Breaking bud: probing the scalability limits of phylogenetic network inference methods

BioRxiv : the Preprint Server for Biology
Hussein A Hejase, Kevin J Liu

Abstract

Background: Branching events in phylogenetic trees reflect strictly bifurcating and/or multifurcating speciation and splitting events. In the presence of gene flow, a phylogeny cannot be described by a tree but is instead a directed acyclic graph known as a phylogenetic network. Both phylogenetic trees and networks are typically reconstructed using computational analysis of multi-locus sequence data. The advent of high-throughput sequencing technologies has brought about two main scalability challenges: (1) dataset size in terms of the number of taxa and (2) the evolutionary divergence of the taxa in a study. The impact of both dimensions of scale on phylogenetic tree inference has been well characterized by recent studies; in contrast, the scalability limits of phylogenetic network inference methods are largely unknown. In this study, we quantify the performance of state-of-the-art phylogenetic network inference methods on large-scale datasets using empirical data sampled from natural mouse populations and synthetic data capturing a wide range of evolutionary scenarios. Results: We find that, as in the case of phylogenetic tree inference, the performance of leading network inference methods is negatively impacted by both dimen...Continue Reading

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