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Related Concept Videos

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.
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.
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,...
Phylogeny01:23

Phylogeny

Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
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...
The Tree of Life - Bacteria, Archaea, Eukaryotes02:40

The Tree of Life - Bacteria, Archaea, Eukaryotes

The “tree of life” describes the evolution of life and the evolutionary relationships between organisms. The root of the tree is the common ancestor to all life on Earth. All other species radiate from this point, much like the branches of a tree. The numerous tips of these branches on the tree of life represent every living, or extant, species. Extinct species, which are species that no longer exist, can be found towards the center of the tree. Currently, these organisms, both extant and...

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Updated: May 30, 2026

A Practical Guide to Phylogenetics for Nonexperts
12:00

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Guided tree topology proposals for Bayesian phylogenetic inference.

Sebastian Höhna1, Alexei J Drummond

  • 1Department of Mathematics, Stockholm University, SE-10691 Stockholm, Sweden. hoehna@math.su.se

Systematic Biology
|August 11, 2011
PubMed
Summary

New Bayesian Markov chain Monte Carlo (MCMC) samplers significantly speed up phylogenetic analysis of large datasets. These methods offer improved performance predictability, reducing run time variance by 20-fold for accurate phylogenetic tree estimation.

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Area of Science:

  • Computational Biology
  • Phylogenetics
  • Statistical Modeling

Background:

  • Large datasets challenge computationally intensive phylogenetic methods like Bayesian Markov chain Monte Carlo (MCMC).
  • Assessing the performance of common MCMC proposal distributions is crucial for efficient phylogenetic inference.

Purpose of the Study:

  • To evaluate MCMC proposal distribution performance on large phylogenetic datasets.
  • To introduce and assess novel Metropolized Gibbs Samplers for enhanced tree space exploration.
  • To develop improved methods for approximating tree topology posterior probabilities.

Main Methods:

  • Investigated median and variance of run time to convergence for common MCMC proposal distributions across 11 datasets.
  • Introduced and implemented two new Metropolized Gibbs Samplers for MCMC in phylogenetics.
  • Developed and applied conditional clade probabilities for approximating tree topology posterior probabilities.

Main Results:

  • New Metropolized Gibbs Samplers demonstrated faster average run times compared to common MCMC proposals.
  • A 20-fold reduction in the variance of run time to convergence was observed with the new samplers.
  • Conditional clade probabilities proved superior for approximating tree topology posterior probabilities from MCMC samples.

Conclusions:

  • The novel Metropolized Gibbs Samplers offer significant improvements in speed and performance predictability for MCMC-based phylogenetics.
  • Conditional clade probabilities provide a more accurate method for estimating tree topology posterior probabilities, enhancing phylogenetic inference from MCMC simulations.