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    Modern compressors significantly outperform gzip for compressing biological sequence data, offering better compactness and speed. This benchmark helps select optimal tools for specific data types and applications.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Data Compression

    Background:

    • Current molecular sequence databases predominantly use gzip for data compression.
    • Rapid data growth necessitates more efficient compression methods.
    • Lack of comparative analysis hindered selection of optimal sequence compression tools.

    Purpose of the Study:

    • To systematically evaluate and compare the performance of various data compressors for biological sequences.
    • To provide a comprehensive resource for selecting efficient compression tools based on specific needs.

    Main Methods:

    • Benchmarked 48 compressors across 430 settings on diverse FASTA-formatted DNA, RNA, and protein sequence datasets.
    • Evaluated 17 performance metrics including compression ratio, compression/decompression speed, and memory usage.
    • Created the Sequence Compression Benchmark (SCB) database for result visualization.

    Main Results:

    • Modern compressors demonstrate substantial improvements in compression efficiency and speed over gzip.
    • Specialized sequence compressors and advanced general-purpose compressors showed superior performance.
    • The SCB database offers detailed insights into compressor performance across various scenarios.

    Conclusions:

    • Modern compression tools offer significant advantages over gzip for biological sequence data.
    • The benchmark facilitates informed selection of the best compressor based on data type and application requirements.
    • Optimized compression strategies are crucial for managing the growing volume of biological data.