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A Practical Guide to Phylogenetics for Nonexperts
Published on: February 5, 2014
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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
Summary
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.
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.
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