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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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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,...
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Related Experiment Video

Updated: Jun 12, 2026

A Concoction Pipeline for Generating Molecular Operational Taxonomic Units (MOTUs) Among Riparian and Aquatic Beetles
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Nonstationary evolution and compositional heterogeneity in beetle mitochondrial phylogenomics.

Nathan C Sheffield1, Hojun Song, Stephen L Cameron

  • 1Department of Biology, Brigham Young University, Provo, UT 84602, USA. nathan.sheffield@duke.edu

Systematic Biology
|June 8, 2010
PubMed
Summary

Phylogenetic methods assuming equal nucleotide composition fail for beetle mitochondrial genomes due to nonstationary evolution. Newer nonstationary models in software like p4, PHASE, and nhPhyML accurately recover evolutionary relationships, unlike traditional methods.

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Genotyping Single Nucleotide Polymorphisms in the Mitochondrial Genome by Pyrosequencing

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Area of Science:

  • Evolutionary Biology
  • Bioinformatics
  • Genomics

Background:

  • Phylogenetic analyses often assume equal nucleotide composition across taxa, which is frequently violated in divergent lineages.
  • Nonstationary sequence evolution, where evolutionary rates and patterns differ among lineages, leads to unequal nucleotide composition and can compromise phylogenetic accuracy.
  • Recent theoretical advancements have introduced nonstationary models of sequence evolution, often outperforming stationary models.

Purpose of the Study:

  • To investigate nucleotide composition patterns within the mitochondrial genomes of the insect order Coleoptera (beetles).
  • To evaluate the performance of various phylogenetic inference methods, including both traditional stationary and newer nonstationary models, on this dataset.
  • To identify which phylogenetic methods are robust to the observed variations in nucleotide composition.

Main Methods:

  • Analysis of nucleotide composition variation across species and genes within Coleoptera mitochondrial genomes.
  • Application of eight diverse phylogenetic inference methods: stationary models (parsimony, MrBayes, NJ, LogDet, PhyloBayes) and nonstationary models (p4, PHASE, nhPhyML).
  • Comparison of the accuracy of recovered phylogenies across the different methods.

Main Results:

  • Discovery of significant convergence of nucleotide composition within the mitochondrial genomes of Coleoptera.
  • Demonstration that commonly used stationary phylogenetic methods failed to accurately reconstruct established beetle clades.
  • Identification of specific nonstationary software packages (p4, PHASE, nhPhyML) that successfully overcame systematic bias and recovered correct phylogenies.

Conclusions:

  • The assumption of equal nucleotide composition is often invalid for insect mitochondrial genomes, particularly in Coleoptera.
  • Traditional phylogenetic methods are susceptible to systematic bias caused by nonstationary evolution.
  • Newer phylogenetic software implementing nonstationary models offers a more reliable approach for inferring evolutionary history from complex sequence data.