Keywords and Synonyms
Evolutionary hidden Markov models
Problem Definition
The three main types of mutations modifying biological sequences are insertions, deletions and substitutions. The simplest model involving these three types of mutations is the so-called Thorne–Kishino–Felsenstein model [13]. In this model, the characters of a sequence evolve independently. Each character in the sequence can be substituted with another character according to a prescribed reversible time-continuous Markov model on the possible characters. Insertion-deletions are modeled as a birth-death process, characters evolve independently and identically, with insertion and deletion rates λ and μ.
The multiple statistical alignment problem is to calculate the likelihood of a set of sequences, namely, what is the probability of observing a set of sequences, given all the necessary parameters that describe the evolution of sequences. Hein, Jensen and Pedersen were the first who gave an algorithm to calculate...
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Csűrös, M., Miklós, I.: A probabilistic model for gene content evolution with duplication, loss, and horizontal transfer. In: Lecture Notes in Bioinformatics, Proceedings of RECOMB2006, vol. 3909, pp. 206–220 (2006)
Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological sequence analysis. Cambridge University Press, Cambridge, UK (1998)
Felsenstein, J.: Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 17, 368–376 (1981)
Hein, J., Jensen, J., Pedersen, C.: Recursions for statistical multiple alignment. PNAS 100, 14,960–14,965 (2003)
Holmes, I.: Using guide trees to construct multiple-sequence evolutionary hmms. Bioinform. 19, i147–i157 (2003)
Holmes, I., Bruno, W.J.: Evolutionary HMMs: a Bayesian approach to multiple alignment. Bioinform. 17(9), 803–820 (2001)
Jukes, T.H., Cantor, C.R.: Evolution of protein molecules. In: Munro (ed.) Mammalian Protein Metabolism, pp. 21–132. Acad. Press (1969)
Lunter, G., Miklós, I., Drummond, A., Jensen, J., Hein, J.: Bayesian phylogenetic inference under a statistical indel model. In: Lecture Notes in Bioinformatics, Proceedings of WABI2003, vol. 2812, pp. 228–244 (2003)
Lunter, G., Miklós, I., Drummond, A., Jensen, J., Hein, J.: Bayesian coestimation of phylogeny and sequence alignment. BMC Bioinformatics (2005)
Lunter, G.A., Miklós, I., Song, Y.S., Hein, J.: An efficient algorithm for statistical multiple alignment on arbitrary phylogenetic trees. J. Comp. Biol. 10(6), 869–889 (2003)
Metzler, D., Fleißner, R., Wakolbringer, A., von Haeseler, A.: Assessing variability by joint sampling of alignments and mutation rates. J. Mol. Evol. 53, 660–669 (2001)
Pereira, F., Riley, M.: Speech recognition by composition of weighted finite automata. In: Finite-State Language Processing, pp. 149–173. MIT Press, Cambridge (1997)
Thorne, J.L., Kishino, H., Felsenstein, J.: An evolutionary model for maximum likelihood alignment of DNA sequences. J. Mol. Evol. 33, 114–124 (1991)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag
About this entry
Cite this entry
Miklós, I. (2008). Statistical Multiple Alignment. In: Kao, MY. (eds) Encyclopedia of Algorithms. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30162-4_400
Download citation
DOI: https://doi.org/10.1007/978-0-387-30162-4_400
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-30770-1
Online ISBN: 978-0-387-30162-4
eBook Packages: Computer ScienceReference Module Computer Science and Engineering