Collaborative Research: CNS Core: Small: Efficient Ways to Enlarge Practical DNA Storage Capacity by Integrating Bio-Computer Technologies

合作研究：中枢神经系统核心：小型：通过集成生物计算机技术扩大实用 DNA 存储容量的有效方法

• 批准号: 2204656
• 负责人: David Du
• 金额: $ 30万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-15 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204656&HistoricalAwards=false
• 关键词: Collaborative; Research; CNS; Core; Small

The world's digital data increases immensely each year. By 2025, it will reach 175 Zettabytes (ZB). Most human activities are recorded in digital format today. However, data recorded in digital media cannot last very long. Therefore, valuable data cannot be preserved today with our current storage technologies and devices for a long duration (beyond 15 years). The capacity of existing storage media cannot keep up with the growth of the amount of digital data. Also, all storage devices could become obsolete within several years, so the data stored are vulnerable as they perish as time goes by. Therefore, synthetic deoxyribonucleic acid (DNA) becomes an attractive alternative storage medium due to its high density and long durability. These characteristics of DNA storage make it a great candidate for archival storage. However, the preliminary study of the project indicates the practical DNA storage tube capacity based on current technologies is only around 250GB, which is much less than the expected capacity. The major reason is that primer-payload collisions in DNA storage can drastically reduce the number of usable primers in a tube as the data payload size increases. The use of primers is essential for random access to DNA data. In this project, an interdisciplinary team is formed to investigate both bio and storage approaches that can improve the scalability of DNA storage. Among the many factors that can scale up DNA storage, the project plans to investigate the following questions: 1) How to identify more primers for a primer library to be used in DNA storage? 2) Given a primer library, how to efficiently allocate payload data to avoid primer-payload collisions to increase DNA storage capacity? and 3) How to effectively use a popular technique called data deduplication in data backup applications to further increase the storage capability of DNA storage? With a deep understanding of molecular biology and computer storage technologies and systems, this interdisciplinary team fosters several innovative ways of understanding the fundamental issues of DNA storage and will develop necessary genome engineering, sequencing techniques, software, and new algorithms to optimize the process of converting the world's digital data to DNA storage for archiving and preserving today's valuable digital data for hundreds of years in the future. The goal of storing the world's digital data in DNA storage to preserve all human activities can move one step closer with this project. The potential research outcomes of the project include fostering the advancement of bioscience and storage technologies, preserving human activities in DNA storage for hundreds of years, and facilitating fundamental understanding, identifying tradeoffs, and creating efficient ways of scaling up DNA storage. The project will provide an ideal inter-disciplinary thinking, hands-on learning, and development environment to teach computer science and electrical and computer engineering graduate and undergraduate students important system building and experimental skills that are critical for today's and the future IT workforce. The research outcomes of the project will be incorporated into the classroom teaching of the team members, for both class projects and the core courses in computer science and electrical and computer engineering. The team plans to include the obtained research results in a new course on Storage Technologies /Systems for Big Data for students in a Data Science Program, as well as in undergraduate senior design and directed research studies. The team plans to disseminate the research advances to industrial collaborators, and through publications, presentations, and public release of research data, software tools, and prototype systems to the research community. The team is committed to recruiting underrepresented undergraduate and graduate students to the project. Research results will be made quickly available to the general public and disseminated via websites and open source repositories like GitHub.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.

