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Modeling Site-and-Branch-Heterogeneity with GFmix
Charley G P McCarthy1,2, Edward Susko1,3, Ryo Harada1,2
1Institute for Comparative Genomics, Dalhousie University, Halifax, NS B3H 4R2, Canada.
Improved phylogenetic models (GFmix) accurately infer evolutionary relationships by accounting for amino acid compositional heterogeneity. Enhanced GFmix models offer greater accuracy and flexibility in phylogenetic inference, reducing artefacts in evolutionary studies.
Area of Science:
- Evolutionary biology
- Bioinformatics
- Computational phylogenetics
Background:
- Phylogenetic trees are inferred from protein sequences, but compositional heterogeneity can cause artefacts.
- Existing models struggle with computational cost and limited scope for compositional variation.
Purpose of the Study:
- To investigate and improve the GFmix model for phylogenetic inference.
- To enhance accuracy and computational efficiency in modeling compositional heterogeneity.
Main Methods:
- Developed improved GFmix models with fewer constraints and user-defined heterogeneity.
- Implemented full maximum-likelihood optimization for parameters.
- Created new methods for detecting compositional heterogeneity.
Main Results:
- Improved GFmix models accurately estimate branch-specific compositions and lengths in heterogeneous trees.
- The most complex GFmix model consistently supported the correct tree with improved likelihoods on real data.
- New methods effectively detect compositional heterogeneity.
Conclusions:
- Enhanced GFmix models provide more accurate and robust phylogenetic inference.
- The improved models overcome limitations of previous versions, particularly for deep divergences.
- These advancements reduce phylogenetic artefacts caused by compositional variation.
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