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Scalable, Flexible, and Cost-Effective Seedling Grafting

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Ranked Subtree Prune and Regraft.

Lena Collienne¹, Chris Whidden², Alex Gavryushkin^3,4

¹Biological Data Science Laboratory, School of Mathematics and Statistics, University of Canterbury, Christchurch, New Zealand. lena@lenacoll.de.

Bulletin of Mathematical Biology

|January 31, 2024

Summary

This summary is machine-generated.

We introduce novel ranked Subtree Prune and Regraft (SPR) treespaces for analyzing phylogenetic time trees. Surprisingly, adding leaves can decrease ranked SPR distance, impacting evolutionary inference algorithms.

Keywords:

Phylogenetics Ranked trees Time trees Tree Rearrangements Tree distance Treespace

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Area of Science:

Computational Biology
Evolutionary Biology
Bioinformatics

Background:

Phylogenetic time trees model evolutionary histories, crucial for understanding virus transmission and cancer evolution.
Reconstructing time trees often uses Bayesian inference with Markov Chain Monte Carlo (MCMC) sampling.
Existing tree rearrangement operations like Subtree Prune and Regraft (SPR) are well-studied, but time tree variants are less understood.

Purpose of the Study:

To introduce and study novel treespaces based on ranked Subtree Prune and Regraft (SPR) operations for phylogenetic time trees.
To analyze algorithmic properties, computational complexity, and distances within these new treespaces.
To compare these novel treespaces with existing tree rearrangement spaces.

Main Methods:

Modification of the classical SPR rearrangement operation applied to ranked phylogenetic trees.
Analysis of algorithmic properties, focusing on distance computation complexity.
Comparison with established tree rearrangement based treespaces.

Main Results:

Introduction of two novel treespaces based on ranked SPR operations.
Characterization of algorithmic properties, including distance computation complexity.
Demonstration of the counterintuitive finding that adding leaves can decrease ranked SPR distance.

Conclusions:

The newly defined ranked SPR treespaces offer a framework for analyzing time tree algorithms.
The counterintuitive distance property has implications for time tree sampling, particularly with uncertain taxa.
Further study of these treespaces can enhance comparative analysis of phylogenetic inference methods.