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Evaluation of the Relative Performance of the Subflattenings Method for Phylogenetic Inference
Joshua Stevenson1, Barbara Holland2, Michael Charleston2
1School of Natural Sciences (Discipline of Mathematics), University of Tasmania, Hobart, Australia. joshua.stevenson@utas.edu.au.
Bulletin of Mathematical Biology
|January 30, 2023
Summary
Subflattenings offer a promising method for identifying phylogenetic tree edges from sequence data. Simulations show they perform similarly to flattenings but may be less susceptible to biases like long branch attraction.
Area of Science:
- Phylogenetics and evolutionary biology
- Computational biology and bioinformatics
Background:
- Phylogenetic tree inference relies on identifying evolutionary relationships from sequence data.
- Flattenings and subflattenings are algebraic methods using pattern counts for edge identification.
- Subflattenings require further evaluation for their effectiveness in phylogenetic tree reconstruction.
Purpose of the Study:
- To introduce software for constructing and evaluating subflattenings for phylogenetic splits.
- To compare the effectiveness of subflattenings against flattenings using simulations.
- To assess susceptibility to biases in phylogenetic inference.
Main Methods:
- Development of software for computing subflattenings.
- Utilizing various methods to improve computational tractability of subflattenings.
- Conducting simulations to compare subflattenings and flattenings on split score distributions and bias susceptibility.
Main Results:
- Subflattenings demonstrate similar split score distributions to flattenings across examined trees.
- Subflattenings appear less affected by biases related to split size/balance.
- Subflattenings show reduced susceptibility to long branch attraction bias compared to flattenings.
Conclusions:
- Subflattenings are a viable tool for phylogenetic tree inference, comparable to flattenings.
- Subflattenings may offer advantages in robustness against specific phylogenetic biases.
- These findings support the development of algorithms utilizing subflattenings for improved phylogenetic analysis.
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