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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Genomics

    Background:

    • Next-generation sequencing (NGS) generates vast amounts of data requiring efficient analysis pipelines.
    • Current bioinformatics tools face challenges in processing speed, precision, and energy consumption for complex genomic analyses.
    • End-to-end analysis of short-read data, including mapping, variant calling, and genotyping, is computationally intensive.

    Purpose of the Study:

    • To develop and present the first end-to-end accelerator for next-generation sequencing (NGS) data analysis.
    • To enhance the efficiency and accuracy of short-read mapping, haplotype calling, variant calling, and genotyping.
    • To provide a hardware acceleration solution that outperforms existing software and hardware approaches.

    Main Methods:

    • Utilized FM-index for exact-match short-read mapping and dynamic programming for inexact matching.
    • Implemented a rapid similarity calculation and a rescue technique to optimize mapping workload and sensitivity.
    • Developed parallel k-mer processing for de Bruijn graph construction and haplotype assembly.
    • Integrated variant discovery and genotype likelihood computing engines for comprehensive variant analysis.

    Main Results:

    • Achieved end-to-end data analysis for the 50× PrecisionFDA dataset in an average of 28.2 minutes.
    • Demonstrated a 3-to-59× higher throughput compared to existing solutions.
    • Attained high precision (99.79%) and sensitivity (99.03%) in variant calling and genotyping.
    • Reported a 935× higher energy efficiency than the Illumina DRAGEN FPGA acceleration system.

    Conclusions:

    • The developed accelerator provides a significant advancement in NGS data analysis efficiency and performance.
    • This hardware solution offers a substantial improvement in throughput, precision, and energy efficiency for critical genomic tasks.
    • The end-to-end approach streamlines the complex bioinformatics workflow, enabling faster and more accurate genomic insights.