Evaluating the robustness of phylogenetic methods to among-site variability in substitution processes

Philosophical Transactions of the Royal Society B: Biological Sciences

Volumn 363, Issue 1512, 2008, Pages 4013-4021

  Holder, Mark T ^a   Zwickl, Derrick J ^a   Dessimoz, Christophe ^b,c  

^a UNIVERSITY OF KANSAS   (United States)

^b ETH ZURICH   (Switzerland)

^c SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

Author keywords

Codon model; Mixture model; Partitioned model; Phylogenetic inference; RY coding; Simulation

Indexed keywords

COMPUTER SIMULATION; EVOLUTION; HETEROGENEITY; MODELING; PHYLOGENY;

MAMMALIA;

EID: 57149093199     PISSN: 09628436     EISSN: None     Source Type: Journal    
DOI: 10.1098/rstb.2008.0162     Document Type: Article

References (50)

1. Akaike, H. 1973 Information theory as an extension of the maximum likelihood principle. In Proc. 2nd Int. Symp. on Information Theory (eds B. N. Petrov & F. Csaki), pp. 267-281. Budapest, Hungary: Akademiai Kiado.
2. Berry, V. & Gascuel, O. 1996 On the interpretation of bootstrap trees: appropriate threshold of clade selection and induced gain. Mol. Biol. Evol. 13, 999-1011.
3. Brent, R. P. 1973 Algorithms for minimization without derivatives. Englewood Cliffs, NJ: Prentice-Hall.
4. Desper, R. & Gascuel, O. 2002 Fast and accurate phylogeny reconstruction algorithms based on the minimum-evolution principle. J. Comput. Biol. 9, 687-705.
5. Desper, R. & Gascuel, O. 2004 Theoretical foundation of the balanced minimum evolution method of phylogenetic inference and its relationship to weighted least-squares tree fitting. Mol. Biol. Evol. 21, 587-598.
6. Dessimoz, C., Cannarozzi, G., Gil, M., Margadant, D., Roth, A., Schneider, A. & Gonnet, G. 2005 OMA, a comprehensive, automated project for the identification of orthologs from complete genome data: introduction and first achievements. In Proc. RECOMB 2005 Workshop on Comparative Genomics (eds A. McLysath & D. H. Huson). Lecture Notes in Bioinformatics, pp. 61-72. Berlin, Germany: Springer.
7. Felsenstein, J. 2003 Inferring phylogenies. Sunderland, MA: Sinauer Associates.
8. Felsenstein, J. 2004 PHYLIP (phylogeny inference package) v. 3.6. Seattle, WA: Department of Genetics, University of Washington.
9. Fitch, W. & Margoliash, E. 1967 Construction of phylogenetic trees. Science 155, 279-284.
10. Gascuel, O. 1997 BIONJ: an improved version of the NJ algorithm based on a simple model of sequence data. Mol. Biol. Evol. 14, 685-695.
11. Geyer, C. 1991 Markov chain Monte Carlo maximum likelihood. In Computing Science and Statistics: Proc. of the 23rd Symp. Interface, Seattle, WA, pp. 156-163. Fairfax Station, VA: Interface Foundation of North America.
12. Goldman, N. & Yang, Z. 1994 A codon-based model of nucleotide substitution for protein-coding DNA sequences. Mol. Biol. Evol. 11, 725-736.
13. Gonnet, G. H., Cohen, M. A. & Benner, S. A. 1992 Exhaustive matching of the entire protein sequence database. Science 256, 1443-1445.
14. Gonnet, G. H., Hallett, M. T., Korostensky, C. & Bernardin, L. 2000 DARWIN v. 2.0: an interpreted computer language for the biosciences. Bioinformatics 16, 101-103.
15. Guindon, S. & Gascuel, O. 2002 Efficient biased estimation of evolutionary distances when substitution rates vary across sites. Mol. Biol. Evol. 19, 534-543.
16. Halpern, A. L. & Bruno, W. J. 1998 Evolutionary distances for protein-coding sequences: modeling site-specific residue frequencies. Mol. Biol. Evol. 15, 910-917.
17. Hasegawa, M., Kishino, H. & Yano, T. 1985 Dating the human-ape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evol. 22, 160-174.
18. Hillis, D. M. 1996 Inferring complex phylogenies. Nature 383, 130-131.
19. Holder, M. T., Sukumaran, J. & Lewis, P. O. 2008 A justification for reporting majority-rule consensus tree in Bayesian phylogenetics. Syst. Biol. 57, 814-821.
20. Huelsenbeck, J. 1995 Performance of phylogenetic methods in simulation. Syst. Biol 44, 17-48.
21. Jukes, T. H. & Cantor, C. R. 1969 Evolution of protein molecules. In Mammalian protein metabolism (ed. H. N. Munro), pp. 21-32. New York, NY: Academic Press.
22. Kimura, M. 1980 A simple method for estimating evolutionary rate of base substitutions through comparative studies of nucleotide sequences. J. Mol. Evol. 16, 111-120.
