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Updated: Apr 29, 2026

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

An experimentally determined evolutionary model dramatically improves phylogenetic fit.

Jesse D Bloom¹

¹Division of Basic Sciences and Computational Biology Program, Fred Hutchinson Cancer Research Center, Seattle, WA jbloom@fhcrc.org.

Molecular Biology and Evolution

|May 27, 2014

Summary

This summary is machine-generated.

Researchers developed a new, parameter-free model for gene evolution using experimental data. This novel approach accurately describes influenza nucleoprotein gene phylogeny, outperforming complex existing models.

Keywords:

codon model deep mutational scanning influenza nucleoprotein phylogenetics substitution model

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Last Updated: Apr 29, 2026

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Area of Science:

Molecular Biology
Evolutionary Biology
Bioinformatics

Background:

Modern molecular phylogenetics relies on quantitative models of gene evolution.
Existing evolutionary models fail to realistically capture site-heterogeneous selection driving sequence change.
Current methods attempt to improve models by adding numerous free parameters.

Purpose of the Study:

To demonstrate an alternative, parameter-free approach to modeling gene evolution.
To experimentally determine a more accurate evolutionary model.
To improve phylogenetic and genetic analyses.

Main Methods:

Experimental determination of an evolutionary model using mutagenesis.
Functional selection and deep sequencing were employed.
Development of a parameter-free model for influenza nucleoprotein.

Main Results:

The experimentally derived model significantly improves the description of influenza nucleoprotein gene phylogeny.
This parameter-free model outperforms existing models with many free parameters.
High-throughput experimental strategies offer new insights into phylogenetic analysis.

Conclusions:

Experimental determination provides a powerful alternative to parameter-heavy evolutionary models.
This approach enhances the accuracy and sensitivity of phylogenetic and genetic analyses.
Emerging experimental techniques have the potential to revolutionize evolutionary studies.