Consensus Clusters in Robinson-Foulds Reticulation Networks

Authors Alexey Markin , Oliver Eulenstein

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Alexey Markin
  • Department of Computer Science, Iowa State University, Ames, IA, USA
Oliver Eulenstein
  • Department of Computer Science, Iowa State University, Ames, IA, USA


The authors want to thank the reviewers for their valuable and constructive comments.

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Alexey Markin and Oliver Eulenstein. Consensus Clusters in Robinson-Foulds Reticulation Networks. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 12:1-12:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


Inference of phylogenetic networks - the evolutionary histories of species involving speciation as well as reticulation events - has proved to be an extremely challenging problem even for smaller datasets easily tackled by supertree inference methods. An effective way to boost the scalability of distance-based supertree methods originates from the Pareto (for clusters) property, which is a highly desirable property for phylogenetic consensus methods. In particular, one can employ strict consensus merger algorithms to boost the scalability and accuracy of supertree methods satisfying Pareto; cf. SuperFine. In this work, we establish a Pareto-like property for phylogenetic networks. Then we consider the recently introduced RF-Net method that heuristically solves the so-called RF-Network problem and which was demonstrated to be an efficient and effective tool for the inference of hybridization and reassortment networks. As our main result, we provide a constructive proof (entailing an explicit refinement algorithm) that the Pareto property applies to the RF-Network problem when the solution space is restricted to the popular class of tree-child networks. This result implies that strict consensus merger strategies, similar to SuperFine, can be directly applied to boost both accuracy and scalability of RF-Net significantly. Finally, we further investigate the optimum solutions to the RF-Network problem; in particular, we describe structural properties of all optimum (tree-child) RF-networks in relation to strict consensus clusters of the input trees.

Subject Classification

ACM Subject Classification
  • Applied computing → Computational biology
  • Mathematics of computing → Graph theory
  • Phylogenetics
  • phylogenetic tree
  • phylogenetic network
  • reticulation network
  • Robinson-Foulds
  • Pareto
  • RF-Net


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