Orientability of Undirected Phylogenetic Networks to a Desired Class: Practical Algorithms and Application to Tree-Child Orientation

TL;DR

This paper introduces a practical fixed-parameter tractable algorithm and a fast heuristic for orienting undirected phylogenetic graphs into desired network classes, improving over existing methods and aiding evolutionary data visualization.

Contribution

It presents a novel exact FPT algorithm applicable to any class C and a fast heuristic specifically for Tree-Child orientation, addressing computational challenges in phylogenetic network design.

Findings

The FPT algorithm outperforms existing exponential algorithms in experiments.

The heuristic is faster but has increasing false negatives with larger reticulation numbers.

The methods expand practical capabilities for visualizing evolutionary data.

Abstract

The C-Orientation problem asks whether it is possible to orient an undirected graph to a directed phylogenetic network of a desired network class C. This problem arises, for example, when visualising evolutionary data, as popular methods such as Neighbor-Net are distance-based and inevitably produce undirected graphs. The complexity of C-Orientation remains open for many classes C, including binary tree-child networks, and practical methods are still lacking. In this paper, we propose an exact FPT algorithm for C-Orientation that is applicable to any class C and parameterised by the reticulation number and the maximum size of minimal basic cycles, and a very fast heuristic for Tree-Child Orientation. While the state-of-the-art for C-Orientation is a simple exponential time algorithm whose computational bottleneck lies in searching for appropriate reticulation vertex placements, our methods significantly reduce this search space. Experiments show that, although our FPT algorithm is still exponential, it significantly outperforms the existing method. The heuristic runs even faster but with increasing false negatives as the reticulation number grows. Given this trade-off, we also discuss theoretical directions for improvement and biological applicability of the heuristic approach.