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Home
Displacement-optimized Tanglegrams For Trees And Networks.

Home
Displacement-optimized Tanglegrams For Trees And Networks.

Related Experiment Video

Automatic Identification of Dendritic Branches and their Orientation

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

Displacement-Optimized Tanglegrams for Trees and Networks.

Daniel H Huson¹

¹Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, Tübingen 72076, Germany.

Molecular Biology and Evolution

|March 10, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Displacement-optimized tanglegrams (DO-tanglegrams) improve visualizations of phylogenetic trees and networks by minimizing taxon and reticulate displacement. This new method offers superior performance compared to existing tools for comparing evolutionary histories.

Keywords:

displacement optimization phylogenetic network phylogenetic tree reticulate displacement tanglegram taxon displacement visualization

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Area of Science:

Computational Biology
Bioinformatics
Mathematical Biology

Background:

Phylogenetic trees and networks are crucial for understanding biological evolution and relationships.
Tanglegrams are widely used to visualize and compare phylogenies, but existing methods primarily focus on minimizing edge crossings in trees.
There is a need for improved tanglegram visualization methods that can handle both trees and networks effectively.

Purpose of the Study:

To introduce a novel tanglegram layout algorithm, displacement-optimized tanglegrams (DO-tanglegrams), designed for both phylogenetic trees and rooted networks.
To explicitly minimize taxon displacement and reticulate displacement, enhancing the clarity of evolutionary comparisons.
To develop a computationally tractable heuristic algorithm for optimizing tanglegram layouts.

Main Methods:

Formalized one-sided and two-sided optimization problems for tanglegram layouts.
Developed a heuristic algorithm combining exhaustive local search and simulated annealing to minimize displacement.
Implemented the DO-tanglegram algorithm in SplitsTree, accommodating multifurcations, multicombinations, and missing taxa.

Main Results:

DO-tanglegrams significantly outperform the phytools::cophylo function on trees by minimizing taxon displacement.
DO-tanglegrams demonstrate superior performance compared to the NN-tanglegram algorithm on networks, reducing reticulate displacement.
The heuristic approach effectively handles complex phylogenetic structures and missing data.

Conclusions:

DO-tanglegrams provide a significant advancement in visualizing and comparing phylogenetic trees and networks.
The method offers improved accuracy and clarity for analyses of evolutionary histories, host-parasite associations, and gene transfer.
DO-tanglegrams represent a valuable new tool for researchers in computational biology and bioinformatics.