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New Distances for Improving Progressive Alignment Algorithm

  • Ahmed Mokaddem
  • Mourad Elloumi
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 177)

Abstract

Distance computation between sequences is an important method to compare between biological sequences. In fact, we attribute a value to the sequences in order to estimate a percentage of similarity that can help to extract structural or functional information. Distance computation is also more important in the progressive multiple alignment algorithm. Indeed, it can influence the branching order of the sequences alignment and then the final multiple alignment. In this paper, we present new methods for distance computation in order to improve the progressive multiple alignment approach. The main difference between our distances and the other existed methods consists in the use of all the sequences of the set in the pair-wise comparison. We tested our distances on BALIBASE benchmarks and we compared with other typical distances. We obtained very good results.

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References

  1. 1.
    Edgar, R.C.: MUSCLE: multiple sequence alignment with high accuracy high throughput. Nucleic Acids Research 32(5), 1792–1797 (2004)CrossRefGoogle Scholar
  2. 2.
    Derrien, V., Richer, J.M., Hao, J.K.: PLasMA: un nouvel algorithme progress if pour l’alignement multiple des séquences. In: Proc. Premières Journées Francophones de Programmation par Contraintes (JFPC 2005), pp. 39–48 (2005)Google Scholar
  3. 3.
    Do, C.B., Mahabhashyam, M.S., Brudno, M., Batzoglou, S.: PROBCONS: Probabilistic consistency-based multiple sequence alignment. Genome Res. 15, 330–340 (2005)CrossRefGoogle Scholar
  4. 4.
    Gotoh, O.: Significant improvement in accuracy of multiple protein sequence alignments by iterative refinement as assessed by reference to structural alignments. J. Mol. Biol. 264(4), 823–838 (1996)CrossRefGoogle Scholar
  5. 5.
    Katoh, K., Kuma, K., Toh, H., Miyata, T.: MAFFT version 5: Improvement in accuracy of multiple sequence alignment. Nucleic Acids Res. 33(2), 511–518 (2005)CrossRefGoogle Scholar
  6. 6.
    Kimura, M.: The neutral theory of molecular evolution. Cambridge University Press (1983)Google Scholar
  7. 7.
    Lassman, T., Frings, O., Sonnhammer, L.L.: KALIGN2: high-performance multiple alignment of protein and nucleotide sequences allowing external features. Nucleic Acids Research 37(3), 858–865 (2009)CrossRefGoogle Scholar
  8. 8.
    Lassman, T., Sonnhammer, L.L.: KALIGN: An accurate and fast multiple sequence alignment algorithm. BMC Bioinformatics 6 (2005)Google Scholar
  9. 9.
    Liang Ye, Y., Huang, X.: MAP2: multiple alignments of syntenic genomic sequences. Nucleic Acids Research 33(1), 162–170 (2005)CrossRefGoogle Scholar
  10. 10.
    Min, Z., Weiwu, F., Junhua, Z., Zhongxian, C.: MSAID: multiple sequence alignment based on a measure of information discrepancy. Computational Biology and Chemistry 29, 175–181 (2005)MATHCrossRefGoogle Scholar
  11. 11.
    Mokaddem, A., Elloumi, M.: PAAA: A Progressive Iterative Alignment Algorithm Based on Anchors. In: PRIB, pp. 296–305 (2011)Google Scholar
  12. 12.
    Muth, R., Manber, U.: Approximate multiple string search. In: Hirschberg, D.S., Meyers, G. (eds.) CPM 1996. LNCS, vol. 1075, pp. 75–86. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  13. 13.
    Needleman, S.B., Wunsch, C.D.: A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology 48(1), 443–453 (1970)CrossRefGoogle Scholar
  14. 14.
    Pei, J., Grishin, N.V.: MUMMALS: Multiple sequence alignment improved by using hidden Markov models with local structural information. Nucleic Acids Res. 34(16), 4364–4374 (2006)CrossRefGoogle Scholar
  15. 15.
    Russell, D.J., Out, H.H., Sayood, K.: Grammar-based distance in progressive multiple sequence alignment. BMC Bioinformatics 9 (2008)Google Scholar
  16. 16.
    Thompson, J.D., Higgins, D.G., Gibson, T.J.: CLUSTALW: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position specific gap penalties and weight matrix choice. Nucleid Acids Research 22(22), 4673–4680 (1994)CrossRefGoogle Scholar
  17. 17.
    Thompson, J.D., Plewniak, F., Poch, O.: A comprehensive comparison of multiple sequence alignment programs. Nucleic Acids Res. 27(13), 2682–2690 (1999)CrossRefGoogle Scholar
  18. 18.
    Sneath, P., Sokal, R.: Numerical taxonomy, pp. 230–234. Freeman, San Francisco (1973)MATHGoogle Scholar
  19. 19.
    Vinga, S., Almeida, J.: Alignment-free sequence comparison-a review. Bioinformatics 19(4), 513–523 (2003)CrossRefGoogle Scholar
  20. 20.
    Wheeler, T.J., Kececioglu, J.D.: Multiple alignment by aligning alignments. Bioinformatics 23(13), 559–568 (2007)CrossRefGoogle Scholar
  21. 21.
    Wu, S., Manber, U.: Fast Text Searching Allowing Errors. Communications of the ACM 35, 83–91 (1992)CrossRefGoogle Scholar
  22. 22.
    Zhong, W.: Using Traveling Salesman Problem Algorithms to Determine Multiple Sequence Alignment Orders. Thesis (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Laboratory of Technologies of Information and Communication and Electrical Engineering (LaTICE), Higher School of Sciences and Technologies of Tunis (HSSTT)University of TunisTunisTunisia

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