Optimal Spaced Seeds for Faster Approximate String Matching

  • Martin Farach-Colton
  • Gad M. Landau
  • S. Cenk Sahinalp
  • Dekel Tsur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3580)


Filtering is a standard technique for fast approximate string matching in practice.In filtering, a quick first step is used to rule out almost all positions of a text as possible starting positions for a pattern. Typically this step consists of finding the exact matches of small parts of the pattern. In the followup step, a slow method is used to verify or eliminate each remaining position. The running time of such a method depends largely on the quality of the filtering step, as measured by its false positives rate. The quality of such a method depends on the number of true matches that it misses, that is, on its false negative rate.

A spaced seed is a recently introduced type of filter pattern that allows gaps (i.e. don’t cares) in the small sub-pattern to be searched for. Spaced seeds promise to yield a much lower false positives rate, and thus have been extensively studied, though heretofore only heuristically or statistically.

In this paper, we show how to optimally design spaced seeds that yield no false negatives.


Pattern Match Edit Distance Seed Length Lower False Positive Rate Text String 
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.
    Altschul, S., Gisch, W., Miller, W., Myers, E., Lipman, D.: Basic local alignment search tool. J. of Molecular Biology 215(3), 403–410 (1990)Google Scholar
  2. 2.
    Amir, A., Lewenstein, M., Porat, E.: Faster algorithms for string matching with k-mismatches. In: Proc. ACM-SIAM SODA, pp. 794–803 (2000)Google Scholar
  3. 3.
    Brejova, B., Brown, D.G., Vinar, T.: Vector seeds: an extension to spaced seeds allows substantial improvements in sensitivity and specificity. In: Proc. WABI, pp. 39–54 (2003)Google Scholar
  4. 4.
    Buhler, J.: Provably sensitive indexing strategies for biosequence similarity search. In: Proc. ACM RECOMB, pp. 90–99 (2002)Google Scholar
  5. 5.
    Buhler, J., Keich, U., Sun, Y.: Designing seeds for similarity search in genomic DNA. In: Proc. ACM RECOMB, pp. 67–75 (2003)Google Scholar
  6. 6.
    Burkhardt, S., Karkkainen, J.: Better filtering with gapped q-grams. Fundamenta Informaticae 56, 51–70 (2003)zbMATHMathSciNetGoogle Scholar
  7. 7.
    Califano, A., Rigoutsos, I.: Flash: a fast look-up algorithm for string homology. In: Proc. ISMB, pp. 56–64 (1993)Google Scholar
  8. 8.
    Cole, R., Hariharan, R.: Approximate string matching, a simpler, faster algorithm. In: Proc. ACM-SIAM SODA, pp. 463–472 (1997)Google Scholar
  9. 9.
    Karpinski, M., Zelikovsky, A.: Approximating dense cases of covering. Electronic Colloquium on Computational Complexity 4(4) (1997)Google Scholar
  10. 10.
    Keich, U., Li, M., Ma, B., Tromp, J.: On spaced seeds for similarity search. Discrete Applied Mathematics 138(3), 253–263 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Kucherov, G., Noé, L., Roytberg, M.: Multi-seed lossless filtration. In: Proc. CPM, pp. 297–310 (2004)Google Scholar
  12. 12.
    Kucherov, G., Noé, L., Ponty, Y.: Estimating seed sensitivity on homogeneous alignments. In: Proc. IEEE BIBE, pp. 387–394 (2004)Google Scholar
  13. 13.
    Landau, G.M., Vishkin, U.: Fast parallel and serial approximate string matching. Journal of Algorithms 10(2), 157–169 (1989)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Li, M., Ma, B., Kisman, D., Tromp, J.: Patternhunter II: Highly sensitive fast homology search. J. of Bioinformatics and Computational Biology 2(3), 417–439 (2004)CrossRefGoogle Scholar
  15. 15.
    Ma, B., Tromp, J., Li, M.: PatternHunter: Faster and more sensitive homology search. Bioinformatics 18, 440–445 (2002)CrossRefGoogle Scholar
  16. 16.
    Pevzner, P., Waterman, M.: Multiple filtration and approximate pattern matching. Algorithmica 13, 135–154 (1995)zbMATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Sahinalp, S.C., Vishkin, U.: Efficient approximate and dynamic matching of patterns using a labeling paradigm. In: Proc. IEEE FOCS, pp. 320–328 (1996)Google Scholar
  18. 18.
    Sun, Y., Buhler, J.: Designing multiple simultaneous seeds for DNA similarity search. In: Proc. ACM RECOMB, pp. 76–84 (2004)Google Scholar
  19. 19.
    Xu, J., Brown, D., Li, M., Ma, B.: Optimizing multiple spaced seeds for homology search. In: Proc. CPM, pp. 47–58 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Martin Farach-Colton
    • 1
  • Gad M. Landau
    • 2
  • S. Cenk Sahinalp
    • 3
  • Dekel Tsur
    • 4
  1. 1.Dept. of Computer Science and DIMACSRutgers University 
  2. 2.Dept. of Computer ScienceUniversity of Haifa 
  3. 3.School of Computing ScienceSimon Fraser University 
  4. 4.Dept. of Computer Science and EngineeringUniversity of CaliforniaSan Diego

Personalised recommendations