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Heterogeneous Compression of Large Collections of Evolutionary Trees.

Suzanne J Matthews

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |September 11, 2015
    PubMed
    Summary

    Compressing heterogeneous collections of trees is now efficient with the extended TreeZip algorithm. This method significantly reduces file sizes for phylogenetic data, enabling faster analysis and archival.

    Area of Science:

    • Computational Phylogenetics
    • Bioinformatics
    • Data Compression

    Background:

    • Compressing heterogeneous collections of trees, where each tree has unique taxa, is a significant challenge.
    • Efficient archival and analysis of large phylogenetic datasets are crucial for evolutionary studies.

    Purpose of the Study:

    • To extend the TreeZip algorithm for effective compression of heterogeneous collections of phylogenetic trees.
    • To enable efficient archival and rapid analysis of large, disparate phylogenetic datasets.

    Main Methods:

    • Extension of the TreeZip algorithm to handle heterogeneous tree collections.
    • Experimental evaluation of compression ratios on unweighted and weighted tree collections.
    • Assessment of computational efficiency for tasks like consensus tree calculation.

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    Main Results:

    • TreeZip achieves average space savings of 89.03% (unweighted) and 72.69% (weighted) for moderately heterogeneous collections.
    • TRZ file organization allows for rapid computation of consensus trees in seconds.
    • Combined TreeZip and general-purpose compression yields up to 97.34% (unweighted) space savings.

    Conclusions:

    • The extended TreeZip algorithm provides an invaluable tool for efficient archival of diverse phylogenetic tree collections.
    • This method facilitates novel analyses by enabling scientists to relate evolutionary relationships across disparate datasets.
    • The compression technique supports faster data processing, accelerating discoveries in computational phylogenetics.