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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Bridging Between Deviation Indices for Non-Tree-Based Phylogenetic Networks.

Takatora Suzuki, Han Guo, Momoko Hayamizu

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 9, 2024
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    Summary
    This summary is machine-generated.

    Phylogenetic networks model complex evolutionary histories. This study establishes a relationship between two measures of how tree-like a network is, providing a new algorithm for the Maximum Covering Subtree Problem.

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    Area of Science:

    • Computational Biology
    • Evolutionary Biology
    • Discrete Mathematics

    Background:

    • Phylogenetic networks model reticulate evolution and complex biological data.
    • Mathematical and computational aspects of tree-based networks are well-studied.
    • Deviation indices measure how far a network deviates from being tree-based.

    Purpose of the Study:

    • To derive a tight inequality for two deviation indices of phylogenetic networks.
    • To characterize phylogenetic networks where these two deviation indices coincide.
    • To develop an efficient algorithm for the Maximum Covering Subtree Problem.

    Main Methods:

    • Derivation of a tight inequality relating two deviation measures.
    • Characterization of phylogenetic networks based on coinciding deviation indices.
    • Development of a new algorithm using maximal zig-zag trail decomposition.

    Main Results:

    • A tight inequality is established for the two deviation measures.
    • A characterization of phylogenetic networks where the measures coincide is provided.
    • An efficient algorithm for the Maximum Covering Subtree Problem is presented.

    Conclusions:

    • The relationship between deviation indices provides insights into network structure.
    • The new algorithm offers an efficient solution for the Maximum Covering Subtree Problem.
    • This work advances the understanding and computational analysis of phylogenetic networks.