A Hidden Markov Model approach to variation among sites in rate of evolution

Molecular Biology and Evolution

Volumn 13, Issue 1, 1996, Pages 93-104

  Felsenstein, Joseph ^a   Churchill, Gary A ^b  

^a UNIVERSITY OF WASHINGTON   (United States)

^b Department of Population Medicine and Diagnostic Sciences   (United States)

Author keywords

Hidden Markov Model; likelihood; molecular sequences; phylogeny; rate of evolution; statistical inference

Indexed keywords

DNA;

ARTICLE; DNA SEQUENCE; EVOLUTION; GENETIC VARIABILITY; MAXIMUM LIKELIHOOD METHOD; MOLECULAR GENETICS; NUCLEOTIDE SEQUENCE; PHYLOGENY; PROBABILITY; STATISTICAL MODEL;

MAMMALIA;

EID: 0030034795     PISSN: 07374038     EISSN: None     Source Type: Journal    
DOI: 10.1093/oxfordjournals.molbev.a025575     Document Type: Article

References (29)

1. BALDI, P., Y. CHAUVIN, T. HUNKAPILLER, and M. A. MCCLURE. 1994. Hidden Markov models of biological primary sequence information. Proc. Natl. Acad. Sci. USA 91:1059-1063.
2. BAUM, L. E., and T. PETRIE. 1966. Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Stat. 37:1554-1563.
3. BAUM, L. E., T. PETRIE, G. SOULES, and N. WEISS. 1970. A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Stat. 41:164-171.
4. CHURCHILL, G. A. 1989. Stochastic models for heterogenous DNA sequences. Bull. Math. Biol. 51:79-94.
5. DEMPSTER, A. P., M. N. LAIRD, and D. B. RUBIN. 1977. Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. B 39:1-38.
6. FELSENSTEIN, J. 1973. Maximum-likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. Syst. Zool. 22:240-249.
7. _. 1981. Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 17:368-376.
8. FORNEY, G. D., JR. 1973. The Viterbi algorithm. Proc. IEEE 61:268-278.
9. HASEGAWA, M., H. KISHINO, and T. YANO. 1988. Phylogenetic inference from DNA sequence data. Pp. 1-13 in K. MATUSITA, ed. Statistical theory and data analysis II: Proceedings of the Second Pacific Area Statistical Conference, North-Holland, Amsterdam.
10. HAUSSLER, D., A. KROGH, I. S. MIAN, and K. SJOLANDER. 1993. Protein modeling using hidden Markov models: analysis of globins. Pp. 792-802 in T. N. MUDGE, V. MILUTINOVIC, and L. HUNTER, eds. Proceeding of the Twenty-Sixth Hawaii International Conference on System Sciences. IEEE, Los Alamitos, Calif.
11. HEIN, J. 1993. A heuristic method to reconstruct the history of sequences subject to recombination. J. Mol. Evol. 36:369-405.
12. HIGGINS, D. G., and P. M. SHARP. 1989. Fast and sensitive multiple sequence alignments on a microcomputer. Comput. Appl. Biosci. 5:151-153.
13. JIN, L., and M. NEI. 1990. Limitations of the evolutionary parsimony method of phylogenetic analysis. Mol. Biol. Evol. 7:82-102.
14. JUKES, T. H., and C. CANTOR. 1969. Evolution of protein molecules. Pp. 21-132 in M. N. MUNRO, ed. Mammalian protein metabolism. Academic Press, New York.
15. KELLY, C., and J. RICE. 1995. Modeling nucleotide evolution: a heterogeneous rate analysis. Math. Biosci. (in press).
16. KIMURA, M. 1980. A simple model for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16:111-120.
17. KISHINO, H., and M. HASEGAWA. 1989. Evaluation of the maximum likelihood estimate of the evolutionary tree topologies from DNA sequence data, and the branching order in Hominoidea. J. Mol. Evol. 29:170-179.
18. KROGH, A., M. BROWN, I. S. MIAN, K. SJOLANDER, and D. HAUSSLER. 1994. Hidden Markov models in computational biology. Applications to protein modeling. J. Mol. Biol. 235:1501-1531.
19. NEYMAN, J. 1971. Molecular studies of evolution: a source of novel statistical problems. Pp. 1-27 in S. S. GUPTA and J. YACKEL, eds. Statistical decision theory and related topics. Academic Press, New York.
20. OLSEN, G. J. 1987. Earliest phylogenetic branchings: comparing rRNA-based evolutionary trees inferred with various techniques. Cold Spring Harbor Symp. Quant. Biol. 52: 825-837.
21. REMPE, U. 1988. Characterizing DNA variability by stochastic matrices. Pp. 375-384 in H. H. BOCK, ed. Classification and related methods of data analysis. Proceedings of the First Conference of the International Federation of Classication Societies (IFCS), Technical University of Aachen, F.R.G., 29 June-1 July 1987. North-Holland, Amsterdam.
22. SWOFFORD, D. L., and G. J. OLSEN. 1990. Phylogeny reconstruction. Chapter 11, Pp. 411-501 in D. M. HILLIS and C. MORITZ, eds. Molecular systematics. Sinauer Associates. Sunderland, Mass.
23. TILLIER, E. R. M. 1994. Maximum likelihood with multiparameter models of substitution. J. Mol. Evol. 39:409-417.
24. TILLIER, E. R. M., and R. A. COLLINS. 1995. Neighbor-joining and maximum likelihood with RNA sequences: addressing the inter-dependence of sites. Mol. Biol. Evol. 12:7-15.
25. VITERBI, A. J. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory IT-13:260-269.
26. VON HAESELER, A., and G. A. CHURCHILL. 1993. Network models for sequence evolution. J. Mol. Evol. 37:77-85.
27. YANG, Z. 1993. Maximum-likelihood estimation of phylogeny from DNA sequences when substitution rates differ over sites. Mol. Biol. Evol. 10:1396-1401.
28. _. 1994. Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J. Mol. Evol. 39:306-314.
29. _. 1995. A space-time process model for the evolution of DNA sequences. Genetics 139:993-1005.