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Optimal Spaced Seeds for Hidden Markov Models, with Application to Homologous Coding Regions

  • Broňa Brejová
  • Daniel G. Brown
  • Tomáš Vinař
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2676)

Abstract

We study the problem of computing optimal spaced seeds for detecting sequences generated by a Hidden Markov model. Inspired by recent work in DNA sequence alignment, we have developed such a model for representing the conservation between related DNA coding sequences. Our model includes positional dependencies and periodic rates of conservation, as well as regional deviations in overall conservation rate. We show that, for hidden Markov models in general, the probability that a seed is matched in a region can be computed efficiently, and use these methods to compute the optimal seed for our models. Our experiments on real data show that the optimal seeds are substantially more sensitive than the seeds used in the standard alignment program BLAST, and also substantially better than those of PatternHunter or WABA, both of which use spaced seeds. Our results offer the hope of improved gene finding due to fewer missed exons in DNA/DNA comparison, and more effective homology search in general, and may have applications outside of bioinformatics.

Keywords

Hide Markov Model Local Alignment Dynamic Programming Algorithm Protein Code Region Alignment Program 
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.
    S. F. Altschul, W. Gish, W. Miller, E. W. Myers, and D. J. Lipman. Basic local alignment search tool. Journal of Molecular Biology, 215(3):403–410, 1990.Google Scholar
  2. 2.
    A. Bairoch and R. Apweiler. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Research, 28(1):45–48, 2000.CrossRefGoogle Scholar
  3. 3.
    J. Buhler, U. Keich, and Y. Sun. Designing seeds for similarity search in genomic dna. In Proceedings of the 7th Annual International Conference on Computational Biology (RECOMB), 2003. To appear.Google Scholar
  4. 4.
    R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological sequence analysis. Cambridge University Press, 1998.Google Scholar
  5. 5.
    U. Keich, M. Li, B. Ma, and J. Tromp. On spaced seeds. Unpublished.Google Scholar
  6. 6.
    W. J. Kent and A. M. Zahler. Conservation, regulation, synteny, and introns in a large-scale C. briggsae-C. elegans genomic alignment. Genome Research, 10(8):1115–1125, 2000.CrossRefGoogle Scholar
  7. 7.
    I. Korf, P. Flicek, D. Duan, and M. R. Brent. Integrating genomic homology into gene structure prediction. Bioinformatics, 17Suppl 1:S140–8, 2001.Google Scholar
  8. 8.
    B. Ma, J. Tromp, and M. Li. PatternHunter: faster and more sensitive homology search. Bioinformatics, 18(3):440–445, March 2002.CrossRefGoogle Scholar
  9. 9.
    L._R. Rabiner. A tutorial on Hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–285, 1989.CrossRefGoogle Scholar
  10. 10.
    Z. Yang. Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites. Molecular Biology and Evolution, 10(6):1396–1401, 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Broňa Brejová
    • 1
  • Daniel G. Brown
    • 1
  • Tomáš Vinař
    • 1
  1. 1.School of Computer ScienceUniversity of WaterlooWaterlooCanada

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