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Related Concept Videos

Peptide Identification Using Tandem Mass Spectrometry01:33

Peptide Identification Using Tandem Mass Spectrometry

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Tandem mass spectrometry, also known as MS/MS or MS2, is an analytical technique that employs two mass analyzers. Essentially it is a series of mass spectrometers that helps isolate a particular biomolecule and then helps study its chemical properties.
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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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FiCOPS: Hardware/Software Co-Design of FPGA Computational Framework for Mass Spectrometry-Based Peptide Database

Sumesh Kumar, Joseph Zambreno, Ashfaq Khokhar

    Biorxiv : the Preprint Server for Biology
    |February 27, 2026
    PubMed
    Summary

    We developed FiCOPS, an FPGA-based framework, to accelerate peptide identification from mass spectrometry data. This hardware/software co-design significantly improves search speed and reduces power consumption for complex proteomic analyses.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • Hardware Acceleration

    Background:

    • Database search algorithms for mass spectrometry (MS) data are crucial for peptide identification but face scalability challenges with large datasets and complex modifications.
    • The growing need for rapid analysis in areas like non-model organisms and metaproteomics demands faster, more efficient computational solutions.
    • Existing serial algorithms struggle with the increasing size of search databases and the complexity of post-translational modifications, hindering real-time applications.

    Purpose of the Study:

    • To develop an efficient system-on-chip solution for accelerating peptide database searches.
    • To address the limitations of current algorithms in terms of speed, scalability, and power consumption.
    • To demonstrate the feasibility of hardware acceleration for real-time, on-the-instrument proteomic data analysis.

    Main Methods:

    • Theoretical analysis of the closed-search algorithm to identify parallelism and computational bottlenecks.
    • Design of a Field-Programmable Gate Array (FPGA)-based architectural template leveraging inherent parallelism.
    • Formulation of an analytical performance model for rapid design space exploration and optimization.
    • Implementation on an Intel Stratix 10 FPGA platform and evaluation using real-world datasets.

    Main Results:

    • The FiCOPS framework achieved a 3.5x speed-up compared to existing Central Processing Unit (CPU) solutions.
    • FiCOPS demonstrated a 3x reduction in power consumption compared to CPU solutions.
    • A 5x reduction in power consumption was observed when compared to Graphics Processing Unit (GPU) solutions.

    Conclusions:

    • FPGA-based hardware acceleration, using a hardware/software co-design methodology, is effective for accelerating peptide database searches.
    • FiCOPS offers a significant improvement in both speed and power efficiency for mass spectrometry data analysis.
    • The developed framework provides a viable solution for real-time, on-the-instrument proteomic data processing.