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A Practical Guide to Phylogenetics for Nonexperts
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Maximum Covering Subtrees for Phylogenetic Networks.

Nathan Davidov, Amanda Hernandez, Justin Jian

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |November 26, 2020
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    Summary
    This summary is machine-generated.

    Determining how far a phylogenetic network is from being tree-based is now computationally tractable. A new method efficiently finds the largest tree within a phylogenetic network, answering a key evolutionary biology question.

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    Area of Science:

    • Evolutionary Biology
    • Computational Biology
    • Phylogenetics

    Background:

    • Phylogenetic networks model complex evolutionary histories beyond simple tree structures.
    • Tree-based phylogenetic networks possess elegant properties but real-world data often requires more complex network models.
    • Quantifying the 'tree-likeness' of a phylogenetic network is an open problem.

    Purpose of the Study:

    • To address the complexity of determining the distance of a phylogenetic network from being tree-based.
    • To provide an efficient method for finding the maximum number of nodes covered by a tree within a given phylogenetic network.
    • To extend these findings to non-binary phylogenetic networks.

    Main Methods:

    • The study involves encoding the problem of finding a maximum node-covering tree into a minimum-cost flow problem.
    • This computational approach is applicable to both binary and non-binary phylogenetic networks.
    • The method leverages established network flow algorithms for efficient computation.

    Main Results:

    • The complexity of determining how far a phylogenetic network is from being tree-based is resolved.
    • A polynomial-time algorithm is presented for finding a phylogenetic tree that covers the maximum number of nodes in a phylogenetic network.
    • The method is shown to be effective for general phylogenetic networks.

    Conclusions:

    • The distance of a phylogenetic network from being tree-based can be computed efficiently.
    • This provides a practical tool for analyzing evolutionary histories represented by phylogenetic networks.
    • The findings open new avenues for comparing and simplifying complex evolutionary network models.