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Related Experiment Videos

Pruning rogue taxa improves phylogenetic accuracy: an efficient algorithm and webservice.

Andre J Aberer1, Denis Krompass, Alexandros Stamatakis

  • 1Exelixis Laboratory, Scientific Computing Group, Heidelberg Institute for Theoretical Studies (HITS gGmbH), Schloss-Wolfsbrunnenweg 35, D-69118 Heidelberg, Germany. andre.aberer@h-its.org

Systematic Biology
|September 11, 2012
PubMed
Summary
This summary is machine-generated.

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A new graph-based algorithm efficiently identifies rogue taxa in phylogenetic trees, improving bootstrap analysis results. This method enhances the accuracy of consensus trees by removing problematic taxa.

Area of Science:

  • Phylogenetics
  • Computational Biology
  • Bioinformatics

Background:

  • Rogue taxa, or problematic taxa, can negatively impact phylogenetic tree reconstruction and bootstrap analysis.
  • Existing methods for rogue taxon identification may lack the scalability to handle large datasets.

Purpose of the Study:

  • To introduce an efficient graph-based algorithm for identifying rogue taxa in phylogenetic trees.
  • To develop an interactive web service for implementing this algorithm.
  • To improve the accuracy and support of consensus trees by effectively pruning rogue taxa.

Main Methods:

  • Development of an efficient graph-based algorithm for rogue taxon identification.
  • Implementation of the algorithm in an interactive web service.
  • Comparison of the new algorithm's performance against previous methods and competing approaches.

Related Experiment Videos

  • Application of a parallelized open-source code to large-scale datasets.

    • Main Results:

    • The new algorithm is up to 4 orders of magnitude faster than previous methods, yielding qualitatively identical results.
    • The enhanced scalability allows for the identification of more complex rogue taxon constellations.
    • The method produces better-supported reduced/pruned consensus trees compared to competing methods.
    • Successful identification of rogue taxa in large datasets (100 trees with 116,334 taxa each).
    • Pruning rogue taxa results in bootstrap and maximum-likelihood trees that are topologically closer to true trees in simulated datasets.

    Conclusions:

    • The novel graph-based algorithm offers a significant improvement in speed and scalability for rogue taxon identification.
    • This method enhances the reliability of phylogenetic analyses by providing more robust consensus trees.
    • The open-source implementation facilitates broader application in bioinformatics and evolutionary studies.