MASTtreedist: visualization of tree space based on maximum agreement subtree

Area of Science:

• Phylogenetics
• Computational Biology
• Bioinformatics

Background:

• Phylogenetic tree construction often yields multiple candidate trees, necessitating efficient comparison methods.
• Existing tools like Mesquite's Tree Set Viz visualize tree distances using measures like Robinson-Foulds (RF).
• Sophisticated measures like Maximum Agreement Subtree (MAST) offer precise similarity insights but are computationally challenging.

Purpose of the Study:

• To introduce MASTtreedist, a practical, MAST-based tree-distance metric for Mesquite's Tree Set Viz.
• To enhance the comparison and visualization of topological differences among phylogenetic trees.
• To provide a more precise metric for discriminating subtle subtree structure divergences.

Main Methods:

• Implementation of a MAST-based comparison metric (MASTtreedist) within Mesquite's Tree Set Viz module.
• Application of efficient optimizations for the maximum weight clique problem to compute MAST distances.
• Statistical evaluation of MASTtreedist against the Robinson-Foulds (RF) metric using experimental datasets.

Main Results:

• The proposed MASTtreedist method efficiently computes MAST distances among trees.
• Tree topological differences are effectively visualized as points in two-dimensional (2D) space.
• MASTtreedist demonstrates improved discrimination of small subtree divergences compared to RF.

Conclusions:

• MASTtreedist offers an efficient and practical approach for comparing phylogenetic trees based on MAST.
• This new metric enhances the visualization of taxa differences by capturing subtle structural variations.
• The module provides a valuable tool for phylogenetic tree reconstruction and analysis.