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Updated: Jun 25, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Data integration in Bayesian phylogenetics.

Gabriel W Hassler¹, Andrew Magee², Zhenyu Zhang²

¹Department of Computational Medicine, University of California, Los Angeles, USA, 90095.

Annual Review of Statistics and Its Application

|May 22, 2024

Summary

This summary is machine-generated.

Bayesian phylogenetics integrates diverse data for evolutionary studies. Advanced statistical models and computational methods help reconstruct organismal history and understand spread dynamics.

Keywords:

Bayesian networks Gaussian processes continuous-time Markov processes phylogenetic comparative methods phylogeography

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Last Updated: Jun 25, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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Published on: August 14, 2018

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

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

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

Published on: August 16, 2017

Area of Science:

Evolutionary biology
Computational statistics
Epidemiology

Background:

Modern biological research generates large, complex datasets from multiple sources.
Phylogenetics is crucial for reconstructing evolutionary history and understanding spread dynamics.
Integrating diverse data (genetic, temporal, spatial, environmental) is standard practice.

Purpose of the Study:

To address the challenge of analyzing large, complex datasets in evolutionary studies.
To highlight advances in statistical modeling and computational methods in Bayesian phylogenetics.
To discuss current research frontiers and open problems in the field.

Main Methods:

Utilizing Bayesian phylogenetics to reconstruct evolutionary histories.
Developing complex, hierarchical statistical models accommodating discrete and continuous data.
Advancing computational methods to handle large parameter spaces in phylogenetic analyses.

Main Results:

Bayesian phylogenetics provides a coherent framework for integrating diverse data types.
Complex models can incorporate genetic sequences, time, location, phenotypes, and covariates.
Computational advances have made previously intractable models computationally tractable.

Conclusions:

Bayesian phylogenetics is essential for understanding organismal evolution and pathogen spread.
Continued development in statistical modeling and computation is vital for future research.
The field is actively researching new methods to address complex evolutionary questions.