Probabilistic graphical model representation in phylogenetics

Systematic Biology

Volumn 63, Issue 5, 2014, Pages 753-771

  Höhna, Sebastian ^a,b   Heath, Tracy A ^c,d   Boussau, Bastien ^c,e   Landis, Michael J ^c   Ronquist, Fredrik ^f   Huelsenbeck, John P ^c,g  

^a STOCKHOLM UNIVERSITY   (Sweden)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

^d UNIVERSITY OF KANSAS   (United States)

^e UNIVERSITÉ DE LYON   (France)

^f SWEDISH MUSEUM OF NATURAL HISTORY   (Sweden)

^g KING ABDULAZIZ UNIVERSITY   (Saudi Arabia)

Author keywords

Computation; Graphical models; Inference; Modularization; Statistical phylogenetics; Tree plate

Indexed keywords

ALGORITHM; ARTICLE; CLASSIFICATION; COMPUTER SIMULATION; METHODOLOGY; PHYLOGENY; STATISTICAL MODEL;

ALGORITHMS; CLASSIFICATION; COMPUTER SIMULATION; MODELS, STATISTICAL; PHYLOGENY;

EID: 84906228797     PISSN: 10635157     EISSN: 1076836X     Source Type: Journal    
DOI: 10.1093/sysbio/syu039     Document Type: Article

References (61)

