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Implementing and testing Bayesian and maximum-likelihood supertree methods in phylogenetics.

Akanni WA, Wilkinson M, Creevey CJ, Foster PG, Pisani D - R Soc Open Sci (2015)

Bottom Line: We investigated new implementations of ML and Bayesian SMs and compared these with some currently available alternative approaches.Computation of input tree likelihoods allows the adoption of standard tests of tree topologies (e.g. the approximately unbiased test).The Bayesian approach is particularly useful in providing support values for supertree clades in the form of posterior probabilities.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology , The National University of Ireland , Maynooth, Co. Kildare, Republic of Ireland ; Department of Life Science , The Natural History Museum , London SW7 5BD, UK.

ABSTRACT
Since their advent, supertrees have been increasingly used in large-scale evolutionary studies requiring a phylogenetic framework and substantial efforts have been devoted to developing a wide variety of supertree methods (SMs). Recent advances in supertree theory have allowed the implementation of maximum likelihood (ML) and Bayesian SMs, based on using an exponential distribution to model incongruence between input trees and the supertree. Such approaches are expected to have advantages over commonly used non-parametric SMs, e.g. matrix representation with parsimony (MRP). We investigated new implementations of ML and Bayesian SMs and compared these with some currently available alternative approaches. Comparisons include hypothetical examples previously used to investigate biases of SMs with respect to input tree shape and size, and empirical studies based either on trees harvested from the literature or on trees inferred from phylogenomic scale data. Our results provide no evidence of size or shape biases and demonstrate that the Bayesian method is a viable alternative to MRP and other non-parametric methods. Computation of input tree likelihoods allows the adoption of standard tests of tree topologies (e.g. the approximately unbiased test). The Bayesian approach is particularly useful in providing support values for supertree clades in the form of posterior probabilities.

No MeSH data available.


A graph showing the comparison of the distribution of the likelihood scores for 1000 random supertrees on the same taxon set of the metazoan dataset and the likelihood scores for the metazoan phylogeny inferred by the MSS, RF, MRP and Bayesian SMs.
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RSOS140436F2: A graph showing the comparison of the distribution of the likelihood scores for 1000 random supertrees on the same taxon set of the metazoan dataset and the likelihood scores for the metazoan phylogeny inferred by the MSS, RF, MRP and Bayesian SMs.

Mentions: The Bayesian (MCMC) analysis of the metazoan dataset converged after 2500 iterations. Figure 1a is the MR consensus of 150 supertrees sampled after convergence, summarizing relationships and support (estimated posterior probabilities of full splits) and is topologically identical to the MRP tree of Holton & Pisani [22]. Posterior probabilities for the nodes in this tree are also very similar to the bootstrap support values obtained in the original MRP analysis [22]. Quite importantly, the relationships in this tree are in full agreement with current understanding of animal relationships. This is not the case for supertrees inferred using MSS (figure 1b) and RF (figure 1c). The MSS incorrectly resolves the relationships among the mammal species, while the RF supertree has many obviously incorrect clades, such as chordates (Ciona) forming a sister group relationship with arthropods (Daphnia, Drosophila, Apis, etc.). Likelihoods of each of these supertrees are compared to each other and to the distribution of likelihoods for a set of 1000 random supertrees in figure 2. Although all the inferred supertrees fit the data better than the random trees, AU tests indicate that the RF supertree (the worst of those considered judged in terms of their likelihoods) and MSS have significantly worse fits to the data (p=0.001 and p<0.00001, respectively) under the SR–RF 2008 model than do the MRP and Bayesian supertrees.Figure 1.


Implementing and testing Bayesian and maximum-likelihood supertree methods in phylogenetics.

Akanni WA, Wilkinson M, Creevey CJ, Foster PG, Pisani D - R Soc Open Sci (2015)

A graph showing the comparison of the distribution of the likelihood scores for 1000 random supertrees on the same taxon set of the metazoan dataset and the likelihood scores for the metazoan phylogeny inferred by the MSS, RF, MRP and Bayesian SMs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4555849&req=5

RSOS140436F2: A graph showing the comparison of the distribution of the likelihood scores for 1000 random supertrees on the same taxon set of the metazoan dataset and the likelihood scores for the metazoan phylogeny inferred by the MSS, RF, MRP and Bayesian SMs.
Mentions: The Bayesian (MCMC) analysis of the metazoan dataset converged after 2500 iterations. Figure 1a is the MR consensus of 150 supertrees sampled after convergence, summarizing relationships and support (estimated posterior probabilities of full splits) and is topologically identical to the MRP tree of Holton & Pisani [22]. Posterior probabilities for the nodes in this tree are also very similar to the bootstrap support values obtained in the original MRP analysis [22]. Quite importantly, the relationships in this tree are in full agreement with current understanding of animal relationships. This is not the case for supertrees inferred using MSS (figure 1b) and RF (figure 1c). The MSS incorrectly resolves the relationships among the mammal species, while the RF supertree has many obviously incorrect clades, such as chordates (Ciona) forming a sister group relationship with arthropods (Daphnia, Drosophila, Apis, etc.). Likelihoods of each of these supertrees are compared to each other and to the distribution of likelihoods for a set of 1000 random supertrees in figure 2. Although all the inferred supertrees fit the data better than the random trees, AU tests indicate that the RF supertree (the worst of those considered judged in terms of their likelihoods) and MSS have significantly worse fits to the data (p=0.001 and p<0.00001, respectively) under the SR–RF 2008 model than do the MRP and Bayesian supertrees.Figure 1.

Bottom Line: We investigated new implementations of ML and Bayesian SMs and compared these with some currently available alternative approaches.Computation of input tree likelihoods allows the adoption of standard tests of tree topologies (e.g. the approximately unbiased test).The Bayesian approach is particularly useful in providing support values for supertree clades in the form of posterior probabilities.

View Article: PubMed Central - PubMed

Affiliation: Department of Biology , The National University of Ireland , Maynooth, Co. Kildare, Republic of Ireland ; Department of Life Science , The Natural History Museum , London SW7 5BD, UK.

ABSTRACT
Since their advent, supertrees have been increasingly used in large-scale evolutionary studies requiring a phylogenetic framework and substantial efforts have been devoted to developing a wide variety of supertree methods (SMs). Recent advances in supertree theory have allowed the implementation of maximum likelihood (ML) and Bayesian SMs, based on using an exponential distribution to model incongruence between input trees and the supertree. Such approaches are expected to have advantages over commonly used non-parametric SMs, e.g. matrix representation with parsimony (MRP). We investigated new implementations of ML and Bayesian SMs and compared these with some currently available alternative approaches. Comparisons include hypothetical examples previously used to investigate biases of SMs with respect to input tree shape and size, and empirical studies based either on trees harvested from the literature or on trees inferred from phylogenomic scale data. Our results provide no evidence of size or shape biases and demonstrate that the Bayesian method is a viable alternative to MRP and other non-parametric methods. Computation of input tree likelihoods allows the adoption of standard tests of tree topologies (e.g. the approximately unbiased test). The Bayesian approach is particularly useful in providing support values for supertree clades in the form of posterior probabilities.

No MeSH data available.