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Updated: May 25, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

E value cutoff and eukaryotic genome content phylogenetics.

Jeffrey A Rosenfeld¹, Rob DeSalle

¹IST/High Performance and Research Computing, University of Medicine and Dentistry of New Jersey, Newark, NJ 07103, United States. rosenfj1@umdnj.edu

Molecular Phylogenetics and Evolution

|February 7, 2012

Summary

This summary is machine-generated.

Genome content analysis is not a reliable method for determining eukaryotic evolutionary relationships. Adjusting the e-value cutoff critically impacts accuracy, with issues like small genome attraction hindering phylogenetic reconstruction.

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Area of Science:

Genomics
Bioinformatics
Evolutionary Biology

Background:

Genome content analysis is a tool for phylogenetic studies, previously applied to prokaryotes.
Recent eukaryotic genome sequencing enables similar phylogenetic analyses for these organisms.

Purpose of the Study:

To assess the utility of genome content analysis for reconstructing phylogenetic patterns in eukaryotes.
To investigate the impact of e-value cutoffs on phylogenetic accuracy using genome content data.

Main Methods:

Construction of multiple phylogenetic matrices using varying e-value cutoffs.
Analysis of five distinct eukaryotic genome datasets.
Evaluation of the influence of e-value cutoff dynamics on matrix accuracy and information content.

Main Results:

The chosen e-value cutoff significantly affects the accuracy and information content of genome content-based phylogenetic analyses.
Genome content alone proved insufficient for reliable phylogenetic reconstruction in the analyzed eukaryotic taxa.
Small genome attraction and genome duplications were identified as key challenges impacting performance.

Conclusions:

Genome content analysis, by itself, is not a robust method for inferring eukaryotic phylogeny.
Careful consideration of e-value cutoffs and potential confounding factors like genome duplications is essential for future studies.