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Probabilistic ancestral sequences and multiple alignments

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Algorithm Theory — SWAT'96 (SWAT 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1097))

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Abstract

An evolutionary configuration (EC) is a set of aligned sequences of characters (possibly representing amino acids, DNA, RNA or natural language). We define the probability of an EC, based on a given phylogenetic tree and give an algorithm to compute this probability efficiently. From these probabilities, we can compute the most likely sequence at any place in the phylogenetic tree, or its probability profile. The probability profile at the root of the tree is called the probabilistic ancestral sequence. By computing the probability of an EC, we can find by dynamic programming alignments over two subtrees. This gives an algorithm for computing multiple alignments. These multiple alignments are maximum likelihood, and are a compatible generalization of two sequence alignments.

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References

  1. L. Allison and C.S. Wallace. The posterior probability distribution of alignments and its application to parameter estimation of evolutionary trees and to optimization of multiple alignments. J. Molecular Evolution, 39:418–430, 1994.

    Google Scholar 

  2. Lachlan H. Bell, John R. Coggins, and James E. Milner-White. Mix'n'match: an improved multiple sequence alignment procedure for distantly related proteins using secondary structure predictions, designed to be independent of the choice of gap penalty and scoring matrix. Protein Engineering, 6(7):683–690, 1993.

    PubMed  Google Scholar 

  3. Steven A. Benner, Mark A. Cohen, and Gaston H. Gonnet. Empirical and structural models for insertions and deletions in the divergent evolution of proteins. J. Molecular Biology, 229:1065–1082, 1993.

    Google Scholar 

  4. Steven A. Benner, Mark A. Cohen, and Gaston H. Gonnet. Amino acid substitution during functionally constrained divergent evolution of protein sequences. Protein Engineering, 7(11), 1994.

    Google Scholar 

  5. Humberto Carillo and David. Lipman. The multiple sequence alignment problem in biology. SIAM J. Appl. Math., 48(5):1073–1082, 1988.

    Google Scholar 

  6. Margaret O. Dayhoff, R. M. Schwartz, and B. C. Orcutt. A model for evolutionary change in proteins. In Margaret O. Dayhoff, editor, Atlas of Protein Sequence and Structure, volume 5, pages 345–352. National Biochemical Research Foundation, Washington DC, 1978.

    Google Scholar 

  7. Adam Godzik and Jeffrey Skolnick. Flexible algorithm for direct multiple alignment of protein structures and sequences. CABIOS, 10(6):587–596, 1994.

    PubMed  Google Scholar 

  8. Gaston H. Gonnet, Mark A. Cohen, and Steven A. Benner. Exhaustive matching of the entire protein sequence database. Science, 256:1443–1445, 1992.

    PubMed  Google Scholar 

  9. Gaston H. Gonnet and Chantal Korostensky. Evaluation measures of multiple sequence alignments. In preparation, 1996.

    Google Scholar 

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

    PubMed  Google Scholar 

  11. Sandeep K. Gupta, John Kececioglu, and Alejandro A. Schaffer. Improving the practical space and time efficiency of the shortest-paths approach to sum-of-pairs multiple sequence alignment. J. Computational Biology, 1996. To appear.

    Google Scholar 

  12. Xiaoqiu Huang. On global sequence alignment. CABIOS, 10(3):227–235, 1994.

    PubMed  Google Scholar 

  13. Charles E. Lawrence, Stephen F. Altschul, Mark S. Boguski, Jun S. Liu, Andrew F. Neuwald, and John C. Wootton. Detecting subtle sequence signals: A gibbs sampling strategy for multiple alignment. Science, 262:208–214, October 1993.

    PubMed  Google Scholar 

  14. David J. Lipman, Stephen F. Altschul, and John D. Kececioglu. A tool for multiple sequence alignment. Proc. Natl. Acad. Sci. USA, 86:4412–4415, June 1989.

    PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  16. Gregory D. Schuler, Stephen F. Altschul, and David J. Lipman. A work-bench for multiple alignment construction and analysis. PROTEINS: Structure, Function, and Genetics, 9:180–190, 1991.

    Google Scholar 

  17. Peter H. Sellers. On the theory and computation of evolutionary distances. SIAM J Appl. Math., 26(4):787–793, Jun 1974.

    Google Scholar 

  18. Temple F. Smith and Michael S. Waterman. Identification of common molecular subsequences. J. Mol. Biol., 147:195–197, 1981.

    PubMed  Google Scholar 

  19. J.D. Thompson, D.G. Higgins, and T.J Gibson. Clustal w: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, positions-specific gap penalties and weight matrix choice. Nucleic Acids Research, 22:4673–4680, 1994.

    PubMed  Google Scholar 

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Authors and Affiliations

  1. Informatik Organic Chemistry E.T.H., Zurich, Switzerland

    Gaston H. Gonnet & Steven A. Benner

Authors
  1. Gaston H. Gonnet
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  2. Steven A. Benner
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Rolf Karlsson Andrzej Lingas

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© 1996 Springer-Verlag Berlin Heidelberg

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Gonnet, G.H., Benner, S.A. (1996). Probabilistic ancestral sequences and multiple alignments. In: Karlsson, R., Lingas, A. (eds) Algorithm Theory — SWAT'96. SWAT 1996. Lecture Notes in Computer Science, vol 1097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61422-2_147

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  • DOI: https://doi.org/10.1007/3-540-61422-2_147

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61422-7

  • Online ISBN: 978-3-540-68529-6

  • eBook Packages: Springer Book Archive

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