A vector representation for phylogenetic trees.

Area of Science:

• Evolutionary biology
• Bioinformatics
• Computational phylogenetics

Background:

• Phylogenetic trees and networks require efficient representations for storage and analysis.
• Scalability is crucial for inferring and analyzing evolutionary relationships across genes, genomes, and species.

Purpose of the Study:

• To propose a novel representation for rooted phylogenetic trees.
• To introduce a new tree rearrangement operator (HOP) and a computable distance metric (HOP distance).
• To evaluate the utility of HOP distance in evolutionary analyses and its generalization to networks.

Main Methods:

• Encoding rooted phylogenetic trees on *n* ordered taxa as a vector of length 2*n* where each taxon appears twice.
• Developing a novel tree rearrangement operator, HOP, defining a tree space with linear diameter and quadratic neighborhood size.
• Introducing the HOP distance metric and computing it in near-linear time.

Main Results:

• The HOP operator creates a tree space with linear diameter and quadratic neighborhood size.
• The HOP distance is a tractable metric, computable in near-linear time.
• HOP distance shows better correlation with Subtree-Prune-and-Regraft distance than Robinson-Foulds distance.
• The representation is generalizable to tree-child networks.

Conclusions:

• The proposed tree representation and HOP distance offer efficient and scalable methods for phylogenetic analysis.
• HOP distance provides a tractable and informative metric for comparing evolutionary trees.
• The generalization to tree-child networks highlights the broad applicability of this approach in evolutionary studies.