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Consensus Clusters in Robinson-Foulds Reticulation Networks

Authors Alexey Markin , Oliver Eulenstein

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Author Details

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

Funding

This material is based upon work supported by the National Science Foundation under Grant No. 1617626.

Acknowledgements

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) https://doi.org/10.4230/LIPIcs.WABI.2019.12

Abstract

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
Keywords
  • Phylogenetics
  • phylogenetic tree
  • phylogenetic network
  • reticulation network
  • Robinson-Foulds
  • Pareto
  • RF-Net

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