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    This study enhances the kdetrees algorithm for analyzing gene evolution, improving the identification of horizontal gene transfers and unreliable sequence alignments in phylogenomic studies.

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    Area of Science:

    • Bioinformatics and Computational Biology
    • Evolutionary Biology
    • Genomics

    Background:

    • Genome sequencing costs have decreased, increasing the need for efficient bioinformatic methods to analyze genome structure and evolution.
    • Current phylogenomic studies often use sequence concatenation or informal gene tree comparisons, limiting comprehensive evolutionary analysis and identification of genes with unusual evolutionary histories.

    Purpose of the Study:

    • To improve the kdetrees algorithm for identifying "outlying" gene trees and estimating their distribution in tree space.
    • To systematically identify genes with unusual evolutionary events such as horizontal gene transfers and gene duplications.

    Main Methods:

    • Adapted kernel density estimation (kdetrees) to the metric space of phylogenetic trees (Billera-Holmes-Vogtman treespace).
    • Estimated kernel normalizing constants using novel holonomic gradient methods.
    • Applied the updated kdetrees algorithm to analyze a set of Apicomplexa genes.

    Main Results:

    • Identified several unreliable sequence alignments missed by previous analyses.
    • Detected a gene consistent with a previously reported case of horizontal gene transfer.
    • The enhanced kdetrees algorithm provides a more comprehensive overview of gene evolution.

    Conclusions:

    • The improved kdetrees algorithm offers a robust method for phylogenomic analysis.
    • This approach facilitates the systematic discovery of genes with complex evolutionary trajectories.
    • The updated kdetrees software is publicly available for use in bioinformatics research.