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Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

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Updated: Feb 21, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

A biologist's guide to Bayesian phylogenetic analysis.

Fabrícia F Nascimento^1,2, Mario Dos Reis³, Ziheng Yang⁴

¹Department of Zoology, University of Oxford, Oxford, OX1 3PS, UK. f.nascimento@imperial.ac.uk.

Nature Ecology & Evolution

|October 7, 2017

Summary

This summary is machine-generated.

Bayesian phylogenetics is widely used but complex. This guide explains Bayesian computation, model selection, and software for accurate evolutionary analyses.

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Last Updated: Feb 21, 2026

A Practical Guide to Phylogenetics for Nonexperts

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Published on: February 5, 2014

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Published on: August 14, 2018

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Published on: August 16, 2017

Area of Science:

Molecular Biology
Evolutionary Biology
Computational Biology

Background:

Bayesian methods are increasingly popular in molecular phylogenetics.
User-friendly software has driven the adoption of sophisticated evolutionary models.
However, the complexity of these models necessitates careful analysis, as default settings may be inadequate.

Purpose of the Study:

To provide a comprehensive overview of Bayesian phylogenetic inference.
To guide researchers in the appropriate application and interpretation of Bayesian phylogenetic analyses.
To highlight potential pitfalls and best practices in Bayesian phylogenetic studies.

Main Methods:

Discussion of Bayesian computation using Markov chain Monte Carlo (MCMC).
Explanation of diagnostic methods for assessing MCMC run convergence.
Methods for summarizing MCMC samples to represent phylogenetic uncertainty.

Main Results:

Detailed examination of prior specification, substitution model selection, and data partitioning strategies.
Identification of key considerations for choosing appropriate evolutionary models.
Recommendations for effective data partitioning in phylogenetic analyses.

Conclusions:

Emphasize the importance of understanding Bayesian phylogenetic methods beyond default settings.
Provide practical guidance on MCMC diagnostics and sample summarization.
Offer a curated list of Bayesian phylogenetic software with usage recommendations.