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

基本信息

批准号：
2204657
负责人：
Bingzhe Li
金额：
$ 30万
依托单位：
Oklahoma State University
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 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204657&HistoricalAwards=false
关键词：
Collaborative Research CNS Core Small

项目摘要

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.

世界数字数据每年都在大幅增加。到 2025 年，它将达到 175 泽字节 (ZB)。如今，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保存很长时间。因此，目前的存储技术和设备无法长期（超过 15 年）保存有价值的数据。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变得过时，因此存储的数据很容易受到攻击，因为它们会随着时间的推移而消失。因此，合成脱氧核糖核酸（DNA）由于其高密度和长耐用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选择。但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250GB左右，远低于预期容量。主要原因是，随着数据有效负载大小的增加，DNA 存储中的引物有效负载碰撞会大大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。在该项目中，成立了一个跨学科团队来研究可以提高 DNA 存储可扩展性的生物和存储方法。在能够扩大DNA存储规模的众多因素中，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引物？ 2）给定一个引物库，如何有效地分配有效负载数据以避免引物与有效负载碰撞，从而增加DNA存储容量？ 3）如何在数据备份应用中有效利用流行的重复数据删除技术来进一步提高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入了解，这个跨学科团队培育了几种理解 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、软件和新算法来优化转换过程将世界各地的数字数据存储到 DNA 存储中，以便在未来数百年中归档和保存当今有价值的数字数据。通过该项目，将世界数字数据存储在 DNA 存储中以保存所有人类活动的目标又向前迈进了一步。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、在 DNA 存储中保存人类活动数百年、促进基本理解、确定权衡以及创建扩大 DNA 存储规模的有效方法。该项目将提供理想的跨学科思维、实践学习和开发环境，教授计算机科学、电气和计算机工程研究生和本科生重要的系统构建和实验技能，这些技能对当今和未来的 IT 劳动力至关重要。该项目的研究成果将纳入团队成员的课堂教学，包括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项目学生的大数据存储技术/系统新课程以及本科生高级设计和定向研究中。该团队计划向工业合作者传播研究进展，并通过出版物、演示和向研究界公开发布研究数据、软件工具和原型系统。该团队致力于招募代表性不足的本科生和研究生参与该项目。研究结果将快速向公众提供，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。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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：
2022-10
期刊：
and Synthesis for Embedded Systems
影响因子：
0
作者：
Yang, Jinfeng;Li, Bingzhe;Yuan, Jianjun;Shen, Zhaoyan;Du, David;Lilja, David
通讯作者：
Lilja, David

Machine Learning-based Adaptive Migration Algorithm for Hybrid Storage Systems

基于机器学习的混合存储系统自适应迁移算法