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Ranking Top- k Trees in Tree-Based Phylogenetic Networks.

Momoko Hayamizu, Kazuhisa Makino

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|April 4, 2023

Summary

This study introduces a linear-delay algorithm to rank the top-k most likely evolutionary support trees within complex phylogenetic networks. This method efficiently identifies optimal and near-optimal evolutionary pathways from complex data.

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Area of Science:

Computational Biology
Phylogenetics
Evolutionary Biology

Background:

Tree-based phylogenetic networks model complex evolutionary histories beyond simple tree structures.
Existing methods face computational challenges due to the exponential number of possible support trees within these networks.
Prior work established a structure theorem for rooted binary phylogenetic networks, enabling efficient counting and enumeration of support trees.

Purpose of the Study:

To develop an algorithm for ranking the top-k most likely support trees in a given tree-based phylogenetic network.
To address the practical need for identifying near-optimal evolutionary solutions, not just the single optimal one.
To leverage arc probabilities for likelihood-based ranking of support trees.

Main Methods:

The study focuses on tree-based phylogenetic networks with probabilistically weighted arcs.
An algorithm is designed to compute the ranking of support trees based on their likelihood values.
The algorithm achieves linear delay, ensuring optimal performance for the ranking task.

Main Results:

A novel algorithm is presented for computing the ranking of top-k support trees in a phylogenetic network.
The algorithm operates with linear delay, providing an efficient solution to the stated problem.
This enables the practical identification of multiple high-likelihood evolutionary scenarios.

Conclusions:

The developed algorithm efficiently ranks top-k support trees in phylogenetic networks, addressing a key computational challenge.
This advancement facilitates a more comprehensive understanding of evolutionary processes by considering multiple likely scenarios.
The linear-delay performance makes this approach practical for analyzing complex evolutionary data.