations using the Intel iPSC/1 hypercube and the Connection Machine (CM-I) for these comparisons. Since these machines have very different architectures, the issues and performance trade-offs discussed have a wide applicability for the parallel processing of biological sequence comparisons.

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References

  1. Academy Backs Genome Project,Science,239:725–726 (1988).
    Google Scholar
  2. H. S. Hilofsky and C. Burks, The Genbank Genetic Sequence Databank,Nucleic Acids Research,16:1861–1863 (1988).
    Google Scholar
  3. D. Sankoff and J. B. Kruskal (eds.),Time Warps, String Edits, and Macromolecules: the Theory and Practice of Sequence Comparisons, Reading, Massachusetts, Addison-Wesley (1983).
    Google Scholar
  4. W. R. Pearson and D. J. Lipman, Improved Tools for Biological Sequence Comparison,PNAS,85:2444–2448 (1988).
    Article Google Scholar
  5. D. J. Kuck, E. S. Davidson, D. H. Lawrie, and A. H. Sameh, Parallel Supercomputing Today and the Cedar Approach,Science,231:967–974 (1986).
    Google Scholar
  6. Y. Saad and M. H. Schultz, Topological Properties of Hypercubes,Technical Report RR-389, Yale University Department of Computer Science (May 1985).
  7. R. P. Gabriel, Massively Parallel Computers: The Connection Machine and NON-VON,Science,231:975–978 (1986).
    Google Scholar
  8. S. B. Needleman and C. D. Wunsch, A General Method Applicable to the Search for Similarities in the Amino Acid Sequence of Two Proteins,Journal of Molecular Biology,48:443–453 (1970).
    Article Google Scholar
  9. T. F. Smith and M. S. Waterman, Identification of Common Molecular Subsequences,Journal of Molecular Biology,147:195–196 (1981).
    Article Google Scholar
  10. O. Gotoh, An Improved Algorithm for Matching Biological Sequences,Journal of Molecular Biology,162:705–708 (1982).
    Article Google Scholar
  11. J. H. Saltz, Aggregration Methods for Solution of Sparse Triangular Systems on Multiprocessors,SIAM Journal of Scientific and Statistical Computation (to appear).
  12. E. W. Edmiston and R. A. Wagner, Parallelization of the Dynamic Programming Algorithm for Comparison of Sequences,Proceedings of the 1987 ICPP, pp. 78–80 (1987).
  13. J. V. Maizel, Supercomputing in Molecular Biology: Applications to Sequence Analysis,IEEE Engineering in Medicine and Biology, pp. 27–30 (1988).
  14. E. Lander, J. P. Mesirov, and W. Taylor, Protein Sequence Comparison on a Data Parallel Computer,Proceedings of the 1988 ICPP,3:257–263 (1988).
    Google Scholar

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Authors and Affiliations

  1. Duke University Department of Computer Science, USA
    Elizabeth W. Edmiston
  2. Yale University School of Medicine, USA
    Nolan G. Core
  3. Yale University Department of Computer Science, USA
    Joel H. Saltz
  4. Yale University Department of Computer Science, USA
    Roger M. Smith

Authors

  1. Elizabeth W. Edmiston
  2. Nolan G. Core
  3. Joel H. Saltz
  4. Roger M. Smith

Additional information

This research was supported in part by the Office of Naval Research under contact No. N00014-86-K-0310 and by NIH Grant T15 LM07056 from the National Library of Medicine.

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Edmiston, E.W., Core, N.G., Saltz, J.H. et al. Parallel processing of biological sequence comparison algorithms.Int J Parallel Prog 17, 259–275 (1988). https://doi.org/10.1007/BF02427852

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