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Predicting Phylogenetic Bootstrap Values via Machine Learning.

Julius Wiegert1, Dimitri Höhler1, Julia Haag1

  • 1Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.

Molecular Biology and Evolution
|October 17, 2024
PubMed
Summary
This summary is machine-generated.

We introduce the educated bootstrap guesser (EBG), a machine learning tool that rapidly predicts phylogenetic tree branch support values. EBG offers a faster and accurate alternative to standard bootstrap methods, improving phylogenetic analysis efficiency.

Keywords:
bootstrap supportmachine learningphylogeneticsuncertainty estimation

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Area of Science:

  • Phylogenetics and evolutionary biology
  • Computational biology and bioinformatics
  • Machine learning applications in bioinformatics

Background:

  • Estimating statistical robustness of phylogenetic trees is crucial for reliable evolutionary inference.
  • Standard nonparametric Felsenstein bootstrap support (SBS) is computationally intensive, leading to the development of faster approximation methods.
  • Existing faster methods like rapid bootstrap (RB), SH-aLRT, and UltraFast bootstrap 2 (UFBoot2) have limitations, including computational cost, model violation assessment needs, or instability in low support ranges.

Purpose of the Study:

  • To develop a machine learning-based tool, the educated bootstrap guesser (EBG), for predicting Standard, nonparametric Felsenstein bootstrap support (SBS) values.
  • To provide a computationally efficient and accurate method for assessing phylogenetic branch support.
  • To offer uncertainty measures for branch support predictions to enhance interpretation.

Main Methods:

  • Development of a machine learning model (EBG) trained to predict SBS values from phylogenetic tree data.
  • Benchmarking EBG against existing methods like UFBoot2 in terms of speed and accuracy.
  • Evaluation of prediction accuracy using median absolute error and assessment of uncertainty quantification.

Main Results:

  • EBG is, on average, 9.4 (σ=5.5) times faster than UFBoot2.
  • EBG achieves a median absolute error of 5 for SBS values between 0 and 100.
  • EBG can predict support values for large phylogenies (e.g., 1,654 sequences) within hours on standard hardware.
  • EBG provides uncertainty estimates for each predicted branch support value.

Conclusions:

  • EBG presents a significant advancement in computational efficiency for phylogenetic analysis.
  • The tool offers accurate SBS predictions with valuable uncertainty quantification, facilitating more robust interpretations.
  • EBG democratizes robust phylogenetic inference by enabling rapid analysis on accessible computational resources.