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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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SCAMPP: Scaling Alignment-Based Phylogenetic Placement to Large Trees.

Eleanor Wedell, Yirong Cai, Tandy Warnow

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |April 26, 2022
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    Summary
    This summary is machine-generated.

    SCAMPP enhances phylogenetic placement accuracy and scalability for large datasets. This method allows maximum likelihood techniques to work efficiently on ultra-large phylogenetic trees with up to 200,000 leaves.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Phylogenetics

    Background:

    • Phylogenetic placement is crucial for large tree construction and taxon identification.
    • Current accurate methods like pplacer and EPA-ng are computationally intensive for large backbone trees (>50,000 leaves).

    Purpose of the Study:

    • To develop a scalable technique for phylogenetic placement on ultra-large datasets.
    • To extend the applicability of maximum likelihood-based placement methods to extremely large phylogenetic trees.

    Main Methods:

    • Introduced SCAMPP (SCaling AlignMent-based Phylogenetic Placement) to enhance scalability.
    • Integrated SCAMPP with existing tools like pplacer and EPA-ng, creating pplacer-SCAMPP and EPA-ng-SCAMPP.

    Main Results:

    • SCAMPP significantly improves the scalability of phylogenetic placement methods.
    • pplacer-SCAMPP and EPA-ng-SCAMPP successfully scale to backbone trees with up to 200,000 leaves.
    • The SCAMPP-enhanced methods demonstrate improved accuracy compared to existing fast methods like APPLES and APPLES-2.

    Conclusions:

    • SCAMPP enables accurate and efficient phylogenetic placement on ultra-large datasets.
    • This technique overcomes computational limitations of traditional methods for massive phylogenetic trees.
    • pplacer-SCAMPP and EPA-ng-SCAMPP represent a significant advancement in scalable phylogenetic placement tools.