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Inferring Gene Orders from Gene Maps Using the Breakpoint Distance

  • Guillaume Blin
  • Eric Blais
  • Pierre Guillon
  • Mathieu Blanchette
  • Nadia El-Mabrouk
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4205)

Abstract

Preliminary to most comparative genomics studies is the annotation of chromosomes as ordered sequences of genes. Unfortunately, different genetic mapping techniques usually give rise to different maps with unequal gene content, and often containing sets of unordered neighboring genes. Only partial orders can thus be obtained from combining such maps. However, once a total order O is known for a given genome, it can be used as a reference to order genes of a closely related species characterized by a partial order P. In this paper, the problem is to find a linearization of P that is as close as possible to O in term of the breakpoint distance. We first prove an NP-complete complexity result for this problem. We then give a dynamic programming algorithm whose running time is exponential for general partial orders, but polynomial when the partial order is derived from a bounded number of genetic maps. A time-efficient greedy heuristic is then given for the general case, with a performance higher than 90% on simulated data. Applications to the analysis of grass genomes are presented.

Keywords

Partial Order Total Order Dynamic Programming Algorithm Greedy Heuristic Maize Chromosome 
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.

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References

  1. 1.
    Bérard, S., Bergeron, A., Chauve, C.: Conservation of combinatorial structures in evolution scenarios. In: Lagergren, J. (ed.) RECOMB-WS 2004. LNCS (LNBI), vol. 3388, pp. 1–14. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  2. 2.
    Bergeron, A., Mixtacki, J., Stoye, J.: Reversal distance without hurdles and fortresses. In: Sahinalp, S.C., Muthukrishnan, S.M., Dogrusoz, U. (eds.) CPM 2004. LNCS, vol. 3109, pp. 388–399. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Blin, G., Rizzi, R.: Conserved interval distance computation between non-trivial genomes. In: Wang, L. (ed.) COCOON 2005. LNCS, vol. 3595, pp. 22–31. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Bowers, J.E., Abbey, C., Anderson, A., Chang, C., Draye, X., Hoppe, A.H., Jessup, R., Lemke, C., Lennington, J., Li, Z.K., Lin, Y.R., Liu, S.C., Luo, L.J., Marler, B., Ming, R.G., Mitchell, S.E., Qiang, D., Reischmann, K., Schulze, S.R., Skinner, D.N., Wang, Y.W., Kresovich, S., Schertz, K.F., Paterson, A.H.: A high-density genetic recombination map of sequence-tagged sites for Sorghum, as a framework for comparative structural and evolutionary genomics of tropical grains and grasses. Genetics (2003)Google Scholar
  5. 5.
    Figeac, M., Varré, J.S.: Sorting by reversals with common intervals. In: Jonassen, I., Kim, J. (eds.) WABI 2004. LNCS (LNBI), vol. 3240, pp. 26–37. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Floyd, R.W.: Algorithm 97: Shortest path. Communications of the ACM (1962)Google Scholar
  7. 7.
    Gale, M.D., Devos, K.M.: Comparative genetics in the grasses. Proceedings of the National Academy of Sciences USA 95, 1971–1974 (1998)CrossRefGoogle Scholar
  8. 8.
    Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman and Company, New York (1979)MATHGoogle Scholar
  9. 9.
    Hannenhalli, S., Pevzner, P.A.: Transforming cabbage into turnip (polynomial algorithm for sorting signed permutations by reversals). Journal of the ACM 48, 1–27 (1999)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Jackson, B.N., Aluru, S., Schnable, P.S.: Consensus genetic maps: a graph theory approach. In: IEEE Computational Systems Bioinformatics Conference (CSB 2005), pp. 35–43 (2005)Google Scholar
  11. 11.
    Keller, B., Feuillet, C.: Colinearity and gene density in grass genomes. Trends Plant Sci. 5, 246–251 (2000)CrossRefGoogle Scholar
  12. 12.
    Lander, S.E., Green, P., Abrahamson, J., Barlow, A., Daly, M.J., et al.: MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1, 174–181 (1987)CrossRefGoogle Scholar
  13. 13.
    Menz, M.A., Klein, R.R., Mullet, J.E., Obert, J.A., Unruh, N.C., Klein, P.E.: A High-Density Genetic Map of Sorghum Bicolor (L.) Moench Based on 2926 Aflp, Rflp and Ssr Markers. Plant Molecular Biology (2002)Google Scholar
  14. 14.
    Pevzner, P.A., Tesler, G.: Human and mouse genomic sequences reveal extensive breakpoint reuse in mammalian evolution. Proc. Natl. Acad. Sci. USA 100, 7672–7677 (2003)CrossRefGoogle Scholar
  15. 15.
    Polacco, M.L., Coe, Jr., E.: IBM neighbors: a consensus GeneticMap (2002)Google Scholar
  16. 16.
    Sankoff, D., Zheng, C., Lenert, A.: Reversals of fortune. In: McLysaght, A., Huson, D.H. (eds.) RECOMB 2005. LNCS (LNBI), vol. 3678, pp. 131–141. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Yap, I.V., Schneider, D., Kleinberg, J., Matthews, D., Cartinhour, S., McCouch, S.R.: A graph-theoretic approach to comparing and integrating genetic, physical and sequence-based maps. Genetics 165, 2235–2247 (2003)Google Scholar
  18. 18.
    Zheng, C., Lenert, A., Sankoff, D.: Reversal distance for partially ordered genomes. Bioinformatics 21 (in press, 2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guillaume Blin
    • 1
  • Eric Blais
    • 2
  • Pierre Guillon
    • 1
  • Mathieu Blanchette
    • 2
  • Nadia El-Mabrouk
    • 3
  1. 1.IGM-LabInfo – UMR CNRS 8049Université de Marne-la-ValléeFrance
  2. 2.McGill Centre for BioinformaticsMcGill UniversityCanada
  3. 3.DIROUniversité de MontréalCanada

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