世界上的数字数据每年都在大幅增加。到2025年，它将达到175 Zettabytes（ZB）。今天，大多数人类活动都以数字格式记录。然而，记录在数字媒体中的数据不能持续很长时间。因此，使用我们目前的存储技术和设备，无法长期保存有价值的数据（超过15年）。现有存储介质的容量无法跟上数字数据量的增长。此外，所有存储设备都可能在几年内过时，因此存储的数据随着时间的推移而变得脆弱。因此，合成的脱氧核糖核酸（DNA）由于其高密度和长耐久性而成为一种有吸引力的替代存储介质。DNA存储的这些特性使其成为档案存储的绝佳候选者。然而，该项目的初步研究表明，基于现有技术的实际DNA存储管容量仅为250 GB左右，远远低于预期容量。主要原因是，随着数据有效载荷大小的增加，DNA存储中的引物-有效载荷碰撞会大幅减少试管中可用引物的数量。引物的使用对于随机获取DNA数据至关重要。在这个项目中，成立了一个跨学科的团队来研究可以提高DNA存储可扩展性的生物和存储方法。在可以扩大DNA存储的许多因素中，该项目计划研究以下问题：1）如何为用于DNA存储的引物库识别更多的引物？2)给定一个引物库，如何有效地分配有效载荷数据以避免引物-有效载荷碰撞，从而增加DNA存储容量？ 3）如何在数据备份应用中有效地使用一种流行的称为数据去重的技术，以进一步提高DNA存储的存储能力？ 凭借对分子生物学和计算机存储技术和系统的深刻理解，这个跨学科的团队培养了几种理解DNA存储基本问题的创新方法，并将开发必要的基因组工程，测序技术，软件，以及新的算法，以优化将世界数字数据转换为DNA存储的过程，以便将当今有价值的数字数据存档和保存数百年在未来通过这个项目，将世界上的数字数据存储在DNA存储中以保存所有人类活动的目标可以更近一步。该项目的潜在研究成果包括促进生物科学和存储技术的进步，将人类活动保存在DNA存储中数百年，促进基本理解，确定权衡，并创造扩大DNA存储的有效方法。该项目将提供一个理想的跨学科思维，动手学习和开发环境，教计算机科学和电气和计算机工程研究生和本科生重要的系统建设和实验技能，是至关重要的今天和未来的IT劳动力。该项目的研究成果将被纳入团队成员的课堂教学中，用于课堂项目以及计算机科学和电气与计算机工程的核心课程。该团队计划将获得的研究成果纳入数据科学课程学生的大数据存储技术/系统新课程，以及本科高级设计和指导研究。该团队计划将研究进展传播给工业合作者，并通过出版物，演示文稿和研究数据，软件工具和原型系统的公开发布到研究社区。该团队致力于招募代表性不足的本科生和研究生参加该项目。研究成果将迅速提供给公众，并通过网站和GitHub等开源存储库传播。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

DP-DNA: A Digital Pattern-Aware DNA Encoding Scheme to Improve Encoding Density of DNA Storage

DP-DNA：一种提高 DNA 存储编码密度的数字模式感知 DNA 编码方案

• DOI: 10.1109/mascots59514.2023.10387655
• 发表时间: 2023
• 期刊: IEEE
• 作者: Li, Bingzhe;Ou, Li;Yuan, Bo;Du, David H.C.
• 通讯作者: Du, David H.C.

Work-in-Progress: ExpCache: Online-Learning based Cache Replacement Policy for Non-Volatile Memory

正在进行中的工作：ExpCache：基于在线学习的非易失性内存缓存替换策略

• DOI: 10.1109/cases55004.2022.00010
• 发表时间: 2022
• 期刊: and Synthesis for Embedded Systems
• 作者: Yang, Jinfeng;Li, Bingzhe;Yuan, Jianjun;Shen, Zhaoyan;Du, David;Lilja, David
• 通讯作者: Lilja, David

SMRTS: A Performance and Cost-Effectiveness Optimized SSD-SMR Tiered File System with Data Deduplication

• DOI: 10.1109/iccd58817.2023.00050
• 发表时间: 2023-11
• 期刊: 2023 IEEE 41st International Conference on Computer Design (ICCD)
• 作者: Zhichao Cao;Hao Wen;Fenggang Wu;David Hung-Chang Du

Machine Learning-based Adaptive Migration Algorithm for Hybrid Storage Systems

• DOI: 10.1109/nas55553.2022.9925545
• 发表时间: 2022-10
• 期刊: 2022 IEEE International Conference on Networking, Architecture and Storage (NAS)
• 作者: Milan M. Shetti;Bingzhe Li;D. Du

HL-DNA: A Hybrid Lossy/Lossless Encoding Scheme to Enhance DNA Storage Density and Robustness for Images

HL-DNA：一种增强图像 DNA 存储密度和鲁棒性的有损/无损混合编码方案

• DOI: 10.1109/iccd56317.2022.00071
• 发表时间: 2022
• 期刊: IEEE International Conference on Computer Design
• 作者: Li, Yi;Du, David H.C.;Ou, Li;Li, Bingzhe
• 通讯作者: Li, Bingzhe