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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 Barker^1,2, Marc A Suchard^3,4,5

¹Computational Biology Program, Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N, Seattle, WA 98109, USA.

Systematic Biology

|October 25, 2024

Summary

This summary is machine-generated.

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.

Keywords:

Maximum Likelihood online phylogenetics phylogenetic stability taxon addition

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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.