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Updated: Jun 13, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

EREM: Parameter Estimation and Ancestral Reconstruction by Expectation-Maximization Algorithm for a Probabilistic

Liran Carmel¹, Yuri I Wolf, Igor B Rogozin

¹Department of Genetics, The Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Edmond J. Safra Campus, Givat Ram, Jerusalem 91904, Israel.

Advances in Bioinformatics

|May 15, 2010

Summary

This study introduces a probabilistic model for analyzing evolutionary binary characters, like gene presence or absence. The EREM software tool uses this model for accurate ancestral state and event reconstruction in phylogenetics.

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Area of Science:

Evolutionary biology
Phylogenetics
Bioinformatics

Background:

Binary characters (presence/absence) are crucial in evolutionary studies.
Parsimony methods are common but may fail when character gains/losses are frequent.
A probabilistic approach is needed for accurate evolutionary analysis.

Purpose of the Study:

To develop a comprehensive probabilistic model for binary character evolution on phylogenetic trees.
To provide a fast software tool (EREM) for parameter estimation and ancestral state reconstruction.
To improve the analysis of evolutionary events like character gain and loss.

Main Methods:

Developed a probabilistic model for binary character evolution.
Utilized maximum likelihood for parameter estimation.
Implemented a fast computational tool, EREM, for analysis.

Main Results:

The EREM tool efficiently estimates model parameters.
Accurate reconstruction of ancestral states (presence/absence) is achieved.
Gain and loss events along phylogenetic branches are reliably identified.

Conclusions:

The probabilistic model and EREM tool offer a robust framework for studying binary character evolution.
This approach is essential when character gain and loss are not rare events.
Enhances understanding of evolutionary processes in species and genes.