Performance of Supertree Methods on Various Data Set Decompositions

Abstract

Many large-scale phylogenetic reconstruction methods attempt to solve hard optimization problems such as Maximum Parsimony (MP) and Maximum Likelihood (ML), but they are severely limited by the number of taxa that they can handle in a reasonable timeframe. A standard heuristic approach to this problem is the divide-and-conquer strategy: decompose the data set into smaller subsets, solve the subsets (i.e., use MP or ML on each subset to obtain trees), and then combine the solutions to the subsets into a solution for the original data set. This last step — combining given trees into a single tree — is known as supertree construction in computational phylogenetics. The traditional application of supertree methods is to combine existing, published phylogenies into a single phylogeny. Here, we study supertree construction in the context of divide-and-conquer methods for large-scale tree reconstruction. We study several divide-and-conquer approaches and demonstrate experimentally their advantage over the traditional supertree technique of Matrix Representation with Parsimony (MRP), and over global heuristics such as the parsimony ratchet. For the ten large biological data sets under investigation, our study shows that the techniques used for dividing the data set into subproblems as well as those used for merging them into a single solution influence the quality of the supertree construction strongly. In most cases, our merging technique — the Strict Consensus Merger — outperformed MRP with respect to MP scores and running time. Divide-and-conquer techniques are also a highly competitive alternative to global heuristics such as the parsimony ratchet, especially on the more challenging data sets.