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

Area of Science:

• Phylogenetics
• Computational Biology
• Evolutionary Biology

Background:

• Supertree methods (SMs) are crucial for large-scale evolutionary studies requiring phylogenetic frameworks.
• Advancements include maximum likelihood (ML) and Bayesian SMs, modeling incongruence with exponential distributions.
• These newer methods are expected to outperform traditional non-parametric SMs like matrix representation with parsimony (MRP).

Purpose of the Study:

• To evaluate new implementations of ML and Bayesian supertree methods.
• To compare their performance against existing alternative approaches.
• To investigate potential biases related to input tree shape and size.

Main Methods:

• Utilized hypothetical datasets previously used for bias investigations.
• Employed empirical studies using literature-derived and phylogenomic datasets.
• Compared Bayesian and ML SMs against non-parametric methods, including MRP.

Main Results:

• No evidence of size or shape biases was found in the investigated SMs.
• The Bayesian method proved to be a viable alternative to MRP and other non-parametric methods.
• Bayesian SMs provide posterior probabilities for supertree clade support.

Conclusions:

• Bayesian supertree methods are a reliable and effective alternative for constructing phylogenetic frameworks.
• These methods overcome limitations of traditional approaches and offer robust clade support.
• The computational ability to calculate input tree likelihoods enables standard topology testing.