NOSI cloud computing Supplement

NOSI云计算补充

• 批准号: 10827318
• 负责人: MICHAEL L WHITFIELD
• 金额: $ 24.53万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10827318
• 关键词: Adoption; Area; Awareness; Biology; Biomedical Research; Cells; Centers of Research Excellence; Cloud Computing; Communities; Complex; Computer Systems; Consultations; Core Facility; Cost Analysis; Cost Savings; Data; Data Analyses; Data Analytics; Data Engineering; Data Science; Data Set; Data Storage and Retrieval; Development; Documentation; Ecosystem; Engineering; Environment; Extramural Activities; Feedback; Funding; Goals; Infrastructure; Institution; Investments; Learning; Libraries; Link; Modeling; Occupations; Organism; Phase; Positioning Attribute; Process; Reporting; Research; Research Personnel; Resources; Running; Services; Site; Surveys; Taxes; Technology; Testing; Training; United States National Institutes of Health; Wait Time; archived data; cloud platform; cloud storage; computational platform; computing resources; cost; cost comparison; data access; data complexity; data ecosystem; data management; data reduction; data storage site; experience; genomic data; high dimensionality; improved; innovation; multidisciplinary; parent grant; response; satisfaction; summer internship; transcriptome sequencing; transcriptomics

Project summary The goal of this proposal is to explore and test opportunities to enhance data analytics and the reporting of results to users by incorporating cloud computing and storage capabilities. The overarching goal of the parent grant, Center for Quantitative Biology (CQB): A focus on -omics, from organisms to single cells is to establish a nationally recognized, multidisciplinary center that will enhance Dartmouth’s research profile and extramural funding in the area of single cell ‘omics, both experimentally and computationally. During Phase 1, the Dartmouth CQB COBRE has established the Data Analytics Core (DAC). The core has been extremely successful in developing data science approaches and cutting edge pipelines to process and analyze single cell and spatial transcriptomic data. This includes state of the art downstream analyses. Operational challenges faced by the core include the high cost of long-term data storage, significant demand for computing resources to process single cell and spatial data, and the high cost of maintaining state of the art compute infrastructure. Leveraging cloud computing and storage could increase our computing capacity and storage while providing a model to make resources available to smaller institutions that may not be able to invest in local resources. Archival storage costs on the cloud are significantly less expensive than local storage costs and on-demand access to the latest cloud compute technology would reduce analyst wait times which would improve turnaround times of data to end users. The overarching goal of this proposal is to determine the potential of cloud computing to meet the increasing data management and analysis needs of biomedical researchers with the following specific aims: 1) Train staff in Google Cloud Platform learning paths. 2) Engineer data reduction workflows to operate in the Google Cloud Platform cloud compute environment. 3) Assess the cost savings and ease of implementing data analyses in the Google Cloud Platform environment. As genomic data becomes more complex, and analysis becomes more computationally intensive cloud compute systems will be part of the solution to optimize data analysis infrastructure.

项目摘要 本提案的目标是探索和测试加强数据分析和报告的机会， 通过整合云计算和存储功能，为用户提供最佳的结果。家长的首要目标 格兰特，定量生物学中心（CQB）：从生物体到单细胞的组学重点是建立一个 一个全国公认的多学科中心，将提高达特茅斯的研究概况和校外 单细胞组学领域的资金，包括实验和计算。在第一阶段， 达特茅斯CQB COBRE建立了数据分析核心（DAC）。核心已经非常 成功开发数据科学方法和尖端管道，以处理和分析单个 细胞和空间转录组学数据。这包括最先进的下游分析。 核心所面临的运营挑战包括长期数据存储的高成本， 用于处理单个像元和空间数据的计算资源，以及维护最新技术的高成本 计算基础设施。利用云计算和存储可以提高我们的计算能力， 存储，同时提供一种模型，使资源可用于可能无法 投资当地资源。云上的归档存储成本明显低于本地 存储成本和对最新云计算技术的按需访问将减少分析师的等待时间 这将改善数据到最终用户的周转时间。本提案的总体目标是 确定云计算的潜力，以满足日益增长的数据管理和分析需求， 生物医学研究人员，具体目标如下：1）培训员工了解Google Cloud Platform学习路径。 2)设计数据精简工作流程，以便在Google Cloud Platform云计算环境中运行。第三章 评估在Google Cloud Platform环境中实施数据分析的成本节约和易用性。 随着基因组数据变得更加复杂，分析变得更加计算密集型， 计算系统将成为优化数据分析基础设施的解决方案的一部分。