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TB级的DNA序列数据，预计未来几年其吞吐量将以每年3-5倍的速度增长。为了将这些大数据应用于后续的复杂疾病诊断/预后，如癌症风险评估、量身定制的患者治疗和产前测试，它们必须首先与32亿长度的人类参考基因组进行比对。然而，现有的用于此目的的软件工具可能需要数小时或数天来将如此大量的DNA序列数据与当今非常强大的计算系统进行比对，这是因为在描述存储单元和计算单元之间的速度不匹配的最先进的计算体系结构中存在存储墙的挑战。为此，该项目利用了基于非易失性纳米磁体的磁随机存取存储器(MRAM)技术和内存计算架构的创新。如果成功，它可以为下一代DNA序列分析系统实现高达两个数量级的计算性能、速度和能效，从而使大规模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。该项目将开发新的本科生/研究生水平的内存计算架构和生物信息学课程模块。这个项目将遵循两条主要的研究路线。第一部分探讨如何利用MRAM器件固有的非易失性特性，高效地开发DNA比对计算所需的超并行、可重新配置的内存中逻辑及其大型DNA数据内存中处理(PIM)加速器架构。第二个研究轨道将研究如何开发基于Burrow-Wheeler变换的快速DNA内存中比对算法，以匹配所提出的基于MRAM的PIM平台及其大规模基因组分析在疾病表型预测中的应用。产生的比对将用于估计基因表达，并识别患者样本的单核苷酸突变事件，导致用于疾病风险评估的分子签名。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。