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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

基本信息

批准号：
1908495
负责人：
Deliang Fan
金额：
$ 49.13万
依托单位：
The University of Central Florida Board of Trustees
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2019-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1908495&HistoricalAwards=false
关键词：
FET Small AlignMEM Fast Efficient

项目摘要

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生成 TB 级的 DNA 序列数据，并且其吞吐量预计在未来几年每年将增加 3-5 倍。为了将这些大 DNA 数据应用于后续复杂疾病诊断/预后，例如癌症风险评估、定制患者治疗和产前检测，必须首先将它们与 32 亿长度的人类参考基因组进行比对。然而，由于最先进的计算架构中描述内存单元和计算单元之间速度不匹配的“内存墙”挑战，用于此目的的现有软件工具可能需要数小时或数天来比对如此大量的DNA序列数据，即使使用当今非常强大的计算系统。为此，该项目利用基于非易失性纳米磁铁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，下一代DNA序列分析系统的计算性能、速度和能源效率可以提高两个数量级，从而实现大规模快速基因组数据分析，以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架构和生物信息学的新本科/研究生课程模块。该项目将遵循两个主要研究方向。第一个探索如何利用固有的非易失性 MRAM 器件特性来高效开发 DNA 比对计算及其大 DNA 数据内存处理 (PIM) 加速器架构所需的超并行、可重新配置内存逻辑。第二个研究方向将研究如何开发基于 Burrows-Wheeler 变换的快速 DNA 内存比对算法，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，并识别患者样本的单核苷酸突变事件，从而产生用于疾病风险评估的分子特征。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。