Aligning DNA sequences to minimize the change in protein

Extended Abstract
  • Yufang Hua
  • Tao Jiang
  • Bin Wu
Session V
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1448)


We study an alignment model for coding DNA sequences recently proposed by J. Hein that takes into account both DNA and protein information, and attempts to minimize the total amount of evolution at both DNA and protein levels. Assuming that the gap penalty function is affine, we design a quadratic time dynamic programming algorithm for the model. Although the algorithm theoretically solves an open question of Hein, its running time is impractical because of the large constant factor embedded in the quadratic time complexity function. We therefore consider a mild simplification of Hein's model and present a much more efficient algorithm for the simplified model. The algorithms have been implemented and tested on both real and simulated sequences, and it is found that they produce almost identical alignments in most cases.


Recurrence Equation Dynamic Programming Algorithm Optimal Alignment Simulated Sequence Quadratic Time 
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  1. 1.
    M. Dayhoff, et al., Atlas of Protein Sequence and Structure, 5 suppl. 3, pp. 345–352, 1978.Google Scholar
  2. 2.
    O. Gotoh, An improved algorithm for matching biological sequences, J. Mol. Biol. 162, pp. 705–708, 1981.Google Scholar
  3. 3.
    D. Gusfield, Algorithms on Strings, Trees, and Sequences, Cambridge University Press, 1997.Google Scholar
  4. 4.
    J. Hein, An algorithm combining DNA and protein alignment, Journal of Theoretical Biology 167, pp. 169–174, 1994.Google Scholar
  5. 5.
    J. Hein and J. Støvlbaek, Genomic alignment, J. Mol. Evol. 38, pp. 310–316, 1994.Google Scholar
  6. 6.
    J. Hein and J. Støvlbaek, Combined DNA and protein alignment, Methods in Enzymology 266, pp. 402–418, 1996.Google Scholar
  7. 7.
    Y. Hua, An improved algorithm for combined DNA and protein alignment, M. Eng. Thesis, Department of Computer and Electrical Engineering, McMaster University, 1997.Google Scholar
  8. 8.
    S. Needlemann and C. Wunsch, A general method applicable to the search for similarities in the amino acid sequences of two proteins, J. Mol. Biol. 48, pp. 443–453, 1970.Google Scholar
  9. 9.
    C. Pedersen, Computational analysis of biological sequences, Manuscript, 1997.Google Scholar
  10. 10.
    D. Sankoff, Matching sequences under deletion/insertion constraints, Proc. Nat. Acad. Sci. 69(1), pp. 4–6, 1972.Google Scholar
  11. 11.
    D. Sankoff, R. Cedergren and G. Lapalme, Frequency of insertion-deletion, transversion, and transition in the evolution of 5S ribosomal RNA, J. Mol. Evol. 7, pp.133–149, 1976.Google Scholar
  12. 12.
    P. Sellers, On the theory and computation of evolutionary distances, SIAM J. Appl. Math. 26, pp. 787–793, 1974.Google Scholar
  13. 13.
    D. States, et al., Methods: A companion to methods in Enzymology 3, pp. 66–70, 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Yufang Hua
    • 1
  • Tao Jiang
    • 2
  • Bin Wu
    • 2
  1. 1.Dept 659IBM CanadaTorontoCanada
  2. 2.Department of Computer ScienceMcMaster UniversityHamiltonCanada

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