Asymmetric Cluster-Based Measures for Comparative Phylogenetics.

Area of Science:

• Evolutionary biology
• Bioinformatics
• Computational phylogenetics

Background:

• Phylogenetic inference methods generate evolutionary hypotheses.
• Tree comparison costs, like Robinson-Foulds (RF) distance, evaluate these hypotheses but have limitations.
• Existing Cluster Affinity (CA) distances are symmetric and cannot compare heterogeneous trees.

Purpose of the Study:

• Introduce new, asymmetric tree comparison costs for heterogeneous phylogenetic trees.
• Develop a biologically interpretable cost that normalizes by cluster size.
• Provide objective, fine-scale, and interpretable values for assessing phylogenetic tree differences.

Main Methods:

• Developed an asymmetric Cluster Affinity (CA) cost, a relaxation of the original Affinity distance.
• Introduced a Cluster Support cost, normalizing by cluster size across gene trees.
• Described efficient algorithms and derived exact diameters for cost standardization.

Main Results:

• The new asymmetric CA cost and Cluster Support cost offer improved comparisons for heterogeneous trees.
• These costs provide objective, fine-scale, and biologically interpretable values.
• Standardization ensures practical applicability in phylogenetic analyses.

Conclusions:

• The asymmetric CA cost and Cluster Support cost overcome limitations of symmetric and RF distances.
• These novel costs enhance the evaluation of evolutionary hypotheses, especially for gene trees, species trees, and phylogenetic networks.
• The developed methods provide robust tools for comparing diverse phylogenetic datasets.