FET: Small: AlignMEM: Fast and Efficient DNA Sequence Alignment in Non-Volatile Magnetic RAM

FET：小型：AlignMEM：非易失性磁性 RAM 中快速高效的 DNA 序列比对

• 批准号: 2349802
• 负责人: Deliang Fan
• 金额: $ 49.13万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2349802&HistoricalAwards=false
• 关键词: FET; Small; AlignMEM; Fast; Efficient

The state-of-the-art DNA sequencing technologies could generate Terabytes of DNA sequence data in a single run, and their throughput is expected to increase 3-5 times each year in the coming years. In order to apply these big DNA-data into follow-up complex disease diagnostics/prognostics, such as cancer risk assessment, tailor patient treatment, and prenatal testing, they must be first aligned to a 3.2-billion-length human reference genome. However, the existing software tools for this purpose may need hours or days to align such large amount of DNA sequence data even with very powerful computing systems of today due to the 'memory wall' challenge in state-of-the-art computing architecture that describes the speed mismatch between memory units and computing units. To this end this, project leverages innovations from non-volatile nano-magnet based Magnetic Random Access Memory (MRAM) technology and in-memory computing architecture. If successful, it can achieve up to two orders magnitude higher computing performance, speed and energy efficiency for next-generation DNA sequence analysis system, which enables large-scale fast genomic data analytics to support research on various disease studies and biomedical applications. This project will develop new undergraduate/graduate level course modules on in-memory computing architecture and bioinformatics. This project will follow two main research tracks. The first one explores how to leverage the intrinsic non-volatile MRAM device property to efficiently develop ultra-parallel, reconfigurable in-memory logic required by DNA alignment computation and its big DNA-data Processing-in-Memory (PIM) accelerator architecture. The second research track will investigate how to develop fast DNA alignment-in-memory algorithm based on Burrows-Wheeler Transformation to match with the proposed MRAM based PIM platform and its large-scale genomic analysis application in disease phenotype prediction. Alignments generated will be used to estimate gene expression, and identify single nucleotide mutation events for patient samples, leading to molecular signatures for disease risk assessment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

最先进的DNA测序技术可以在一次运行中产生DNA序列数据的TB，并且预计未来几年每年的吞吐量将增加3-5次。为了将这些大型DNA数据应用于随访复杂疾病诊断/预后，例如癌症风险评估，裁缝患者治疗和产前测试，必须首先将它们与32亿的人类参考基因组保持一致。但是，现有的用于此目的的软件工具可能需要数小时或几天才能使如此大量的DNA序列数据对齐，即使在当今的非常强大的计算系统中，由于最先进的计算体系结构中的“内存墙”挑战，它描述了记忆单元和计算单元之间的速度不匹配。为此，项目利用基于非挥发性纳米磁力的磁随机访问记忆（MRAM）技术和内存计算体系结构的创新。如果成功，它可以达到下一代DNA序列分析系统的最多两个级级，速度和能源效率，这使大规模的快速基因组数据分析能够支持各种疾病研究和生物医学应用的研究。该项目将开发有关内存计算体系结构和生物信息学的新的本科/研究生级课程模块。 该项目将遵循两个主要的研究轨道。第一个探讨了如何利用固有的非挥发性MRAM设备属性，以有效地开发出DNA对齐计算及其大型DNA-DATA在内存（PIM）加速器架构所需的超平行，可重新配置的内存内存逻辑。第二个研究轨道将研究如何基于洞穴 - 轮毂转化开发快速的内存算法，以与所提出的基于MRAM的PIM平台及其在疾病表型预测中的大规模基因组分析应用相匹配。生成的比对将用于估计基因表达，并确定患者样本的单核苷酸突变事件，从而导致疾病风险评估的分子特征。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的审查标准来通过评估来通过评估来支持的。