NetRAX: Accurate and Fast Maximum Likelihood Phylogenetic Network Inference

Phylogenetic networks are used to represent non-treelike evolutionary scenarios. Current, actively developed approaches for phylogenetic network inference jointly account for non-treelike evolution and incomplete lineage sorting (ILS). Unfortunately, this induces a very high computational complexity. Hence, current tools can only analyze small data sets. We present NetRAX, a tool for maximum likelihood inference of phylogenetic networks in the absence of incomplete lineage sorting. Our tool leverages state-of-the-art methods for efficiently computing the phylogenetic likelihood function on trees, and extends them to phylogenetic networks via the notion of "displayed trees". NetRAX can infer maximum likelihood phylogenetic networks from partitioned multiple sequence alignments and returns the inferred networks in Extended Newick format. On simulated data, our results show a very low relative difference in BIC score and a near-zero unrooted softwired cluster distance to the true, simulated networks. With NetRAX, a network inference on a partitioned alignment with 8, 000 sites, 30 taxa, and 3 reticulations completes within a few minutes on a standard laptop. Our implementation is available under the GNU General Public License v3.0 at https://github.com/lutteropp/NetRAX.