International Symposium on String Processing and Information Retrieval

SPIRE 2015: String Processing and Information Retrieval pp 116-123 | Cite as

Chaining Fragments in Sequences: to Sweep or Not (Extended Abstract)

  • Julien AllaliEmail author
  • Cedric Chauve
  • Laetitia Bourgeade
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9309)


Computing an optimal chain of fragments is a classical problem in string algorithms, with important applications in computational biology. There exist two efficient dynamic programming algorithms solving this problem, based on different principles. In the present note, we show how it is possible to combine the principles of two of these algorithms in order to design a hybrid dynamic programming algorithm that combines the advantages of both algorithms.


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  1. 1.
    Abouelhoda, M.I., Ohlebusch, E.: Chaining algorithms for multiple genome comparison. J. Discrete Algorithms 3, 321–341 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Allali, J., Bourgeade, L., Chauve, C.: Chaining fragments in sequences: to sweep or not. CoRR abs/1506.07458 (2015)Google Scholar
  3. 3.
    Arge, L., Fischer, J., Sanders, P., Sitchinava, N.: On (Dynamic) Range Minimum Queries in External Memory. In: Dehne, F., Solis-Oba, R., Sack, J.-R. (eds.) WADS 2013. LNCS, vol. 8037, pp. 37–48. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  4. 4.
    Eppstein, D., Galil, Z., Giancarlo, R., Italiano, G.F.: Sparse dynamic programming. I: linear cost functions; II: convex and concave cost functions. J. Assoc. Comput. Mach. 39, 519–567 (1992)Google Scholar
  5. 5.
    Felsner, S., Müller, R., Wernisch, L.: Trapezoid graphs and generalizations, geometry and algorithms. Discrete Appl. Math. 74, 13–32 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Gusfield, D.: Algorithms on Strings, Trees and Sequences. Cambridge University Press (1997)Google Scholar
  7. 7.
    Höhl, M., Kurtz, S., Ohlebusch, E.: Efficient multiple genome alignment. Bioinformatics 18, S312–S320 (2002)CrossRefGoogle Scholar
  8. 8.
    Joseph, D., Meidanis, J., Tiwari, P.: Determining DNA sequence similarity using maximum independent set algorithms for interval graphs. In: Nurmi, O., Ukkonen, E. (eds.) SWAT 1992. LNCS, vol. 621, pp. 326–337. Springer, Heidelberg (1992)Google Scholar
  9. 9.
    Morgenstern, B.: A simple and space-efficient fragment-chaining algorithm for alignment of DNA and protein sequences. Appl. Math. Lett. 15, 11–16 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Myers, G., Miller, W.: Chaining multiple-alignmment fragments in sub-quadratic time. SODA 1995, 38–47 (1995)MathSciNetGoogle Scholar
  11. 11.
    Myers, G., Huang, X.: An \(O(N^2\log N)\) restriction map comparison and search algorithm. Bull. Math. Biol. 54, 599–618 (1992)zbMATHGoogle Scholar
  12. 12.
    Ohlebusch, E., Abouelhoda, M.I.: Chaining Algorithms and Applications in Comparative Genomics. In: Aluru, S. (ed.) Handbook of Computational Molecular Biology. CRC Press (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Julien Allali
    • 1
    • 2
    Email author
  • Cedric Chauve
    • 2
    • 3
  • Laetitia Bourgeade
    • 1
  1. 1.LaBRIUniversité BordeauxTalenceFrance
  2. 2.ENSEIRB-MATMECABordeaux INPTalenceFrance
  3. 3.Department of MathematicsSimon Fraser UniversityBurnabyCanada

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