Phylogenetic reconstruction through Bayesian inference is currently widely used. The main advantage of this method is the direct output of posterior probabilities for each clade on the final phylogeny. Thus, it does not require bootstrapping as a measure of uncertainty. Moreover, Bayesian inference is perfectly fit for dating phylogenies through molecular clocks. In this paper, the basics of Bayesian inference applied to phylogenetic reconstruction are described, starting with an explanation of Bayes’ theorem. Then, the use of the Metropolis-Hastings algorithm to sample topologies from the posterior distribution is characterized and illustrated through a simple example. At the end, there is a mention of the software used for Bayesian phylogeny reconstruction.
Resúmen
La inferencia bayesiana como modelo de reconstrucción filogenética es muy utilizada en la actualidad. La ventaja de este método es la generación directa de probabilidades posteriores para cada clado en la filogenia final, por lo cual no se requiere de bootstrapping como medida de incertidumbre. Además, la inferencia bayesiana se presta perfectamente para la datación de filogenias por medio de relojes moleculares. En este trabajo se describen los principios de este método, comenzando por el teorema de Bayes; posteriormente se caracteriza el uso del algoritmo de Metropolis-Hastings para el muestreo de las topologías más probables y se le ilustra con un ejemplo sencillo. Se finaliza mencionando los programas más usados actualmente.
Ene | Feb | Mar | Abr | May | Jun | Jul | Ago | Sept | Oct | Nov | Dic |
---|---|---|---|---|---|---|---|---|---|---|---|
- | - | - | - | - | - | - | 9 | 7 | 13 | 9 | 41 |
Ene | Feb | Mar | Abr | May | Jun | Jul | Ago | Sept | Oct | Nov | Dic |
---|---|---|---|---|---|---|---|---|---|---|---|
8 | 23 | 22 | 27 | 15 | 16 | 12 | 21 | 15 | 13 | 17 | 14 |
Ene | Feb | Mar | Abr | May | Jun | Jul | Ago | Sept | Oct | Nov | Dic |
---|---|---|---|---|---|---|---|---|---|---|---|
12 | 7 | 17 | 11 | 21 | 29 | 31 | 46 | 48 | 18 | 51 | 34 |
Ene | Feb | Mar | Abr | May | Jun | Jul | Ago | Sept | Oct | Nov | Dic |
---|---|---|---|---|---|---|---|---|---|---|---|
51 | 40 | 55 | 44 | 46 | - | - | - | - | - | - | - |
1. Critchlow, D. E., Pearl, D. K. & Qian, C. (1996). The triples distance for rooted bifurcating phylogenetic trees. Systematic Biology, 45, 323-334. Https://doi.org/10.1093/sysbio/45.3.323
2. Drummond, A. J. & Rambaut, A. (2007). BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology, 7, 214. Https://doi.org/10.1186/1471-2148-7-214
3. Felsenstein, J. (1973). Maximum likelihood and minimum-steps methods for estimating evolutionary trees from data on discrete characters. Systematic Biology, 22, 240-249. https://doi.org/10.1093/sysbio/22.3.240
4. Felsenstein, J. (2004). Inferring phylogenies, vol. 2. Sunderland, Massachusetts: Sinauer associates.
5. Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109. Https://doi.org/10.1093/biomet/57.1.97
6. Höhna, S., Landis, M. J., Heath, T. A., Boussau, B., Lartillot, N., Moore, B. R., Huelsenbeck, J. P. & Ronquist, F. (2016). RevBayes: Bayesian phylogenetic inference using graphical models and an interactive modelspecification language. Systematic Biology, 65, 726-736. Https://doi.org/10.1093/sysbio/syw021
7. Huelsenbeck, J. P. & Ronquist, F. (2001). MrBayes: Bayesian inference of phylogenetic trees. Bioinformatics, 17, 754-755. Https://doi.org/10.1093/bioinformatics/17.8.754
8. Huelsenbeck, J. P., Ronquist, F., Nielsen, R. & Bollback, J. P. (2001). Bayesian inference of phylogeny and its impact on evolutionary biology. Science, 294, 2310-2314. https://doi.org/10.1126/science.1065889
9. Larget, B. & Simon, D. L. (1999). Markov chain Monte Carlo algorithms for the Bayesian analysis of phylogenetic trees. Molecular Biology and Evolution, 16, 750-759. https://doi.org/10.1093/oxfordjournals.molbev.a026160
10. Li, S., Pearl, D. K. & Doss, H. (2000). Phylogenetic tree construction using Markov chain Monte Carlo. Journal of the American statistical Association, 95, 493-508. Https://doi.org/10.1080/01621459.2000.10474227
11. Mau, B. & Newton, M. A. (1997). Phylogenetic inference for binary data on dendograms using Markov chain Monte Carlo. Journal of Computational and Graphical Statistics, 6, 122-131. https://doi.org/10.1080/10618600.1997.10474731
12. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H. & Teller, E. (1953). Equation of state calculations by fast computing machines. The Journal of Chemical Physics, 21, 1087-1092. https://doi.org/10.1063/1.1699114
13. Peña, C. (2011). Métodos de inferencia filogenética. Revista Peruana de Biología, 18, 265-267.
14. Rannala, B. & Yang, Z. (1996). Probability distribution of molecular evolutionary trees: A new method of phylogenetic inference. Journal of Molecular Evolution, 43, 304-311. https://doi.org/10.1007/BF02338839
15. 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. Systematic Biology, 61, 539-542. Https://doi.org/10.1093/sysbio/sys029
16. Yang, Z. & Rannala, B. (1997). Bayesian phylogenetic inference using DNA sequences: A Markov chain Monte Carlo method. Molecular Biology and Evolution, 14, 717-724. Https://doi.org/10.1093/oxfordjournals.molbev.a025811