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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Bayesian evolutionary model testing in the phylogenomics era: matching model complexity with computational

Guy Baele1, Philippe Lemey

  • 1Department of Microbiology and Immunology, Rega Institute, KU Leuven, Leuven, Belgium. guy.baele@rega.kuleuven.be.

Bioinformatics (Oxford, England)
|June 15, 2013
PubMed
Summary
This summary is machine-generated.

Complex phylogenetic models using codon substitution models best fit mitochondrial genome data. Hierarchical priors improve nucleotide models, outperforming standard codon models, highlighting the need for accurate marginal likelihood estimation.

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Last Updated: May 10, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

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A Practical Guide to Phylogenetics for Nonexperts
12:00

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07:49

Creating and Applying a Reference to Facilitate the Discussion and Classification of Proteins in a Diverse Group

Published on: August 16, 2017

Area of Science:

  • Phylogenomics
  • Computational evolutionary biology
  • Bioinformatics

Background:

  • Advancements in sequencing technologies generate vast genomic data for phylogenetic analysis.
  • High-performance computing is crucial for fitting complex evolutionary models to large datasets.
  • Phylogenomics integrates genomic data and evolutionary modeling.

Purpose of the Study:

  • To conduct an extensive Bayesian phylogenetic model selection study.
  • To compare codon and nucleotide substitution models, including codon position and gene-specific partitioning.
  • To evaluate different prior specifications for partitioned models.

Main Methods:

  • Bayesian phylogenetic model selection.
  • Comparison of codon and nucleotide substitution models.
  • Marginal likelihood estimation using path sampling and stepping-stone sampling.
  • Utilized the BEAGLE library for BEAST on graphics cards.

Main Results:

  • A full codon model, with gene-specific codon models, best fits mitochondrial genome data.
  • Codon position partitioned nucleotide models with hierarchical priors outperformed standard codon models.
  • Hierarchical prior specification is more effective than independent diffuse priors for partitioned models.

Conclusions:

  • Complex codon models are essential for accurate phylogenomic analyses of mitochondrial genomes.
  • Hierarchical priors enhance the performance of partitioned nucleotide models.
  • Accurate marginal likelihood estimation is critical for evaluating complex evolutionary models.