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Updated: Mar 8, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Visualizing phylogenetic tree landscapes.

James C Wilgenbusch^1,2, Wen Huang^3,4, Kyle A Gallivan³

¹Department of Scientific Computing, Florida State University, Tallahassee, FL, 32306, USA. jwilgenb@umn.edu.

BMC Bioinformatics

|February 4, 2017

Summary

Dimensionality reduction methods effectively visualize complex phylogenetic relationships from genomic data. Three-dimensional projections using Curvilinear Components Analysis and stochastic gradient descent best represent evolutionary patterns among competing trees.

Keywords:

Bootstrap Combining data MDS Mitochondrial DNA NLDR Tree landscape Visualization

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

Area of Science:

Evolutionary biology
Bioinformatics
Computational biology

Background:

Genomic-scale sequence alignments are crucial for inferring evolutionary phylogenies.
Analyzing multi-source data generates numerous competing phylogenies, challenging traditional summary methods.
Effective dimensionality reduction is needed to visualize and compare these phylogenies, aiding in diagnosing evolutionary and reconstruction method limitations.

Purpose of the Study:

Introduce and evaluate dimensionality reduction methods for visualizing relationships among competing phylogenies.
Assess the performance of these methods on mitochondrial genome alignments.
Determine if 2D and 3D projections can reveal meaningful phylogenetic relationships.

Main Methods:

Applied several dimensionality reduction techniques to visualize phylogenetic landscapes from three mitochondrial genome alignments.
Utilized goodness-of-fit measures to test method performance.
Estimated intrinsic dimensionality to assess projection suitability.

Main Results:

Curvilinear Components Analysis (CCA) with stochastic gradient descent (SGD) optimization provided the best representation of tree-to-tree distances.
CCA+SGD outperformed existing tree landscape visualization methods and converged efficiently.
Three-dimensional projections significantly improved the fit and interpretation of relationships among phylogenetic trees.

Conclusions:

The choice of dimensionality reduction method significantly impacts the visualization of competing phylogenetic trees.
Selecting appropriate methods is crucial for visualizing large, multi-locus phylogenetic landscapes.
Three-dimensional projections of mitochondrial tree landscapes offer superior representation of inter-tree relationships.