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Statistical Alignment: Recent Progress, New Applications, and Challenges

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Statistical Methods in Molecular Evolution

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References

  1. S. F. Altschul, W. Gisha, W. Miller, E. W. Meyers, and D. J. Lipman. Basic local alignment search tool. J. Mol. Biol., 215(3):403–410, 1990.

    Article  Google Scholar 

  2. S. F. Altschul, T. L. Madden, A. A. Schaffer, J. Zhang, Z. Zhang, W. Miller, and D. J. Lipman. Gapped-BLAST and PSI-BLAST: A new generation of protein database search programs. Nucleic Acids Res., 25:3389–3402, 1997.

    Article  Google Scholar 

  3. H. Carillo and D. Lipman. The multiple alignment problem in biology. SIAM J. Appl. Math., 48:1073–1082, 1988.

    MathSciNet  Google Scholar 

  4. A. J. Drummond, G. K. Nicholls, A. G. Rodrigo, and W. Solomon. Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data. Genetics, 161(3):1307–1320, 2002.

    Google Scholar 

  5. A. J. Drummond and A. Rambaut. BEAST v1.0.3. http://evolve.zoo.ox.ac.uk/beast/, 2003.

    Google Scholar 

  6. R. Durbin, S. Eddy, A. Krogh, and G. Mitchison. Biological sequence analysis. Cambridge University Press, Camebridge, 1998.

    Google Scholar 

  7. S. Eddy. HMMER: Profile hidden Markov models for biological sequence analysis (http://hmmer.wustl.edu/), 2001.

    Google Scholar 

  8. J. Felsenstein. Evolutionary trees from DNA sequences: A maximum likelihood approach. J. Mol. Evol., 17:368–376, 1981.

    Article  Google Scholar 

  9. J. Felsenstein. The troubled growth of statistical phylogenetics. Syst. Biol., 50:465–467, 2001.

    Google Scholar 

  10. O. Gotoh. An improved algorithm for matching biological sequences. J. Mol. Biol., 162:705–708, 1982.

    Article  Google Scholar 

  11. J. Hein. A unified approach to phylogenies and alignments. Meth. Enzym., 183:625–644, 1990.

    Google Scholar 

  12. J. Hein. An algorithm for statistical alignment of sequences related by a binary tree. In Pacific Symposium on Biocomputing, pages 179–190. World Scientific, Singapore, 2001.

    Google Scholar 

  13. J. Hein, J. L. Jensen, and C. N. S. Pedersen. Recursions for statistical multiple alignment. Proc. Natl. Acad. Sci. USA, 100(25):14960–14965, 2003.

    Article  Google Scholar 

  14. J. Hein, C. Wiuf, B. Knudsen, M. B. Møller, and G. Wibling. Statistical alignment: Computational properties, homology testing and goodness-of-fit. J. Mol. Biol., 302:265–279, 2000.

    Article  Google Scholar 

  15. D. S. Hirschberg. A linear space algorithm for computing maximal common subsequences. Communi. ACM, 18:341–343, 1975.

    MATH  MathSciNet  Google Scholar 

  16. A. Hobolth and J. L. Jensen. Applications of hidden Markov models for comparative gene structure prediction. Technical Report MPS-RR 2003-35, MaPhySto, Aarhus, Denmark, 2003.

    Google Scholar 

  17. I. Holmes. Using guide trees to construct multiple-sequence evolutionary HMMs. Bioinformatics, 19:i147–i157, 2003.

    Article  Google Scholar 

  18. I. Holmes and W. J. Bruno. Evolutionary HMMs: A Bayesian approach to multiple alignment. Bioinformatics, 17(9):803–820, 2001.

    Article  Google Scholar 

  19. J. L. Jensen and J. Hein. Gibbs sampler for statistical multiple alignment. Stat. Sinica, 2004. (In press).

    Google Scholar 

  20. K. Karplus, C. Barrett, and R. Hughey. Hidden Markov Models for detecting remote protein homologies. Bioinformatics, 14:846–856, 1998.

    Article  Google Scholar 

  21. B. Knudsen and M. M. Miyamoto. Sequence alignments and pair Hidden Markov Models using evolutionary history. J. Mol. Biol., 333:453–460, 2003.

    Article  Google Scholar 

  22. A. Krogh. Two methods for improving performance of a HMM and their application for gene finding. In T. Gaasterland, P. Karp, K. Karplus, C. Ouzounis, C. Sander, and A. Valencia, editors, Proceedings of the Fifth International Conference on Intelligent Systems for Molecular Biology, pages 179–186, Menlo Park, CA, 1997. AAAI Press.

    Google Scholar 

  23. M. S. Y. Lee. Unalignable sequences and molecular evolution. Trends Ecol. Evol., 16:681–685, 2001.

    Article  Google Scholar 

  24. D. J. Lipman, S. F. Altschul, and J. D. Kececioglu. A tool for multiple sequence alignment. Proc. Natl. Acad. Sci. USA, 86:4412–4415, 1989.

