CRII: SHF: HPC Solutions to Big NGS Data Compression

由于下一代高通量基因组测序 (NGS) 技术的出现，包括人类在内的许多物种的基因组测序变得越来越经济实惠。这为遗传疾病的诊断和治疗开辟了前景，并且在进行系统生物学研究方面日益有效。然而，在这些技术进入日常健康和人类护理之前，仍然存在许多计算挑战需要解决。其中一项艰巨的挑战是用于综合系统生物学研究的测序数据量可达拍字节级别。需要对基因组数据进行压缩以减少存储大小、提高速度并降低传输此类数据所需的 I/O 带宽成本。然而，现有的基因组压缩解决方案对于大基因组数据的性能较差。此外，现有的最先进的工具要求用户在将数据用于进一步分析之前对其进行解压缩。该项目的重点是基因组信息的压缩和开发一个允许分析数据压缩形式的框架。该项目开发 HPC 解决方案，利用 GPU 和多核处理器等普遍存在的架构快速压缩 NGS 大数据集。 HPC 技术用于使用 NGS 数据的压缩形式来计算基本功能，例如对齐和映射。为了更好地利用网络，对 NGS 数据进行更有效的编码也在研究之中。

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.