Networks for phylogenomics
Abstract of C. Scornavacca presentation at the Cross Disciplinary Genomics Symposium
Phylogenetic analysis is the study of evolution and relationships between different species (or taxa), generally computed from biological sequence data. Trees are the most used datatypes to conceptualize, visualize and analyze the evolution of different biological lineages. In fact, they are well suited for describing the evolutionary pathways of different species, because of them being intuitive as well as easy to query and study from both computational and mathematical point of view. However, trees are impractical when it comes to accommodate for reticulated events such as horizontal gene transfer, hybridization and recombination between lineages, thus the need for novel methods in order to fill this gap. Phylogenetic networks provide such an alternative. The topic that was presented by C. Scornavacca is an overview of how networks can improve phylogenomics.
A phylogenetic network is a connected graph where terminal nodes are associated with biological species or sequences. (including biological species). They can be either explicit or implicit (Huson 2011). It is important to note this distinction, since abstract networks (also called “data visualization graphs”) do not always take into account the evolutionary constraints, therefore making them ill suited for the study of biological phenomena, but still can provide some insight by displaying the data in a differently meaningful way.
Another distinction (that also applies for phylogenetic trees) is whether the network is rooted or unrooted. A rooted tree is a connected and directed acyclic graph where the root of the tree relates to the most ancient common ancestor of the represented species, whereas the unrooted tree only explains how species are related to each other. Networks have been extensively used to model unrooted trees, espacially Neightbor-net, consensus split networks and median-joining. When it comes to rooted trees, those algorithm are ill-suited because of the add biological constraints, not to mention that they are seldom completely defined and optimized, thus not usable as tools at large scales.
There are other means of classifying networks, depending on reconstruction data : from sequences, clusters, distances, trees or splits.