Incremental Multiple Sequence Alignment

  • Marcelino Campos
  • Damián López
  • Piedachu Peris
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)

Abstract

This work proposes a new approach to the alignment of multiple sequences. We take profit from some results on Grammatical Inference that allow us to build iteratively an abstract machine that considers in each inference step an increasing amount of sequences. The obtained machine compile the common features of the sequences, and can be used to align these sequences. This method improves the time complexity of current approaches. The experimentation carried out compare the performance of our method and previous alignment methods.

Keywords

Grammatical inference processing of biosequences multiple alignment of sequences 

References

  1. 1.
    Notredame, C.: Recent progresses in multiple sequence alignment: a survey. Pharmacogenomics 3(1), 1–14 (2002)CrossRefGoogle Scholar
  2. 2.
    Thompson, J., Higgins, D., Gibson, T.: Clustal-w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acid Research 22(22), 4673–4680 (1994)CrossRefGoogle Scholar
  3. 3.
    Notredame, C., Higgins, D., Heringa, J.: T-coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302, 205–217 (2000)CrossRefGoogle Scholar
  4. 4.
    Fu, K., Booth, T.: Grammatical inference: Introduction and survey - Part I. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(3), 343–359 (1975)CrossRefGoogle Scholar
  5. 5.
    Fu, K., Booth, T.: Grammatical inference: Introduction and survey - Part II. IEEE Transactions on Pattern Analysis and Machine Intelligence 8(3), 360–375 (1975)Google Scholar
  6. 6.
    Angluin, D., Smith, C.: Inductive inference:Theory and Methods. Computing Surveys 15(3), 237–269 (1983)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Miclet, L.: Grammatical inference. In: Syntactic and Structural Pattern Recognition. Theory and Applications. Series in Computer Science, vol. 7, pp. 237–290 (1990)Google Scholar
  8. 8.
    Sakakibara, Y.: Recent advances of grammatical inference. Theoretical Computer Science 185, 15–45 (1997)MATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Sakakibara, Y.: Grammatical inference in bioinformatics. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(7), 1051–1062 (2005)CrossRefGoogle Scholar
  10. 10.
    Rulot, H., Vidal, E.: An efficient algorithm for the inference of circuit-free automata. In: Syntactic and Structural Pattern Recognition. NATO Asi Series, pp. 173–184 (1988)Google Scholar
  11. 11.
    Prieto, N., Vidal, E.: Learning language models through the ecgi method. Speech Communication 11, 299–309 (1992)CrossRefGoogle Scholar
  12. 12.
    Vidal, E., Rulot, H., Valiente, J.M., Andreu, G.: Application of the error-correcting grammatical inference algorithm (ECGI) to planar shape. In: Grammatical inference: theory, applications and alternatives. vol. IEE. Digest No: 1993/092 (1993)Google Scholar
  13. 13.
    Hopcroft, J., Ullman, J.: Introduction to Automata Theory, Languages and Computation. Addison-Wesley Publishing Company, Reading (1979)MATHGoogle Scholar
  14. 14.
    Gardner, P., Giegerich, R.: A comprehensive comparison of comparative rna structure prediction aproaches. BMC Bioinformatics 5 (2004)Google Scholar
  15. 15.
    Thompson, J., Plewniak, F., Poch, O.: A comprehensive comparison of multiple sequence alignment programs. Nucleic Acid Research 27(13), 2682–2690 (1999)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Marcelino Campos
    • 1
  • Damián López
    • 1
  • Piedachu Peris
    • 1
  1. 1.Departamento de Sistemas Informáticos y Computación, Universidad Politécnica de Valencia, Camino de Vera s/n, 46071 ValenciaSpain

Personalised recommendations