Algorithms in supertree inference and phylogenetic data mining

Abstract

Science and society would benefit enormously from comprehensive phylogenetic knowledge of the Tree of Life (ToL), a framework that includes above 1.7 million species on Earth. ToL shows how living things have evolved since the origins of life billions of years ago. With existing computational approaches, we cannot produce a global estimate of evolutionary history from molecular data of all these species. Therefore, the development of computer algorithms and methods is critical to the field of phyloinformatics. The goal of this dissertation is to design, analyze, and implement algorithms to solve three specific problems in the the field of phyloinformatics to support ToL studies. The first part of this dissertation is about algorithms for the matrix representation with flipping (MRF) method to construct large subtrees of the ToL. The utility of the MRF supertree method has been limited by the speed of its heuristic algorithms. We describe a new heuristic algorithm for MRF supertree construction that improves upon the speed of the previous heuristic by a factor of n (the number of taxa in the supertree). This new heuristic makes MRF tractable for large-scale supertree analyses and allows the first comparisons of MRF with other supertree methods using large empirical data sets. Analyses of three published supertree data sets indicate that MRF supertrees are equally or more similar to the input trees on average than matrix representation with parsimony (MRP) and modified mincut supertrees. The results also show that large differences may exist between MRF and MRP supertrees, and demonstrate that the MRF supertree method is a practical and potentially more accurate alternative to the nearly ubiquitous MRP supertree method. The second part of this dissertation is dedicated to new algorithms for partitioning phylogenetic data sets. We describe two new methods to partition phylogenetic data sets of discrete characters based on pairwise compatibility. The partitioning methods make no assumptions regarding the phylogeny,