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Bayesian modelling of compositional heterogeneity in molecular phylogenetics.

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    This study introduces a new Bayesian phylogenetic model to address compositional heterogeneity in sequence evolution. The model accurately infers rooted trees and evolutionary relationships, improving phylogenetic analysis.

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    Area of Science:

    • Molecular Phylogenetics
    • Evolutionary Biology
    • Computational Biology

    Background:

    • Standard phylogenetic models assume constant sequence composition over evolutionary time, which is often violated in real datasets.
    • Compositional heterogeneity across taxa can lead to inaccurate evolutionary inferences.

    Purpose of the Study:

    • To develop a Bayesian phylogenetic model that accounts for compositional heterogeneity across tree branches.
    • To enable accurate inference of rooted phylogenetic trees and evolutionary relationships.

    Main Methods:

    • Proposed a Bayesian framework with branch-specific composition vectors and a global exchangeability matrix.
    • Developed two priors for composition vectors to encourage information sharing between branches.
    • Implemented a Markov chain Monte Carlo (MCMC) algorithm with data augmentation for efficient posterior inference.

    Main Results:

    • The model successfully handles compositional heterogeneity, outperforming standard models.
    • The proposed Bayesian framework allows for robust inference of rooted tree topologies.
    • The MCMC algorithm provides efficient and stable parameter estimation without dimension-changing moves.

    Conclusions:

    • The new model significantly advances molecular phylogenetics by incorporating compositional heterogeneity.
    • It offers a powerful tool for inferring evolutionary history, especially for rooted trees.
    • This approach enhances the biological interpretation of phylogenetic relationships and trait evolution.