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FastNet: Fast and accurate inference of phylogenetic networks using large-scale genomic sequence data

Hussein A Hejase, Natalie VandePol, Gregory A Bonito, Kevin J Liu
doi: https://doi.org/10.1101/132795
Hussein A Hejase
1Department of Computer Science and Engineering, Michigan State University, East Lansing, 48824, MI, USA
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Natalie VandePol
2Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, 48824, MI, USA
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Gregory A Bonito
2Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, 48824, MI, USA
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Kevin J Liu
1Department of Computer Science and Engineering, Michigan State University, East Lansing, 48824, MI, USA
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Abstract

Motivation Advances in next-generation sequencing technologies and phylogenomics have reshaped our understanding of evolutionary biology. One primary outcome is the emerging discovery that interspecific gene flow has played a major role in the evolution of many different organisms across the Tree of Life. 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?

Results 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 a recent performance study, we demonstrated that the state of the art falls 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. Using synthetic and empirical data spanning a range of evolutionary scenarios, we demonstrate that FastNet outperforms state-of-the-art methods in terms of computational efficiency and topological accuracy.

We predict an imminent need for new computational methodologies that can cope with dataset scale at the next order of magnitude, involving thousands of genomes or more. We consider FastNet to be a next step in this direction. We conclude with thoughts on the way forward through future algorithmic enhancements.

Contact kjl{at}msu.edu

Supplementary information Supplementary data are available at https://gitlab.msu.edu/liulab/FastNet.data.scripts.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 01, 2017.
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FastNet: Fast and accurate inference of phylogenetic networks using large-scale genomic sequence data
Hussein A Hejase, Natalie VandePol, Gregory A Bonito, Kevin J Liu
bioRxiv 132795; doi: https://doi.org/10.1101/132795
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FastNet: Fast and accurate inference of phylogenetic networks using large-scale genomic sequence data
Hussein A Hejase, Natalie VandePol, Gregory A Bonito, Kevin J Liu
bioRxiv 132795; doi: https://doi.org/10.1101/132795

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