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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: Jul 19, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Data-specific substitution models improve protein-based phylogenetics.

João M Brazão¹, Peter G Foster², Cymon J Cox¹

¹Centro de Ciências do Mar, Universidade do Algarve, Faro, Algarve, Portugal.

|August 14, 2023

Summary

This summary is machine-generated.

Estimating accurate, data-specific amino-acid substitution models for proteins is crucial for phylogenetic analysis. This study found that data-specific models, particularly those from IQ-TREE and P4 (maximum likelihood), offer superior accuracy and efficiency over empirical models.

Keywords:

Amino-acid substitution models Data-specific models Model estimation Phylogenetics Protein evolution

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Area of Science:

Computational Biology
Phylogenetics
Bioinformatics

Background:

Estimating accurate amino-acid substitution models is vital for phylogenetic analysis but computationally challenging.
Existing empirical models may not adequately represent specific protein datasets, potentially leading to inaccurate evolutionary inferences.

Purpose of the Study:

To evaluate the computational efficiency and accuracy of five methods for estimating data-specific amino-acid substitution models.
To compare the performance of data-specific models against commonly used empirical models (cpREV, WAG).
To demonstrate the benefits of using data-specific models in phylogenetic analyses of published datasets.

Main Methods:

Simulated protein alignments were generated using known models and trees.
Five methods (Codeml, FastMG, IQ-TREE, P4-ML, P4-Bayesian) were used to estimate data-specific models.
Maximum likelihood scores and tree topologies were compared between data-specific and empirical models against the simulation model.

Main Results:

Data-specific models significantly outperformed empirical models, showing better fit and inferring more accurate trees.
IQ-TREE and P4 (maximum likelihood) methods produced trees statistically indistinguishable from the simulation model.
Re-analysis of published datasets with IQ-TREE-estimated models revealed improved data fit, longer inferred trees, and altered topologies in over half of the cases.

Conclusions:

Data-specific amino-acid substitution models provide a more accurate representation of protein evolution than empirical models.
IQ-TREE and P4 (maximum likelihood) are recommended for their accuracy and efficiency in estimating these models.
Computational burden and software availability are no longer significant limitations for generating superior data-specific models for phylogenetic analyses.