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A 135-mW Fully Integrated Data Processor for Next-Generation Sequencing.

Yi-Chung Wu, Chia-Hua Chang, Jui-Hung Hung

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    This study introduces the first integrated hardware processor for the entire DNA mapping process in next-generation sequencing (NGS). The novel design significantly accelerates DNA mapping, improving energy efficiency for faster genomic data analysis.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computer Engineering

    Background:

    • Next-generation sequencing (NGS) generates vast amounts of data, requiring efficient analysis.
    • DNA mapping, a crucial step in NGS data analysis, is computationally intensive and time-consuming.
    • Existing hardware solutions focus on specific parts of DNA mapping, neglecting the complex suffix array sorting.

    Purpose of the Study:

    • To develop the first integrated hardware processor for the complete DNA mapping algorithm in next-generation sequencing.
    • To address the computational bottleneck in DNA mapping, specifically the suffix array sorting component.
    • To improve the energy efficiency and speed of genomic data analysis.

    Main Methods:

    • Leveraged the memory-efficient compressed Burrows-Wheeler Transform (sBWT) algorithm.
    • Utilized the -ordered Ferragina and Manzini index to enhance storage and reduce hardware complexity.
    • Implemented the sBWT algorithm using bucket sorting, suffix grouping, and suffix sorting circuits on a 40-nm CMOS chip.

    Main Results:

    • The developed NGS data processor analyzes human DNA within 10 minutes using 1-GB external memory.
    • Achieved significant improvements in energy efficiency (throughput-to-area ratio) compared to high-end CPU and GPU solutions.
    • The processor dissipates 135 mW at 200 MHz from a 0.9-V supply.

    Conclusions:

    • This work presents a novel, integrated hardware solution for accelerating the entire DNA mapping process in NGS.
    • The proposed processor offers superior energy efficiency and speed for genomic data analysis.
    • This advancement has the potential to significantly impact the field of high-throughput sequencing and bioinformatics.