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Empirical Performance of Tree-Based Inference of Phylogenetic Networks

Authors Zhen Cao, Jiafan Zhu, Luay Nakhleh



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Author Details

Zhen Cao
  • Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA
Jiafan Zhu
  • Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA
Luay Nakhleh
  • Department of Computer Science, Rice University, 6100 Main Street, Houston, TX 77005, USA

Acknowledgements

The authors would like to thank Dr. Huw A. Ogilvie for his help.

Cite AsGet BibTex

Zhen Cao, Jiafan Zhu, and Luay Nakhleh. Empirical Performance of Tree-Based Inference of Phylogenetic Networks. In 19th International Workshop on Algorithms in Bioinformatics (WABI 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 143, pp. 21:1-21:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.WABI.2019.21

Abstract

Phylogenetic networks extend the phylogenetic tree structure and allow for modeling vertical and horizontal evolution in a single framework. Statistical inference of phylogenetic networks is prohibitive and currently limited to small networks. An approach that could significantly improve phylogenetic network space exploration is based on first inferring an evolutionary tree of the species under consideration, and then augmenting the tree into a network by adding a set of "horizontal" edges to better fit the data. In this paper, we study the performance of such an approach on networks generated under a birth-hybridization model and explore its feasibility as an alternative to approaches that search the phylogenetic network space directly (without relying on a fixed underlying tree). We find that the concatenation method does poorly at obtaining a "backbone" tree that could be augmented into the correct network, whereas the popular species tree inference method ASTRAL does significantly better at such a task. We then evaluated the tree-to-network augmentation phase under the minimizing deep coalescence and pseudo-likelihood criteria. We find that even though this is a much faster approach than the direct search of the network space, the accuracy is much poorer, even when the backbone tree is a good starting tree. Our results show that tree-based inference of phylogenetic networks could yield very poor results. As exploration of the network space directly in search of maximum likelihood estimates or a representative sample of the posterior is very expensive, significant improvements to the computational complexity of phylogenetic network inference are imperative if analyses of large data sets are to be performed. We show that a recently developed divide-and-conquer approach significantly outperforms tree-based inference in terms of accuracy, albeit still at a higher computational cost.

Subject Classification

ACM Subject Classification
  • Applied computing → Genomics
  • Applied computing → Computational biology
Keywords
  • Phylogenetic networks
  • species tree
  • tree-based networks
  • multi-locus phylogeny

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