Advertisement

Swiftly Computing Center Strings

  • Franziska Hufsky
  • Léon Kuchenbecker
  • Katharina Jahn
  • Jens Stoye
  • Sebastian Böcker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6293)

Abstract

The center string (or closest string) problem is a classical computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a string within Hamming distance d to each input string. This problem is NP-complete. In this paper, we focus on exact methods for the problem that are also fast in application. First, we introduce data reduction techniques that allow us to infer that certain instances have no solution, or that a center string must satisfy certain conditions. Then, we describe a novel search tree strategy that is very efficient in practice. Finally, we present results of an evaluation study for instances from a biological application. We find that data reduction is mandatory for the notoriously difficult case d = d opt− 1.

Keywords

Search Tree Close String Binary String Distance Threshold Input 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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Böcker, S., Jahn, K., Mixtacki, J., Stoye, J.: Computation of median gene clusters. J. Comput. Biol. 16(8), 1085–1099 (2009)CrossRefPubMedGoogle Scholar
  2. 2.
    Davila, J., Balla, S., Rajasekaran, S.: Fast and practical algorithms for planted (l, d) motif search. IEEE/ACM Trans. Comput. Biol. Bioinformatics 4(4), 544–552 (2007)CrossRefGoogle Scholar
  3. 3.
    Frances, M., Litman, A.: On covering problems of codes. Theory Comput. Systems 30(2), 113–119 (1997)CrossRefGoogle Scholar
  4. 4.
    Gramm, J., Niedermeier, R., Rossmanith, P.: Fixed-parameter algorithms for closest string and related problems. Algorithmica 37(1), 25–42 (2003)CrossRefGoogle Scholar
  5. 5.
    Lanctot, J.K., Li, M., Ma, B., Wang, S., Zhang, L.: Distinguishing string selection problems. Information and Computation 185(1), 41–55 (2003)CrossRefGoogle Scholar
  6. 6.
    Liu, X., He, H., Sykora, O.: Parallel genetic algorithm and parallel simulated annealing algorithm for the closest string problem. In: Li, X., Wang, S., Dong, Z.Y. (eds.) ADMA 2005. LNCS (LNAI), vol. 3584, pp. 591–597. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Ma, B., Sun, X.: More efficient algorithms for closest string and substring problems. SIAM J. Comput. 39(4), 1432–1443 (2009)CrossRefGoogle Scholar
  8. 8.
    Rahmann, S., Klau, G.W.: Integer linear programming techniques for discovering approximate gene clusters. In: Mandoiu, I., Zelikovsky, A. (eds.) Bioinformatics Algorithms: Techniques and Applications. Wiley Series on Bioinformatics: Computational Techniques and Engineering, ch. 9, pp. 203–222. Wiley, Chichester (2008)Google Scholar
  9. 9.
    Wang, L., Zhu, B.: Efficient algorithms for the closest string and distinguishing string selection problems. In: Deng, X., Hopcroft, J.E., Xue, J. (eds.) FAW 2009. LNCS, vol. 5598, pp. 261–270. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Chen, W., Li, X., Cheng, B.: Degenerated primer design to amplify the heavy chain variable region from immunoglobulin cdna. BMC Bioinformatics 7(suppl. 4), S9 (2006)Google Scholar
  11. 11.
    Yanai, I., DeLisi, C.: The society of genes: networks of functional links between genes from comparative genomics. Genome Biol. 3(11), research0064 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Franziska Hufsky
    • 1
    • 3
  • Léon Kuchenbecker
    • 2
  • Katharina Jahn
    • 2
  • Jens Stoye
    • 2
  • Sebastian Böcker
    • 1
  1. 1.Lehrstuhl für BioinformatikFriedrich-Schiller-Universität JenaJenaGermany
  2. 2.AG Genominformatik, Technische FakultätUniversität BielefeldGermany
  3. 3.International Max Planck Research SchoolJenaGermany

Personalised recommendations