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Improvement to GLASS/Maximum Tree Method of Species Tree Inference Using Measurement Error Modified Single Linkage

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    This summary is machine-generated.

    A new method, genX, improves species tree inference by adjusting for gene tree estimation errors. This approach enhances accuracy compared to the standard STEM software, particularly when gene trees are derived from DNA sequences.

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    Area of Science:

    • Phylogenetics
    • Computational Biology
    • Evolutionary Genetics

    Background:

    • The Global LAteSt Split (GLASS)/Maximum Tree (MT) method (STEM) is effective for species tree inference from known gene trees.
    • STEM can perform poorly when gene trees are estimated from DNA sequences due to gene tree estimation error.
    • Statistical consistency conditions for STEM may be unrealistic with estimated gene trees.

    Purpose of the Study:

    • To develop and evaluate a modified STEM method (genX) that accounts for gene tree estimation error.
    • To improve the accuracy of species tree inference when using DNA sequence data.
    • To assess the performance of genX against the original STEM method.

    Main Methods:

    • genX replaces STEM's estimated coalescence times with random realizations from an estimated distribution of true coalescence times.
    • This adjustment addresses measurement error, using methods described by Su, Reedy, and Carroll (2018) or a Bayesian approach.
    • Species trees are inferred using single linkage clustering on a distance matrix derived from minimum realizations across loci.

    Main Results:

    • genX outperformed STEM in Robinson-Foulds distance from the true species tree in simulations and empirical datasets.
    • The Bayesian approach within genX showed improved accuracy but incurred higher computation time and convergence issues.
    • Addressing gene tree estimation error demonstrably improved species tree inference accuracy in simulations.

    Conclusions:

    • The genX method offers a significant improvement over STEM for species tree inference, especially with DNA sequence data.
    • Adjusting for gene tree estimation error is crucial for robust phylogenetic analysis.
    • While effective, the Bayesian implementation of genX requires further optimization to address computational demands and convergence.