CRII: SHF: HPC Solutions to Big NGS Data Compression

CRII：SHF：NGS 大数据压缩的 HPC 解决方案

• 批准号: 1855441
• 负责人: Fahad Saeed
• 金额: $ 0.77万
• 依托单位: Florida International University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1855441&HistoricalAwards=false
• 关键词: CRII; SHF; HPC; Solutions; Big

Sequencing of genomes for numerous species including humans has become increasingly affordable due to next generation high-throughput genome sequencing (NGS) technologies. This opens up perspectives for diagnosis and treatment of genetic diseases and is increasingly effective in conducting system biology studies. However, there remain many computational challenges that need to be addressed before these technologies find their way into every day health and human care. One such daunting challenge is the volume of sequencing data which can reach peta-byte level for comprehensive system-biology studies. Genomic data compression is needed to reduce the storage size, to increase the speed and reduce the cost of I/O bandwidth required for transmission of such data. However, existing genomic compression solutions yield poor performance for Big Genomic Data. Further, the existing state of the art tools require the user to decompress the data before it can be used for further analysis. This project is focused on compression of genomic information and developing a framework which will allow analysis of compressed form of the data. The project develops HPC solutions for fast compression of Big NGS Data sets using ubiquitous architectures such as GPUs and multicore processors. HPC techniques are utilized to compute essential functions such as alignment and mapping using the compressed form of the NGS data. More efficient encoding of the NGS data for better network utilization is also being investigated.

由于下一代高通量基因组测序（NGS）技术，包括人类在内的许多物种的基因组测序已经变得越来越负担得起。这为遗传疾病的诊断和治疗开辟了前景，并在进行系统生物学研究方面越来越有效。然而，在这些技术进入日常健康和人类护理之前，仍然存在许多需要解决的计算挑战。其中一个令人生畏的挑战是测序数据的数量，对于全面的系统生物学研究来说，这些数据可以达到千万亿字节的水平。需要基因组数据压缩来减小存储大小，以提高速度并降低传输此类数据所需的I/O带宽的成本。然而，现有的基因组压缩解决方案对大基因组数据的性能较差。此外，现有的最新技术工具要求用户在数据可以用于进一步分析之前对其进行加密。该项目的重点是基因组信息的压缩和开发一个框架，这将允许分析压缩形式的数据。该项目开发HPC解决方案，用于使用GPU和多核处理器等无处不在的架构快速压缩大NGS数据集。HPC技术被用来计算基本功能，如使用NGS数据的压缩形式的对齐和映射。还在研究对NGS数据进行更有效的编码以提高网络利用率。