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Bootstrap hypothesis tests for evolutionary trees and other dendrograms

J K Brown1

  • 1Cereals Research Department, John Innes Centre, Norwich, England.

Proceedings of the National Academy of Sciences of the United States of America
|December 6, 1994
PubMed
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This study introduces efficient bootstrap methods for phylogenetic tree analysis, offering alternatives to the conservative 95% rule for statistical inference and hypothesis testing in phylogenetics.

Area of Science:

  • Computational Biology
  • Statistics
  • Bioinformatics

Background:

  • The bootstrap computer-intensive statistical technique is widely used for phylogenetic tree analysis.
  • The common 95% rule for significant group support in bootstrap trees is often conservative.
  • Existing methods may require extensive computational resources for phylogenetic inference.

Purpose of the Study:

  • To describe novel bootstrap methods for statistical inference on phylogenies.
  • To develop tests for nonrandom support of single or multiple phylogenetic groups/trees.
  • To provide alternatives to the conventional 95% rule for evaluating phylogenetic support.

Main Methods:

  • Developed three distinct bootstrap-based statistical tests for phylogenetic inference.

Related Experiment Videos

  • Proposed a sequential statistical method to enhance computational efficiency.
  • Applied methods to parsimony analysis of plant 5S rRNA sequences and cluster analysis of fungal DNA.
  • Demonstrated applicability to dendrogram analysis beyond taxonomy.

    • Main Results:

    • The proposed methods require fewer bootstrap trees for estimation compared to traditional approaches.
    • The new tests offer statistically rigorous ways to assess phylogenetic support.
    • Illustrative examples show successful application in plant and fungal phylogenetics.
    • The methods are adaptable for analyzing various dendrograms and testing multiple hypotheses.

    Conclusions:

    • The introduced bootstrap methods provide efficient and statistically sound tools for phylogenetic analysis.
    • These methods can replace or supplement existing rules like the 95% rule.
    • The techniques are versatile and applicable to diverse datasets and scientific domains beyond taxonomy.