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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.


Phylogenomic supertrees of the Carnivora. These supertrees have been reconstructed from 274 gene trees overlapping on 245 taxa and had 26 unstable taxa (according to a leaf stability test) pruned. (a) Phylogeny inferred by MRP with equal weighting of clades. (b) Phylogeny inferred by differentially weighted MRP with a taxonomy tree [18]. (c) Phylogeny inferred by the Bayesian MCMC SM with posterior probabilities shown.
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RSOS140436F3: Phylogenomic supertrees of the Carnivora. These supertrees have been reconstructed from 274 gene trees overlapping on 245 taxa and had 26 unstable taxa (according to a leaf stability test) pruned. (a) Phylogeny inferred by MRP with equal weighting of clades. (b) Phylogeny inferred by differentially weighted MRP with a taxonomy tree [18]. (c) Phylogeny inferred by the Bayesian MCMC SM with posterior probabilities shown.

Mentions: With the carnivore example, the Bayesian supertree (after pruning unstable taxa) has generally high levels of support (figure 3a) and is in good agreement with the original MRP supertree [18]. At the ordinal level, the only differences are two clades that have low support in the Bayesian tree, suggesting that there is not much signal in the data and that the alternative placements of these taxa cannot be considered reliable. By contrast, the newly generated MRP tree (figure 3b), constructed using the same dataset as that used for the Bayesian supertree analysis (i.e. without a taxonomy tree and with equal weighting of input trees), is very different and biologically highly implausible in several respects. Supertrees built with the MSS and RF SMs (not shown) differed to some extent from the Bayesian supertree. The AU test shows that the Bayesian supertree fits the data significantly better than the original MRP supertree (p=0.003), the MSS (p=0.002) and our newly generated MRP tree (p=10−49) under the SR–RF 2009 model. Only the RF supertree did not have a significantly worse fit to the data (p=0.343). Figure 4 shows that all the considered methods returned supertrees that, judged by their likelihoods, are significantly better than expected for randomly selected trees.Figure 3.


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)

Phylogenomic supertrees of the Carnivora. These supertrees have been reconstructed from 274 gene trees overlapping on 245 taxa and had 26 unstable taxa (according to a leaf stability test) pruned. (a) Phylogeny inferred by MRP with equal weighting of clades. (b) Phylogeny inferred by differentially weighted MRP with a taxonomy tree [18]. (c) Phylogeny inferred by the Bayesian MCMC SM with posterior probabilities shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

RSOS140436F3: Phylogenomic supertrees of the Carnivora. These supertrees have been reconstructed from 274 gene trees overlapping on 245 taxa and had 26 unstable taxa (according to a leaf stability test) pruned. (a) Phylogeny inferred by MRP with equal weighting of clades. (b) Phylogeny inferred by differentially weighted MRP with a taxonomy tree [18]. (c) Phylogeny inferred by the Bayesian MCMC SM with posterior probabilities shown.
Mentions: With the carnivore example, the Bayesian supertree (after pruning unstable taxa) has generally high levels of support (figure 3a) and is in good agreement with the original MRP supertree [18]. At the ordinal level, the only differences are two clades that have low support in the Bayesian tree, suggesting that there is not much signal in the data and that the alternative placements of these taxa cannot be considered reliable. By contrast, the newly generated MRP tree (figure 3b), constructed using the same dataset as that used for the Bayesian supertree analysis (i.e. without a taxonomy tree and with equal weighting of input trees), is very different and biologically highly implausible in several respects. Supertrees built with the MSS and RF SMs (not shown) differed to some extent from the Bayesian supertree. The AU test shows that the Bayesian supertree fits the data significantly better than the original MRP supertree (p=0.003), the MSS (p=0.002) and our newly generated MRP tree (p=10−49) under the SR–RF 2009 model. Only the RF supertree did not have a significantly worse fit to the data (p=0.343). Figure 4 shows that all the considered methods returned supertrees that, judged by their likelihoods, are significantly better than expected for randomly selected trees.Figure 3.

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.