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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.
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.
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,...
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...
Pedigree Analysis01:35

Pedigree Analysis

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Related Experiment Video

Updated: May 14, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Graph hierarchies for phylogeography.

Gabriela B Cybis1, Janet S Sinsheimer, Philippe Lemey

  • 1Department of Biomathematics, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
|February 6, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel hierarchical Bayesian phylogeographic framework to analyze spatial spread patterns in evolving organisms. The enhanced method integrates multiple datasets for more efficient and accurate evolutionary and geographical modeling.

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Area of Science:

  • Computational Biology
  • Evolutionary Biology
  • Epidemiology

Background:

  • Bayesian phylogeographic methods are valuable for understanding spatial spread in evolving organisms.
  • Existing methods can be limited when analyzing multiple, related datasets.

Purpose of the Study:

  • To develop an improved Bayesian phylogeographic framework by integrating multiple datasets hierarchically.
  • To enhance the analysis of spatial spread patterns and evolutionary histories.

Main Methods:

  • A hierarchical Bayesian approach combining multiple phylogeographic datasets.
  • Utilizing a random graph at the hierarchical level to model inter-location migration relevance.
  • Applying the method to viral sequence and location data.

Main Results:

  • The proposed framework offers an efficient and improved approach for analyzing hierarchical datasets.
  • Demonstrated effectiveness in analyzing dengue virus serotypes across the Americas.
  • Successfully applied to intrahost HIV evolution across multiple patients.

Conclusions:

  • Hierarchical Bayesian phylogeography provides a powerful tool for complex spatial and evolutionary analyses.
  • The method enhances the integration of diverse data sources for robust phylogeographic inference.
  • This framework has broad applicability in studying the spread of infectious diseases and other evolving entities.