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    Area of Science:

    • Computational Bioinformatics
    • Genomics
    • Proteomics

    Background:

    • Exponential growth in biological data necessitates accelerated computational solutions.
    • String matching is a fundamental operation in bioinformatics pipelines, requiring efficient algorithms.
    • Dynamic search patterns in biological databases demand reconfigurable and high-speed matching methods.

    Purpose of the Study:

    • To propose a novel hardware-software co-design for accelerated and real-time reconfigurable string matching.
    • To develop a system-on-chip solution utilizing Field Programmable Gate Arrays (FPGAs).
    • To enhance the speed and adaptability of string matching for large-scale biological data analysis.

    Main Methods:

    • Hardware-software co-design methodology for string matching.
    • Implementation of a system-on-chip solution using FPGAs.
    • Verification of the methodology in the protein identification pipeline.

    Main Results:

    • The proposed methodology achieves accelerated and real-time reconfigurable string matching.
    • Achieved 4X speedup over state-of-the-art software implementations.
    • Demonstrated 1.5X-4X speedup compared to existing hardware accelerators.

    Conclusions:

    • The novel FPGA-based methodology offers a significant advancement for high-speed string matching in bioinformatics.
    • The system provides efficient reconfiguration capabilities for time-varying search patterns.
    • This approach is particularly effective for applications like protein identification, addressing the challenges of big biological data.