Phylogenetic networks: modeling, reconstructibility, and accuracy.

Phylogenetic networks model the evolutionary history of sets of organisms when events such as hybrid speciation and horizontal gene transfer occur. In spite of their widely acknowledged importance in evolutionary biology, phylogenetic networks have so far been studied mostly for specific data sets. We present a general definition of phylogenetic networks in terms of directed acyclic graphs (DAGs) and a set of conditions. Further, we distinguish between model networks and reconstructible ones and characterize the effect of extinction and taxon sampling on the reconstructibility of the network. Simulation studies are a standard technique for assessing the performance of phylogenetic methods. A main step in such studies entails quantifying the topological error between the model and inferred phylogenies. While many measures of tree topological accuracy have been proposed, none exist for phylogenetic networks. Previously, we proposed the first such measure, which applied only to a restricted class of networks. In this paper, we extend that measure to apply to all networks, and prove that it is a metric on the space of phylogenetic networks. Our results allow for the systematic study of existing network methods, and for the design of new accurate ones.

Towards the development of computational tools for evaluating phylogenetic network reconstruction methods.

We report on a suite of algorithms and techniques that together provide a simulation flow for studying the topological accuracy of methods for reconstructing phylogenetic networks. We implemented those algorithms and techniques and used three phylogenetic reconstruction methods for a case study of our tools. We present the results of our experimental studies in analyzing the relative performance of these methods. Our results indicate that our simulator and our proposed measure of accuracy, the latter an extension of the widely used Robinson-Foulds measure, offer a robust platform for the evaluation of network reconstruction algorithms.