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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
Microbial Phylogeny01:28

Microbial Phylogeny

Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...
Phylogenetic Trees03:21

Phylogenetic Trees

Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.The length of the branches can depict time or the relative amount of change among organisms. For instance, the branch length might indicate the number of amino acid changes in the sequence that underlies the...

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Related Experiment Video

Updated: Jun 23, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Computational methods for evaluating phylogenetic models of coding sequence evolution with dependence between codons.

Nicolas Rodrigue1, Claudia L Kleinman, Hervé Philippe

  • 1Department of Biology, Center for Advanced Research in Environmental Genomics, University of Ottawa, Ottawa, Ontario, Canada. nicolas.rodrigue@uottawa.ca

Molecular Biology and Evolution
|April 23, 2009
PubMed
Summary
This summary is machine-generated.

New computational methods enable quantitative evaluation of molecular evolutionary models. Site-interdependent codon substitution models, while improved, were outperformed by site-independent versions in phylogenetic analyses.

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Related Experiment Videos

Last Updated: Jun 23, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Published on: August 14, 2018

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12:00

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Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues
07:08

Optimization of Synthetic Proteins: Identification of Interpositional Dependencies Indicating Structurally and/or Functionally Linked Residues

Published on: July 14, 2015

Area of Science:

  • Molecular Evolution
  • Computational Biology
  • Phylogenetics

Background:

  • Site-interdependent Markovian codon substitution models aim to capture long-range evolutionary selective features.
  • These models utilize protein structure and statistical potentials but face computational challenges for quantitative evaluation.

Purpose of the Study:

  • To develop Markov chain Monte Carlo (MCMC) methodologies for parameter sampling and Bayesian model assessment of site-interdependent codon substitution models.
  • To enable quantitative comparison and ranking of these models within a phylogenetic context.

Main Methods:

  • Implemented MCMC techniques for posterior distribution sampling of model parameters.
  • Utilized posterior predictive checking and thermodynamic integration for Bayes factor computation.
  • Applied methodologies to two empirical data sets.

Main Results:

  • The developed MCMC methods allow for Bayesian model assessment and ranking of codon substitution models.
  • Current site-interdependent models show improved fit over simpler models but are outperformed by extended site-independent models.
  • The study provides a framework for contrasting different approaches to modeling structural constraints.

Conclusions:

  • The developed computational methodologies facilitate rigorous evaluation of complex molecular evolutionary models.
  • Extended site-independent models currently offer a better fit than existing site-interdependent models for the tested data.
  • This work enables quantified comparisons of models incorporating site interdependencies versus those that do not.