Parallel Multiple Sequence Alignment with Decentralized Cache Support

  • Denis Trystram
  • Jaroslaw Zola
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3648)


In this paper we present a new method for aligning large sets of biological sequences. The method performs a sequence alignment in parallel and uses a decentralized cache to store intermediate results. The method allows alignments to be recomputed efficiently when new sequences are added or when alignments of different precisions are requested. Our method can be used to solve important biological problems like the adaptive update of a complete evolution tree when new sequences are added (without recomputing the whole tree).

To validate the method, some experiments were performed using up to 512 Small Subunit Ribosomal RNA sequences, which were analyzed with different levels of precision.


Multiple Sequence Alignment Multiple Alignment Input Sequence Pairwise Alignment Cache Replacement 
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.
    Holder, M., Lewis, P.O.: Phylogeny estimation: traditional and bayesian approaches. Nature Reviews Genetics 4, 275–284 (2003)CrossRefGoogle Scholar
  2. 2.
    Jiang, T., Lawler, E.L., Wang, L.: Aligning sequences via an evolutionary tree: complexity and approximation. In: ACM Symp. on Theory of Computing, pp. 760–769 (1994)Google Scholar
  3. 3.
    Duret, L., Mouchiroud, D., Gouy, M.: HOVERGEN, a database of homologous vertebrate genes. Nucleic Acids Res. 22, 2360–2365 (1994)CrossRefGoogle Scholar
  4. 4.
    Guinand, F., Parmentier, G., Trystram, D.: Integration of multiple alignment and phylogeny reconstruction. In: Eur. Conf. on Comp. Biology, Poster Abstr (2002)Google Scholar
  5. 5.
    Parmentier, G., Trystram, D., Zola, J.: Cache-based parallelization of multiple sequence alignment problem. In: Danelutto, M., Vanneschi, M., Laforenza, D. (eds.) Euro-Par 2004. LNCS, vol. 3149, pp. 1005–1012. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Higgins, D., Thompson, J., Gibson, T.: CLUSTALW: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 22, 4673–4680 (1994)CrossRefGoogle Scholar
  7. 7.
    Li, K.B.: ClustalW–MPI: ClustalWanalysis using distributed and parallel computing. Bioinformatics 19, 1585–1586 (2003)CrossRefGoogle Scholar
  8. 8.
    Mikhailov, D., Cofer, H., Gomperts, R.: Performance optimization of ClustalW: Parallel ClustalW, HT Clustal, and MULTICLUSTAL (2005),
  9. 9.
    Catalyurek, U., Ferreira, R., Kurc, T., Saltz, J.: Improving performance of multiple sequence alignment analysis in multi–client environments. In: Proc. of HiCOMB 2002 (2002)Google Scholar
  10. 10.
    Stamatakis, A., Ludwig, T., Meier, H.: Parallel inference of a 10.000–taxon phylogeny with maximum likelihood. In: Danelutto, M., Vanneschi, M., Laforenza, D. (eds.) Euro-Par 2004. LNCS, vol. 3149, pp. 997–1004. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  11. 11.
    Zola, J.: CaLi – generic computational buffers library (2005),
  12. 12.
    Balamsh, A., Krunz, M.: An overview of web caching replacement algorithms. IEEE Comm. Surv. & Tutor. 6, 44–56 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Denis Trystram
    • 1
  • Jaroslaw Zola
    • 1
    • 2
  1. 1.Laboratoire ID–IMAGGrenobleFrance
  2. 2.Institute of Computer & Information SciencesCzestochowa University of TechnologyPoland

Personalised recommendations