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Updated: Mar 7, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

State aggregation for fast likelihood computations in molecular evolution.

Iakov I Davydov^1,2, Marc Robinson-Rechavi^1,2, Nicolas Salamin^1,2

¹Department of Ecology and Evolution, Biophore, University of Lausanne, 1015 Lausanne, Switzerland

Bioinformatics (Oxford, England)

|February 8, 2017

Summary

This summary is machine-generated.

State aggregation speeds up codon model computations for evolutionary analysis by reducing computational complexity. This method enhances efficiency without introducing bias, making it applicable to large biological datasets and various phylogenetic models.

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Area of Science:

Evolutionary biology
Computational biology
Molecular evolution

Background:

Codon models are crucial for detecting selection signatures and evolutionary changes in protein-coding genes.
Large state spaces in Markov processes for codon evolution hinder analysis of extensive biological datasets.

Purpose of the Study:

To introduce state aggregation as a method to reduce the state space of codon models.
To improve the computational performance of likelihood estimation in codon evolution models.

Main Methods:

State aggregation heuristic applied to Markov processes.
Implementation in phylogenetic software (godon, FastCodeML).

Main Results:

Accelerated computations for M0 and branch-site models by up to 6.8 times.
Simulations confirmed no detectable bias introduced by state aggregation.
Analysis of real data showed highly correlated predictions with full computations.

Conclusions:

State aggregation is a general and effective approach for optimizing codon and other continuous-time Markov process models.
This method significantly improves computational efficiency for large-scale phylogenetic analyses.