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Phylogeny

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Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.

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Updated: Jun 25, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Sequential Bayesian Phylogenetic Inference.

Sebastian Höhna^1,2, Allison Y Hsiang^1,2

¹GeoBio-Center LMU, Ludwig-Maximilians-Universität München, Richard-Wagner Str. 10, 80333 Munich, Germany.

Systematic Biology

|May 21, 2024

Summary

This summary is machine-generated.

We developed a sequential Bayesian phylogenetic inference method using importance sampling to propagate uncertainty between analysis steps. This approach improves accuracy and flexibility compared to traditional methods, especially when testing multiple models for divergence time estimation.

Keywords:

Bayesian inference RevBayes divergence time estimation joint posterior distribution parameter uncertainty phylogenetics

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Last Updated: Jun 25, 2025

A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

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Published on: August 16, 2017

Area of Science:

Computational Biology
Evolutionary Biology
Bioinformatics

Background:

Bayesian phylogenetic inference ideally uses joint hierarchical models but is computationally expensive.
Current pipelines use sequential analyses with point estimates, failing to propagate uncertainty.
This lack of uncertainty propagation can lead to inaccurate results and overconfidence.

Purpose of the Study:

To develop and test a sequential inference approach for Bayesian phylogenetics.
To account for uncertainty between analysis steps and enhance flexibility in software and model choice.
To provide a computationally efficient alternative to joint inference, particularly for testing multiple models.

Main Methods:

Formal development of a sequential inference approach using importance sampling.
Generating observations for subsequent analyses from posterior distributions of previous steps.
Testing the approach with a toy example and an empirical case study of insect divergence-time estimation.

Main Results:

The sequential approach accounts for uncertainty and offers greater software flexibility.
It is computationally more efficient than joint inference when testing multiple models.
The method accurately reflects joint inference with sufficient data or samples; point estimates can be biased.

Conclusions:

Sequential Bayesian phylogenetic inference provides a robust and flexible framework.
It enables efficient testing of various divergence time estimation strategies.
The approach accurately estimates posterior distributions, improving phylogenetic analyses.