Building explicit hybridization networks using the maximum likelihood and Neighbor-Joining approaches release_g3yer6wisfavtpaqmzuapg4zaa

Abstract

Tree topologies are the simplest structures which can be used to represent the evolution of species. Over the two last decades more complex structures, called phylogenetic networks, have been introduced to take into account the mechanisms of reticulate evolution, such as species hybridization and horizontal gene transfer among bacteria and viruses. Several algorithms and software have been developed in this context, but most of them yield as output only an implicit network, which can be difficult to interpret. In this paper, we introduce a new algorithm for inferring explicit hybridization networks from binary data. In order to build our explicit hybridization networks, we use a maximum likelihood approach applied to Neighbor-Joining tree configurations.
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