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    RabbitFX accelerates bioinformatics by efficiently parsing sequencing data on multi-core systems. This framework significantly reduces runtimes for I/O-intensive next-generation sequencing (NGS) analysis tools.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • The exponential growth of sequencing data necessitates efficient bioinformatics tools.
    • Many existing tools are limited by slow file parsing, failing to leverage modern multi-core hardware.
    • This bottleneck hinders the performance of next-generation sequencing (NGS) data analysis.

    Purpose of the Study:

    • To develop an efficient file parsing framework for biological sequencing data.
    • To design a method that exploits modern multi-core processors and fast storage.
    • To provide a user-friendly and modular C++ API for integrating into existing applications.

    Main Methods:

    • Developed RabbitFX, a lightweight parsing framework with optimized formatting.
    • Implemented user-friendly, modular C++ APIs for easy integration.
    • Integrated RabbitFX into three I/O-intensive applications: fastp, Ktrim, and Mash.

    Main Results:

    • RabbitFX demonstrated significant speedups in file parsing for FASTA and FASTQ formats.
    • Speedups of 11.6x (plain) and 6.6x (gzip) were observed for fastp.
    • Ktrim and Mash showed speedups of 2.4x and 3.7x, respectively, highlighting RabbitFX's effectiveness.

    Conclusions:

    • RabbitFX offers a fast, efficient, and easy-to-use solution for processing biological sequencing data.
    • The framework can be readily integrated into various NGS analysis tools to substantially decrease execution times.
    • RabbitFX empowers researchers to better utilize computational resources for large-scale genomic analyses.