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Phylogenetic Trees

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.

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Updated: Aug 1, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Fidelity of hyperbolic space for Bayesian phylogenetic inference.

Matthew Macaulay¹, Aaron Darling², Mathieu Fourment¹

¹University of Technology Sydney, Australian Institute for Microbiology & Infection, Sydney, Australia.

Plos Computational Biology

|April 26, 2023

Summary

This study introduces hyperbolic space for Bayesian phylogenetic inference, enabling efficient computation of evolutionary relationships. This novel approach improves the analysis of complex genomic data by reducing dimensionality.

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Last Updated: Aug 1, 2025

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Area of Science:

Computational Biology
Phylogenetics
Machine Learning

Background:

Bayesian inference is crucial for phylogenetic analysis, but computationally intensive due to high-dimensional tree spaces.
Hyperbolic space offers a lower-dimensional representation suitable for tree-like data, potentially simplifying computations.

Purpose of the Study:

To develop and evaluate a novel method for Bayesian phylogenetic inference using hyperbolic space.
To assess the performance and fidelity of hyperbolic Markov Chain Monte Carlo for phylogenetic tree reconstruction.

Main Methods:

Genomic sequences were embedded as points in hyperbolic space.
Hyperbolic Markov Chain Monte Carlo was employed for Bayesian inference within this space.
Posterior probabilities were computed by decoding neighbour-joining trees from sequence embeddings.

Main Results:

The method demonstrated high fidelity in reconstructing phylogenetic trees across eight diverse datasets.
Systematic investigation showed that embedding dimension and hyperbolic curvature impact Markov Chain performance.
The sampled posterior distributions accurately recovered tree splits and branch lengths.

Conclusions:

Hyperbolic space provides a suitable and effective low-dimensional representation for Bayesian phylogenetic inference.
The proposed hyperbolic Markov Chain Monte Carlo method offers a computationally efficient alternative for analyzing complex genomic data.
This approach enhances the accuracy and feasibility of phylogenetic analyses, particularly for large datasets.