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FastNet: Fast and Accurate Statistical Inference of Phylogenetic Networks Using Large-Scale Genomic Sequence Data

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11183)

Abstract

An emerging discovery in phylogenomics is that interspecific gene flow has played a major role in the evolution of many different organisms. To what extent is the Tree of Life not truly a tree reflecting strict “vertical” divergence, but rather a more general graph structure known as a phylogenetic network which also captures “horizontal” gene flow? The answer to this fundamental question not only depends upon densely sampled and divergent genomic sequence data, but also computational methods which are capable of accurately and efficiently inferring phylogenetic networks from large-scale genomic sequence datasets. Recent methodological advances have attempted to address this gap. However, in the 2016 performance study of Hejase and Liu, state-of-the-art methods fell well short of the scalability requirements of existing phylogenomic studies.

The methodological gap remains: how can phylogenetic networks be accurately and efficiently inferred using genomic sequence data involving many dozens or hundreds of taxa? In this study, we address this gap by proposing a new phylogenetic divide-and-conquer method which we call FastNet. We conduct a performance study involving a range of evolutionary scenarios, and we demonstrate that FastNet outperforms state-of-the-art methods in terms of computational efficiency and topological accuracy.

Keywords

Fastnet Phylogenetic Network Topological Accuracy Reticulation Edges Subproblem Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

We gratefully acknowledge the following support: NSF grants no. CCF-1565719 (to KJL), CCF-1714417 (to KJL), and DEB-1737898 (to GMB and KJL), BEACON grants (NSF STC Cooperative Agreement DBI-093954) to GMB and KJL, and computing resources provided by MSU HPCC. We would also like to acknowledge Daniel Neafsey for kindly sending us a processed version of the genomic sequence dataset from [36].

Supplementary material

473852_1_En_14_MOESM1_ESM.pdf (479 kb)
Supplementary material 1 (pdf 479 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Simons Center for Quantitative Biology, Cold Spring Harbor LaboratoryCold Spring HarborUSA
  2. 2.Department of Plant, Soil and Microbial SciencesMichigan State UniversityEast LansingUSA
  3. 3.Department of Computer Science and EngineeringMichigan State UniversityEast LansingUSA

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