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A Polynomial-Time Algorithm for Minimizing the Deep Coalescence Cost for Level-1 Species Networks

Matthew LeMay, View ORCID ProfileRan Libeskind-Hadas, View ORCID ProfileYi-Chieh Wu
doi: https://doi.org/10.1101/2020.11.04.368845
Matthew LeMay
1Department of Mathematics, Harvey Mudd College, Claremont, California 91711, USA
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Ran Libeskind-Hadas
2Department of Computer Science, Harvey Mudd College, Claremont, California 91711, USA
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  • ORCID record for Ran Libeskind-Hadas
Yi-Chieh Wu
2Department of Computer Science, Harvey Mudd College, Claremont, California 91711, USA
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  • For correspondence: yjw@cs.hmc.edu
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Abstract

Phylogenetic analyses commonly assume that the species history can be represented as a tree. However, in the presence of hybridization, the species history is more accurately captured as a network. Despite several advances in modeling phylogenetic networks, there is no known polynomial-time algorithm for parsimoniously reconciling gene trees with species networks while accounting for incomplete lineage sorting. To address this issue, we present a polynomial-time algorithm for the case of level-1 networks, in which no hybrid species is the direct ancestor of another hybrid species. This work enables more efficient reconciliation of gene trees with species networks, which in turn, enables more efficient reconstruction of species networks.

Competing Interest Statement

The authors have declared no competing interest.

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  • Draft remarks removed.

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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 December 07, 2020.
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A Polynomial-Time Algorithm for Minimizing the Deep Coalescence Cost for Level-1 Species Networks
Matthew LeMay, Ran Libeskind-Hadas, Yi-Chieh Wu
bioRxiv 2020.11.04.368845; doi: https://doi.org/10.1101/2020.11.04.368845
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A Polynomial-Time Algorithm for Minimizing the Deep Coalescence Cost for Level-1 Species Networks
Matthew LeMay, Ran Libeskind-Hadas, Yi-Chieh Wu
bioRxiv 2020.11.04.368845; doi: https://doi.org/10.1101/2020.11.04.368845

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