Generalized Buneman pruning for inferring the most parsimonious multi-state phylogeny

TL;DR

This paper introduces a new exact method for multi-state phylogeny reconstruction that generalizes the Buneman graph, enabling provably optimal solutions for larger and more complex datasets than previously possible.

Contribution

It presents a generalized Buneman graph and an ILP-based approach for exact multi-state maximum parsimony phylogeny inference, improving scalability and optimality.

Findings

Able to solve previously intractable datasets

Runs in times comparable to heuristics

First practical exact method for multi-state data

Abstract

Accurate reconstruction of phylogenies remains a key challenge in evolutionary biology. Most biologically plausible formulations of the problem are formally NP-hard, with no known efficient solution. The standard in practice are fast heuristic methods that are empirically known to work very well in general, but can yield results arbitrarily far from optimal. Practical exact methods, which yield exponential worst-case running times but generally much better times in practice, provide an important alternative. We report progress in this direction by introducing a provably optimal method for the weighted multi-state maximum parsimony phylogeny problem. The method is based on generalizing the notion of the Buneman graph, a construction key to efficient exact methods for binary sequences, so as to apply to sequences with arbitrary finite numbers of states with arbitrary state transition weights. We implement an integer linear programming (ILP) method for the multi-state problem using this generalized Buneman graph and demonstrate that the resulting method is able to solve data sets that are intractable by prior exact methods in run times comparable with popular heuristics. Our work provides the first method for provably optimal maximum parsimony phylogeny inference that is practical for multi-state data sets of more than a few characters.