1. Ahmadi A., Serpedini E., Qaraqell K.A. 2012. Mathematical foundations for signal processing, communications, and networking, chap. 13. Factor Graphs and Message Passing Algorithms. USA: CRC Press.
2. Blanquart S., Lartillot N. 2006. A Bayesian compound stochastic process for modeling nonstationary and nonhomogeneous sequence evolution. Mol. Biol. Evol. 23:2058-2071.
3. Bollback J. 2002. Bayesian model adequacy and choice in phylogenetics. Mol. Biol. Evol. 19:1171-1180.
4. Boussau B., Guéguen L., Gouy M. 2009. Amixturemodel and a hidden markov model to simultaneously detect recombination breakpoints and reconstruct phylogenies. Evol. Bioinformatics 5:67.
5. Brown J., ElDabaje R. 2009. Puma: Bayesian analysis of partitioned (and unpartitioned) model adequacy. Bioinformatics 25:537-538.
6. dos Reis M., Inoue J., Hasegawa M., Asher R. J., Donoghue P. C., Yang Z. 2012. Phylogenomic datasets provide both precision and accuracy in estimating the timescale of placental mammal phylogeny. Proc. R. Soc. B Biol. Sci. 279:3491-3500.
7. Felsenstein J. 1981. Evolutionary trees from DNA sequences: a maximum likelihood approach. J. Mol. Evol. 17:368-376.
8. Felsenstein J. 1985. Phylogenies and the comparative method. Am. Naturalist 125:1-15.
9. Forney Jr, G. D. 1973. The viterbi algorithm. Proc. IEEE 61:268-278.
10. Friedman N. 2004. Inferring cellular networks using probabilistic graphical models. Science 303:799-805.
11. Friedman N., Ninio M., Pe'er I., Pupko T. 2002. A structural em algorithm for phylogenetic inference. J. Comput. Biol. 9:331-353.
12. Gallager R. 1962. Low-density parity-check codes. Informat. Theory, IRE Trans. 8:21-28.
13. Galtier N., Gouy M. 1998. Inferring pattern and process: maximumlikelihood implementation of a nonhomogeneous model of DNA sequence evolution for phylogenetic analysis. Mol. Biol. Evol. 15:871-879.
14. Gelfand A. E., Smith A. F. M. 1990. Sampling-based approaches to calculating marginal densities. J. Am. Stat. Assoc. 85:398-409.
15. Geman S., Geman D. 1984. Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. Pattern Anal. Mach. Intell. IEEE Trans. 721-741.
16. Gilks W., Thomas A., Spiegelhalter D. 1994. A language and program for complex Bayesian modelling. Statistician 43:169-177.
17. Groussin M., Boussau B., Gouy M. 2013. A branch-heterogeneous model of protein evolution for efficient inference of ancestral sequences. Syst. Biol. 62:523-538.
18. Hasegawa M., Kishino H., Yano T. 1985. Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J. Mol. Evol. 22:160-174.
19. Hastings W. K. 1970. Monte Carlo sampling methods using markov chains and their applications. Biometrika 57:97-109.
20. Heath T. A. 2012. A hierarchical bayesian model for calibrating estimates of species divergence times. Syst. Biol. 61:793-809.
21. Höhna S. 2013. Fast simulation of reconstructed phylogenies under global time-dependent birth-death processes. Bioinformatics 29:1367-1374.
22. Höhna S. 2014. Likelihood inference of non-constant diversification rates with incomplete taxon sampling. PLoS one 9:e84184.
23. Huelsenbeck J. P. 1995. Performance of phylogenetic methods in simulation. Syst. Biol. 44:17-48.
24. Huelsenbeck J. P., Dyer K. A. 2004. Bayesian estimation of positively selected sites. J. Mol. Evol. 58:661-672.
25. Jordan M. 2004. Graphical models. Stat. Sci. 19:140-155.
26. Jukes T., Cantor C. 1969. Evolution of protein molecules. Mammalian Protein Metab. 3:21-132.
27. Kingman J. F. C. 1982. On the genealogy of large populations. J. Appl. Probab. 19:27-43.
28. Koller D., Friedman N. 2009. Probabilistic graphical models: principles and techniques. Cambridge: The MIT Press.
29. Kschischang F. R., Frey B. J., Loeliger H.-A. 2001. Factor graphs and the sum-product algorithm. Informat. Theory, IEEE Trans. 47:498-519.
30. Landis M. J., Matzke N. J., Moore B. R., Huelsenbeck J. P. 2013. Bayesian analysis of biogeography when the number of areas is large. Syst. Biol. 62:789-804.
31. Lariviére S., Ferguson S. H. 2002. On the evolution of the mammalian baculum: vaginal friction, prolonged intromission or induced ovulation? Mammal Rev. 32:283-294.
32. Lartillot N., Philippe H. 2004. Abayesian mixture model for across-site heterogeneities in the amino-acid replacement process. Mol. Biol. Evol. 21:1095-1109.
33. Lartillot N., Poujol R. 2011. A phylogenetic model for investigating correlated evolution of substitution rates and continuous phenotypic characters. Mol. Biol. Evol. 28:729-744.
34. Lauritzen S. 1996. Graphical models. USA: Oxford University Press.
35. Loeliger H.-A. 2004. An introduction to factor graphs. Signal Proces. Magazine, IEEE 21:28-41.
36. Long C., Frank T. 1968. Morphometric variation and function in the Baculum, with comments on correlation of parts. J. Mammalogy 49:32-43.
37. Lunn D., Jackson C., Spiegelhalter D. J., Best N., Thomas A. 2012. The BUGS book: a practical introduction to Bayesian analysis, vol. 98. CRC Press.
38. Lunn D., Spiegelhalter D., Thomas A., Best N. 2009. The BUGS project: evolution, critique and future directions. Stat. Med. 28:3049-3067.
39. Lunn D. J., Thomas A., Best N., Spiegelhalter D. 2000. WinBUGSa Bayesian modelling framework: concepts, structure, and extensibility. Stat. Comput. 10:325-337.
40. Mayrose I., Friedman N., Pupko T. 2005. A gamma mixture model better accounts for among site rate heterogeneity. Bioinformatics 21:ii151-ii158.
41. Metropolis N., Rosenbluth A., Rosenbluth M., Teller A., Teller E. 1953. Equation of state calculations by fast computing machines. J. Chem. Phys. 21:1087-1092.
42. Nee S., May R. M., Harvey P. H. 1994. The reconstructed evolutionary process. Philos. Trans. Biol. Sci. 344:305-311.
43. Pagel M., Meade A. 2004. A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data. Syst. Biol. 53:571-581.
44. Pagel M., Meade A. 2005. Mixturemodels in phylogenetic inference. In O. Gascuel, editor.Mathematics of evolution and phylogeny. Oxford (UK): Oxford University Press. pp. 121-142.
45. Patterson B., Thaeler C. J. 1982. The mammalian Baculum: hypotheses on the nature of bacular variability. J. Mammalogy 63:1-15.
46. Pearl J. 1982. Reverend Bayes on inference engines: a distributed hierarchical approach. In: Proceedings of the Second National Conference on Artificial Intelligence. Menlo Park, CA: AAAI Press. pp. 133-136
47. Rabosky D., Lovette I. 2008. Explosive evolutionary radiations: decreasing speciation or increasing extinction through time? Evolution 62:1866-1875.
48. Rodrigue N., Philippe H., Lartillot N. 2008. Uniformization for sampling realizations of markov processes: applications to Bayesian implementations of codon substitution models. Bioinformatics 24:56-62.
49. Ronquist F., Teslenko M., van der Mark P., Ayres D. L., Darling A., Höhna S., Larget B., Liu L., Suchard M. A., Huelsenbeck J. P. 2012. Mrbayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space. Syst. Biol. 61:539-542.
50. Spiegelhalter D. J., Lauritzen S. L. 1990. Sequential updating of conditional probabilities on directed graphical structures.Networks 20:579-605.
51. Tamura K. 1992. Estimation of the number of nucleotide substitutions when there are strong transition-transversion and g+ c-content biases. Mol. Biol. Evol. 9:678-687.
52. Tanner R. 1981. A recursive approach to low complexity codes. Informat. Theory, IEEE Trans. 27:533-547.
53. Tavaré S. 1986. Some probabilistic and statistical problems in the analysis of DNA sequences. Lectures Math. Life. Sci. 17: 57-86.
54. Thompson E. 1975. Human evolutionary trees. Cambridge: Cambridge University Press.
55. Thorne J., Kishino H., Painter I. S. 1998. Estimating the rate of evolution of the rate of molecular evolution. Mol. Biol. Evol. 15:1647-1657.
56. Yang Z. 1994. Maximum likelihood phylogenetic estimation from dna sequences with variable rates over sites: approximate methods. J. Mol. Evol. 39:306-314.
57. Yang Z. 1996. Among-site rate variation and its impact on phylogenetic analyses. Trends Ecol. Evol. 11:367-372.
58. Yang Z., Kumar S., Nei M. 1995. A new method of inference of ancestral nucleotide and amino acid sequences. Genetics 141:1641-1650.
59. Yang Z., Nielsen R. 2002. Codon-substitution models for detecting molecular adaptation at individual sites along specific lineages. Mol. Biol. Evol. 19:908-917.
60. Yang Z., Roberts D. 1995. On the use of nucleic acid sequences to infer early branchings in the tree of life. Mol. Biol. Evol. 12:451-458.
61. Yule G. 1925. A mathematical theory of evolution, based on the conclusions of Dr. JcWillis, FRS. Philos. Trans. R. Soc. London. Ser. B. 213:21-87.