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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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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

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Published on: August 14, 2018

Uncovering hidden phylogenetic consensus in large data sets.

Nicholas D Pattengale1, Andre J Aberer, Krister M Swenson

  • 1Sandia National Laboratories, PO Box 5800, Albuquerque, NM 87185, USA. ndpatte@sandia.gov

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|February 9, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to identify and address "rogue taxa" in phylogenetic reconstruction. By removing these problematic taxa, the accuracy and reliability of evolutionary trees are significantly improved.

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Last Updated: Jun 4, 2026

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Area of Science:

  • Phylogenetics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Phylogenetic reconstruction is often hindered by "rogue taxa," which are difficult to place accurately within evolutionary trees.
  • Phylogenetic consensus methods are particularly susceptible to the confounding effects of rogue taxa.

Purpose of the Study:

  • To develop a novel framework for defining and identifying rogue taxa in phylogenetic analyses.
  • To propose a method for recomputing support values to mitigate inaccuracies caused by rogue taxa.

Main Methods:

  • Formulated a bicriterion optimization problem, the relative information criterion, to quantify the information gain from removing taxa.
  • Developed and applied a greedy heuristic to identify subsets of rogue taxa.
  • Integrated the rogue taxa identification algorithm into RAxML v7.2.7 for large-scale analyses.

Main Results:

  • Experiments with pathological and large biological datasets demonstrated the effectiveness of the rogue taxa identification framework.
  • Recomputing support values after identifying rogue taxa led to a significant increase in supported phylogenetic edges.
  • The algorithm was successfully integrated into RAxML, enabling analysis of datasets up to 2,500 taxa.

Conclusions:

  • The proposed framework provides an effective means to define, identify, and manage rogue taxa in phylogenetics.
  • Addressing rogue taxa can substantially improve the reliability and accuracy of phylogenetic trees.
  • Existing phylogenies may require recomputation and reevaluation to account for the impact of rogue taxa.