    Google Scholar 

  25. A. Löytynoja and M. C. Milinkovitch. A hidden Markov model for progressive multiple alignment. Bioinformatics, 19:1505–1513, 2003.

    Google Scholar 

  26. G. A. Lunter, I. Miklós, A. Drummond, J. L. Jensen, and J. Hein. Bayesian phylogenetic inference under a statistical indel model. In Proceedings of WABI’03, volume 2812 of Lecure Notes in Bioinformatics, pages 228–244, Heidelberg, 2003. Springer-Verlag.

    Google Scholar 

  27. G. A. Lunter, I. Miklós, Y. S. Song, and J. Hein. An efficient algorithm for statistical multiple alignment on arbitrary phylogenetic trees. J. Comp. Biol., 10(6):869–889, 2004.

    Google Scholar 

  28. D. Metzler. Statistical alignment based on fragment insertion and deletion models. Bioinformatics, 19(4):490–499, 2003.

    Article  Google Scholar 

  29. D. Metzler, R. Fleissner, A. Wakolbinger, and A. von Haeseler. Assessing variability by joint sampling of alignments and mutation rates. J. Mol. Evol., 53:660–669, 2001.

    Article  Google Scholar 

  30. I. M. Meyer and R. Durbin. Comparative ab initio prediction of gene structures using pair HMMs. Bioinformatics, 18(10):1309–1318, 2002.

    Article  Google Scholar 

  31. I. Miklós. An improved algorithm for statistical alignment of sequences related by a star tree. Bull. Math. Biol., 64:771–779, 2002.

    Google Scholar 

  32. I. Miklós, G. A. Lunter, and I. Holmes. A “long indel” model for evolutionary sequence alignment. Mol. Biol. Evol., 21(3):529–540, 2004.

    Google Scholar 

  33. G. Mitchison. A probabilistic treatment of phylogeny and sequence alignment. J. Mol. Evol., 49:11–22, 1999.

    Google Scholar 

  34. B. Morgenstern. DIALIGN 2: Improvement of the segment-to-segment approach to multiple sequence alignment. Bioinformatics, 15:211–218, 1999.

    Article  Google Scholar 

  35. E. W. Myers. An O(ND) difference algorithm and its variations. Algorithmica, 1:251–266, 1986.

    Article  MATH  Google Scholar 

  36. S. B. Needleman and C. D. Wunsch. A general method applicable to the search for similarities in the amino acid sequences in two proteins. J. Mol. Biol., 48:443–453, 1970.

    Article  Google Scholar 

  37. C. Notredame, D. Higgins, and J. Heringa. T-Coffee: A novel method for multiple sequence alignments. J. Mol. Biol., 302:205–217, 2000.

    Article  Google Scholar 

  38. B. Qian and R. A. Goldstein. Distribution of indel lengths. Proteins Struct. Func. Gen., 45:102–104, 2001.

    Google Scholar 

  39. D. Sankoff. Minimal mutation trees of sequences. SIAM J. Appl. Math., 28:35–42, 1975.

    Article  MATH  MathSciNet  Google Scholar 

  40. M. Steel and J. Hein. Applying the Thorne-Kishino-Felsenstein model to sequence evolution on a star-shaped tree. Appl. Math. Lett., 14:679–684, 2001.

    Article  MathSciNet  Google Scholar 

  41. J. D. Thompson, D. G. Higgins, and T. J. Gibson. CLUSTAL W: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res., 22:4673–4680, 1994.

    Google Scholar 

  42. J. L. Thorne, H. Kishino, and J. Felsenstein. An evolutionary model for maximum likelihood alignment of DNA sequences. J. Mol. Evol., 33:114–124, 1991.

    Article  Google Scholar 

  43. J. L. Thorne, H. Kishino, and J. Felsenstein. Inching toward reality: An improved likelihood model of sequence evolution. J. Mol. Evol., 34:3–16, 1992.

    Article  Google Scholar 

  44. E. Ukkonen. Algorithms for approximate string matching. Inf. Control, 64:100–118, 1985.

    Article  MATH  MathSciNet  Google Scholar 

  45. A. von Haeseler and M. Vingron. Towards integration of multiple alignment and phylogenetic tree construction. J. Comp. Biol., 4(1):23–34, 1997.

    Google Scholar 

  46. L. Wang and T. Jiang. On the complexity of multiple sequence alignment. J. Comp. Biol., 1:337–348, 1994.

    Google Scholar 

  47. S. Wu, U. Manber, G. Myers, and W. Miller. An O(NP) sequence comparison algorithm. Inf. Process. Lett., 35:317–323, 1990.

    MathSciNet  Google Scholar 

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Lunter, G., Drummond, A.J., Miklós, I., Hein, J. (2005). Statistical Alignment: Recent Progress, New Applications, and Challenges. In: Statistical Methods in Molecular Evolution. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/0-387-27733-1_14

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