Reversible polymorphism-aware phylogenetic models and their application to tree inference.

Area of Science:

• Evolutionary Biology
• Genomics
• Computational Biology

Background:

• Accurate species tree estimation is crucial for understanding evolutionary history.
• Standard phylogenetic models struggle with polymorphic data and incomplete lineage sorting.
• Large-scale genomic datasets require efficient and accurate phylogenetic methods.

Purpose of the Study:

• To introduce a novel reversible Polymorphism-Aware Phylogenetic Model (revPoMo).
• To enable accurate species tree reconstruction from genome-wide data, accounting for polymorphism.
• To improve the accuracy and scalability of phylogenetic analyses.

Main Methods:

• Implemented revPoMo within the IQ-TREE software using maximum likelihood.
• Expanded DNA substitution models to incorporate polymorphic states.
• Evaluated performance through simulation studies and analysis of great ape genomic data.

Main Results:

• revPoMo demonstrated comparable runtimes to standard models.
• Significantly improved accuracy in estimating species trees, divergence times, and mutation rates.
• Increased sample size per species enhanced estimation accuracy without increasing runtime.

Conclusions:

• revPoMo is an accurate and efficient tool for species tree estimation from genomic data.
• The model effectively handles incomplete lineage sorting by incorporating polymorphic states.
• revPoMo offers a valuable advancement for evolutionary studies, including speciation dating and large-scale phylogenetic reconstruction.