Sequence Alignment as a Database Technology Challenge

  • Hans Philippi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4653)


Sequence alignment is an important task for molecular biologists. Because alignment basically deals with approximate string matching on large biological sequence collections, it is both data intensive and computationally complex. There exist several tools for the variety of problems related to sequence alignment. Our first observation is that the term ’sequence database’ is used in general for textually formatted string collections. A second observation is that the search tools are specifically dedicated to a single problem. They have limited capabilities to serve as a solution for related problems that require minor adaptations. Our aim is to show the possibilities and advantages of a DBMS-based approach toward sequence alignment. For this purpose, we will adopt techniques from single sequence alignment to speed up multiple sequence alignment. We will show how the problem of matching a protein string family against a large protein string database can be tackled with q-gram indexing techniques based on relational database technology. The use of Monet, a main-memory DBMS, allows us to realize a flexible environment for developing searching heuristics that outperform classical dynamic programming, while keeping up satisfying sensitivity figures.


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  1. 1.
    Korf, I., Yandell, M., Bedell, J.: Blast, O’Reilly (2003)Google Scholar
  2. 2.
    Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Biological Sequence Analysis. Cambridge University Press, Cambridge (1998)zbMATHGoogle Scholar
  3. 3.
    Altschul, S.F., Gish, W., Miller, W., Myers, E.W., Lipman, D.J.: Basic local alignment search tool. Journal of Molecular Biology 215, 403–410 (1990)Google Scholar
  4. 4.
    Altschul, S.F., Madden, T.L., et al.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Research 25(17), 3389–3402 (1997)CrossRefGoogle Scholar
  5. 5.
    Aluru, S. (ed.): Handbook of Computational Molecular Biology, Chapman & Hall/CRC (2005)Google Scholar
  6. 6.
    Krogh, A.: An introduction to Hidden Markov Models for biological sequences. In: Salzberg, S.L., Searls, D.B., Kasif, S. (eds.) Computational Methods in Molecular Biology, pp. 45–63. Elsevier, Amsterdam (1998)CrossRefGoogle Scholar
  7. 7.
    Boncz, P.A., Kersten, M.L.: MIL Primitives for Querying a Fragmented World. The VLDB Journal 8, 101–119 (1999)CrossRefGoogle Scholar
  8. 8.
    Boncz, P.A.: Monet: A Next-Generation DBMS Kernel For Query-Intensive Applications, PhD thesis, UVA, Amsterdam, The Netherlands (May 2002)Google Scholar
  9. 9.
  10. 10.
    Garcia Molina, H., Ullman, J.D., Widom, J.D.: Database System Implementation. Prentice-Hall, Englewood Cliffs (2000)Google Scholar
  11. 11.
    Williams, H.E., Zobel, J.: Indexing and Retrieval for Genomic Databases. IEEE Transactions on Knowledge and Data Engineering 14, 63–78 (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Hans Philippi
    • 1
  1. 1.Dept. of Computing and Information Sciences, Utrecht University 

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