Phylogenetic Consensus for Exact Median Trees

Abstract

Solving median tree problems is a classic approach for inferring species trees from a collection of discordant gene trees. Such problems are typically NP-hard and dealt with by local search heuristics. Unfortunately, such heuristics generally lack any provable correctness and precision. Algorithmic advances addressing this uncertainty, have led to exact dynamic programming formulations suitable to solve a well-studied group of median tree problems for smaller phylogenetic analyzes. However, these formulations allow to compute only very few optimal species trees out of possibly many such trees, and phylogenetic studies often require the analysis of all optimal solutions through their consensus tree. Here, we describe a significant algorithmic modification of the dynamic programming formulations that compute the cluster counts of all optimal species trees from which various types of consensus trees can be efficiently computed. Through experimental studies, we demonstrate that our parallel implementation of the modified programming formulation is more efficient than a previous implementation of the original formulation, and can greatly benefit phylogenetic analyses.