23. Kishino, H., Thorne, J. L. & Bruno, W. J. 2001 Performance of a divergence time estimation method under a probabilistic model of rate evolution. Mol. Biol. Evol. 18, 352-361.
24. Kosiol, C., Holmes, I. & Goldman, N. 2007 An empirical codon model for protein sequence evolution. Mol. Biol. Evol. 24, 1464-1479.
25. Kuhner, M. K. & Felsenstein, J. 1994 A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates. Mol. Biol. Evol. 11, 459-468.
26. Lanave, C., Preparata, G., Saccone, C. & Serio, G. 1984 A new method for calculating evolutionary substitution rates. J. Mol. Evol. 20, 86-93.
27. Lartillot, N. & Phillipe, H. 2004 A Bayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol. Biol. Evol. 21, 1095-1109.
28. Lartillot, N., Blanquart, S. & Lepage, T. 2004 PHYLOBAYES 2.3. See http://www.lirmm.fr/mab/article.php3? id_articleZ329.
29. Liò, P., Goldman, N., Thorne, J. L. & Jones, D. T. 1998 PASSML: combining evolutionary inference and protein secondary structure prediction. Bioinformatics 14, 726-733.
30. Martin, A. & Palumbi, S. 1993 Protein evolution in different cellular environments: cytochrome b in sharks and mammals. Mol. Biol. Evol. 10, 873.
31. Naylor, G. J. P. & Brown, W. M. 1998 Amphioxus mitochondrial DNA, chordate phylogeny, and the limits of inference based on comparisons of sequences. Syst. Biol. 47, 61-76.
32. Powell, M. J. D. 1964 An efficient method for finding the minimum of a function of several variables without calculating derivatives. Comput. J. 7, 155-162.
33. Ren, F., Tanaka, H. & Yang, Z. 2005 An empirical examination of the utility of codon-substitution models in phylogeny reconstruction. Syst. Biol. 54, 808-818.
34. Robinson, D. F. & Foulds, L. R. 1981 Comparison of phylogenetic trees. Math. Biosci. 53, 131-147.
35. Ronquist, F. R. & Huelsenbeck, J. P. 2001 MRBAYES 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 754-755.
36. Rzhetsky, A. & Nei, M. 1992 Statistical properties of the ordinary least-squares, generalized least-squares, and minimum-evolution methods of phylogenetic inference. J. Mol. Evol. 35, 367-375.
37. Saitou, N. & Nei, M. 1987 The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406-425.
38. Schoniger, M. & von Haeseler, A. 1995 Performance of the maximum likelihood, neighbor joining, and maximum parsimony methods when sequence sites are not independent. Syst. Biol. 44, 533-547.
39. Sullivan, J., Abdo, Z., Joyce, P. & Swofford, D. 2005 Evaluating the performance of a successive-approximations approach to parameter optimization in maximum-likelihood phylogeny estimation. Mol. Biol. Evol. 22, 1386-1392.
40. Swofford, D. L. 2001 PAUP*: phylogenetic analysis using parsimony (and other methods), version 4.0.b10. Sunderland, MA: Sinauer.
41. Takahashi, K. & Nei, M. 2000 Efficiencies of fast algorithms of phylogenetic inference under the criteria of maximum parsimony, minimum evolution, and maximum likelihood when a large number of sequences are used. Mol. Biol. Evol. 17, 1251-1258.
42. Tamura, K. & Nei, M. 1993 Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10, 512-526.
43. Tavaré, S. 1986 Some probabilistic and statistical problems in the analysis of DNA sequences. In Some mathematical questions in biology - DNA sequence analysis (ed. R. M. Miura), Providence, RI: American Mathematical Society.
44. Voelker, G. & Edwards, S. V. 1998 Can weighting improve bushy trees? Models of cytochrome b evolution and the molecular systematics of pipits and wagtails (Aves: Motacillidae). Syst. Biol. 47, 589-603.
45. Whelan, S. & Goldman, N. 2001 A general empirical model of protein evolution derived from multiple protein families using a maximum-likelihood approach. Mol. Biol. Evol. 18, 691-699.
46. Yang, Z. 1994 Maximum likelihood phylogenetic estimation from DNA sequences with variable rates over sites: approximate methods. J. Mol. Evol. 39, 306-314.
47. Yang, Z., Goldman, N. & Friday, A. 1995 Maximum likelihood trees from DNA sequences: a peculiar statistical estimation problem. Syst. Biol. 11, 316-324.
48. Yang, Z., Nielsen, R., Goldman, N. & Pedersen, A. 2000 Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics 155, 431-449.
49. Yule, G. U. 1925 A mathematical theory of evolution, based on the conclusions of Dr. J. C. Willis, F.R.S. Phil. Trans. R. Soc. B 213, 21-87.
50. Zwickl, D. J. 2006 Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion. PhD thesis, University of Texas, Austin, TX.