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Updated: Jun 9, 2025
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Phylogenetic Tree Instability After Taxon Addition: Empirical Frequency, Predictability, and Consequences For Online
Lena Collienne1, Mary Barker1,2, Marc A Suchard3,4,5
1Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA.
Systematic Biology
|October 25, 2024
Summary
Online phylogenetic inference methods can be unstable, with new sequences altering existing tree topologies in nearly 90% of cases. Machine learning can predict and identify key factors contributing to this instability in phylogenetic trees.
Area of Science:
- Computational Biology
- Phylogenetics
- Bioinformatics
Background:
- Online phylogenetic inference methods sequentially add new sequences without full tree recalculation.
- Concerns exist regarding the topological stability of phylogenies when new taxa are added.
Purpose of the Study:
- To analyze the stability of single taxon addition in Maximum Likelihood phylogenetic inference.
- To investigate the predictability of topological instability using machine learning.
Main Methods:
- Analysis of single taxon addition stability across 1000 empirical datasets using a Maximum Likelihood framework.
- Hypothesizing sources of instability and designing summary statistics.
- Employing machine learning (random forests) with summary statistics to predict instability.
Main Results:
- Instability in tree topology was observed in almost 90% of analyzed datasets.
- Topological changes were more frequent in distant parts of the tree with low bootstrap support.
- Machine learning models successfully predicted instability and identified influential features.
Conclusions:
- Strict insertion-only online phylogenetic inference may not yield globally optimal trees.
- Allowing minor tree rearrangements or accepting near-optimal solutions could be feasible alternatives.
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