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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...
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Phylogeny01:23

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Updated: Jun 21, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

Bayesian phylogeny analysis via stochastic approximation Monte Carlo.

Sooyoung Cheon1, Faming Liang

  • 1KU Industry-Academy Cooperation Group Team of Economics and Statistics, Korea University, Jochiwon, South Korea. stat-csy@hanmail.net

Molecular Phylogenetics and Evolution
|July 11, 2009
PubMed
Summary
This summary is machine-generated.

Stochastic Approximation Monte Carlo (SAMC) improves Bayesian phylogeny analysis by avoiding local modes common in other Markov chain Monte Carlo methods. SAMC offers more accurate tree inference and parameter estimation with less computational time compared to existing software.

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Related Experiment Videos

Last Updated: Jun 21, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Markov chain Monte Carlo (MCMC) methods are widely used in phylogenetic analysis.
  • Conventional MCMC algorithms, like Metropolis-Hastings, can get trapped in local modes, limiting the effectiveness of phylogenetic tree inference.
  • Bayesian phylogeny analysis requires accurate simulation from posterior distributions.

Purpose of the Study:

  • To introduce and evaluate the Stochastic Approximation Monte Carlo (SAMC) algorithm for Bayesian phylogeny analysis.
  • To compare the performance of SAMC against established Bayesian phylogeny software (BAMBE and MrBayes).
  • To assess the accuracy and efficiency of SAMC in inferring phylogenetic trees and model parameters.

Main Methods:

  • Application of the Stochastic Approximation Monte Carlo (SAMC) algorithm to Bayesian phylogeny reconstruction.
  • Comparative analysis using simulated and real biological datasets.
  • Performance evaluation against BAMBE and MrBayes software.

Main Results:

  • SAMC significantly outperforms BAMBE and MrBayes in Bayesian phylogeny analysis.
  • SAMC generates consensus trees with higher similarity to true trees.
  • SAMC provides more accurate model parameter estimates with smaller mean square errors.
  • SAMC requires less CPU time compared to the other methods.

Conclusions:

  • The Stochastic Approximation Monte Carlo algorithm is a superior method for Bayesian phylogeny analysis.
  • SAMC offers a more efficient and accurate approach to phylogenetic inference.
  • SAMC overcomes limitations of conventional MCMC methods in exploring posterior distributions of phylogenetic trees.