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Flexible Stochastic Local Search for Haplotype Inference

  • Luca Di Gaspero
  • Andrea Roli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5851)

Abstract

Haplotype Inference is a challenging problem in bioinformatics that consists in inferring the basic genetic constitution of diploid organisms on the basis of their genotype. This information allows researchers to perform association studies for the genetic variants involved in diseases and the individual responses to therapeutic agents. A notable approach to the problem is to encode it as a combinatorial problem (under certain hypotheses, such as the pure parsimony of the entropy minimization criteria) and to solve it using off-the-shelf combinatorial optimization techniques.

In this paper, we present and discuss an approach based on local search metaheuristics. A flexible solver is designed to tackle the Haplotype Inference under the criterion of choice, that could be defined by the user. We test our approach by solving instances from common Haplotype Inference benchmarks both under the hypothesis of pure parsimony and entropy minimization. Results show that the approach achieves a good trade-off between solution quality and execution time and compares favorably with the state of the art.

Keywords

Local Search Tabu Search Entropy Minimization Distinct Haplotype Haplotype Inference 
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.
    Birattari, M.: On the estimation of the expected performance of a metaheuristic on a class of instances. how many instances, how many runs? Technical Report TR/IRIDIA/2004-01, IRIDIA, Univerisé Libre de Bruxelles (2004)Google Scholar
  2. 2.
    Blum, C., Roli, A.: Metaheuristics in combinatorial optimization: Overview and conceptual comparison. ACM Computing Surveys 35(3), 268–308 (2003)CrossRefGoogle Scholar
  3. 3.
    Brown, D.G., Harrower, I.M.: Integer programming approaches to haplotype inference by pure parsimony. IEEE/ACM Transactions on Computational Biology and Bioinformatics 3(2), 141–154 (2006)CrossRefGoogle Scholar
  4. 4.
    Clark, A.G.: Inference of haplotypes from PCR-amplified samples of diploid populations. Molecular Biology and Evolution 7, 111–122 (1990)Google Scholar
  5. 5.
    Di Gaspero, L., Roli, A.: Stochastic local search for large-scale instances of the haplotype inference problem by pure parsimony. Journal of Algorithms in Logic, Informatics and Cognition 63(1-3), 55–69 (2008)zbMATHGoogle Scholar
  6. 6.
    Di Gaspero, L., Roli, A.: A preliminary analysis on metaheuristics methods applied to the haplotype inference problem. Technical Report DEIS-LIA-07-006, University of Bologna (Italy). LIA Series no. 84 (2007)Google Scholar
  7. 7.
    Di Gaspero, L., Schaerf, A.: EasyLocal++: An object-oriented framework for flexible design of local search algorithms. Software—Practice and Experience 33(8), 733–765 (2003)CrossRefGoogle Scholar
  8. 8.
    Graça, A., Marques-Silva, J., Lynce, I., Oliveira, A.L.: Efficient haplotype inference with pseudo-boolean optimization. In: Anai, H., Horimoto, K., Kutsia, T. (eds.) AB 2007. LNCS, vol. 4545, pp. 125–139. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Gusev, A., Pasaniuc, B., Mandoiu, I.: Highly scalable genotype phasing by entropy minimization. IEEE/ACM Trans. on Computational Biology and Bioinformatics 5(2), 252–261 (2008)CrossRefGoogle Scholar
  10. 10.
    Gusfield, D.: Haplotype inference by pure parsimony. In: Baeza-Yates, R., Chávez, E., Crochemore, M. (eds.) CPM 2003. LNCS, vol. 2676, pp. 144–155. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Halldórsson, B.V., Bafna, V., Edwards, N., Lippert, R., Yooseph, S., Istrail, S.: A survey of computational methods for determining haplotypes. In: Istrail, S., Waterman, M.S., Clark, A. (eds.) DIMACS/RECOMB Satellite Workshop 2002. LNCS (LNBI), vol. 2983, pp. 26–47. Springer, Heidelberg (2002)Google Scholar
  12. 12.
    Halperin, E., Karp, R.M.: The minimum-entropy set cover problem. Theoretical Computer Science 348(2-3), 240–250 (2005)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Huang, Y.-T., Chao, K.-M., Chen, T.: An approximation algorithm for haplotype inference by maximum parsimony. In: Haddad, H., Liebrock, L.M., Omicini, A., Wainwright, R.L. (eds.) Proceedings of the 2005 ACM Symposium on Applied Computing (SAC 2005), pp. 146–150. ACM Press, New York (2005)CrossRefGoogle Scholar
  14. 14.
    Kalpakis, K., Namjoshi, P.: Haplotype phasing using semidefinite programming. In: BIBE, pp. 145–152. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  15. 15.
    Lancia, G., Pinotti, M.C., Rizzi, R.: Haplotyping populations by pure parsimony: Complexity of exact and approximation algorithms. INFORMS Journal on Computing 16(4), 348–359 (2004)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Lynce, I., Marques-Silva, J.: Efficient haplotype inference with boolean satisfiability. In: Proceedings of the 21st National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference. AAAI Press, Menlo Park (2006)Google Scholar
  17. 17.
    Lynce, I., Marques-Silva, J.: SAT in bioinformatics: Making the case with haplotype inference. In: Biere, A., Gomes, C.P. (eds.) SAT 2006. LNCS, vol. 4121, pp. 136–141. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  18. 18.
    The International HapMap Consortium. The international HapMap project. Nature 426, 789–796 (2003)Google Scholar
  19. 19.
    The International HapMap Consortium. A haplotype map of the human genome. Nature 437 (2005)Google Scholar
  20. 20.
    Wang, R.-S., Zhang, X.-S., Sheng, L.: Haplotype inference by pure parsimony via genetic algorithm. In: Zhang, X.-S., Liu, D.-G., Wu, L.-Y. (eds.) Operations Research and Its Applications: the Fifth International Symposium (ISORA 2005), Tibet, China, August 8-13. Lecture Notes in Operations Research, vol. 5, pp. 308–318. Beijing World Publishing Corporation, Beijing (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Luca Di Gaspero
    • 1
  • Andrea Roli
    • 2
  1. 1.DIEGMUniversity of UdineUdineItaly
  2. 2.DEISUniversity of BolognaCesenaItaly

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