An Evolutionary Algorithm for the Maximum Weight Trace Formulation of the Multiple Sequence Alignment Problem

  • Gabriele Koller
  • Günther R. Raidl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3242)


The multiple sequence alignment problem (MSA) can be re-formulated as the problem of finding a maximum weight trace in an alignment graph, which is derived from all pairwise alignments. We improve the alignment graph by adding more global information. A new construction heuristic and an evolutionary algorithm with specialized operators are proposed and compared to three other algorithms for the MSA, indicating the competitiveness of the new approaches.


Evolutionary Algorithm Multiple Sequence Alignment Edge Weight Pairwise Alignment Construction Heuristic 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chellapilla, K., Fogel, G.B.: Multiple sequence alignment using evolutionary programming. In: Angeline, P.J., et al. (eds.) Proceedings of the 1999 IEEE Congress on Evolutionary Computation, pp. 445–452. IEEE Press, Los Alamitos (1999)Google Scholar
  2. 2.
    Gusfield, D.: Algorithms on Strings, Trees, and Sequences. Cambridge University Press, Cambridge (1997)MATHCrossRefGoogle Scholar
  3. 3.
    Kececioglu, J.D.: The maximum weight trace problem in multiple sequence alignment. In: Apostolico, A., Crochemore, M., Galil, Z., Manber, U. (eds.) CPM 1993. LNCS, vol. 684, pp. 106–119. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  4. 4.
    Kececioglu, J.D., Lenhof, H.-P., Mehlhorn, K., Mutzel, P., Reinert, K., Vingron, M.: A polyhedral approach to sequence alignment problems. Discrete Applied Mathematics 104, 143–186 (2000)MATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Leopold, S.: An alignment graph based evolutionary algorithm for the multiple sequence alignment problem. Master’s thesis, Vienna University of Technology, Vienna, Austria (February 2004)Google Scholar
  6. 6.
    Needleman, S., Wunsch, C.: Ageneralmethod applicable to the search for similarities in the amino acid sequence of two proteins. J.Mol. Biol. 48, 443–453 (1970)CrossRefGoogle Scholar
  7. 7.
    Notredame, C.: Recent progresses in multiple sequence alignment: A survey. Pharmacogenomics 3(1), 131–144 (2002)CrossRefGoogle Scholar
  8. 8.
    Notredame, C., Higgins, D.G.: SAGA: Sequence alignment by genetic algorithm. Nucleic Acids Research 24(8), 1515–1524 (1996)CrossRefGoogle Scholar
  9. 9.
    Notredame, C., Higgins, D.G., Heringa, J.: T-COFFEE: A novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 392, 205–217 (2000)CrossRefGoogle Scholar
  10. 10.
    Notredame, C., Holm, L., Higgins, D.G.: COFFEE: An objective function for multiple sequence alignment. Bioinformatics 14(5), 407–422 (1998)CrossRefGoogle Scholar
  11. 11.
    Thompson, J., Plewniak, F., Poch, O.: BAliBASE: A benchmark alignments database for the evaluation of multiple sequence alignment programs. Bioinformatics 15, 87–88 (1999)CrossRefGoogle Scholar
  12. 12.
    Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice. Nucleic Acids Research 22(22), 4673–4680 (1994)CrossRefGoogle Scholar
  13. 13.
    Zhang, C., Wong, A.K.C.: A genetic algorithm for multiple molecular sequence alignment. CABIOS 13(6), 565–581 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Gabriele Koller
    • 1
  • Günther R. Raidl
    • 1
  1. 1.Institute of Computer Graphics and AlgorithmsVienna University of TechnologyViennaAustria

Personalised recommendations