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.

项目概要 该提案的目标是探索和测试增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结果 母公司的总体目标。 定量生物学中心 (CQB) 拨款：重点关注从有机体到单细胞的组学，旨在建立一个 国家认可的多学科中心，将提高达特茅斯的研究形象和校外 在第一阶段，在单细胞组学领域提供实验和计算方面的资金。 达特茅斯 CQB COBRE 建立了数据分析核心 (DAC) 核心。 成功开发数据科学方法和尖端管道来处理和分析单个数据 细胞和空间转录组数据。这包括最先进的下游分析。 核心面临的运营挑战包括长期数据存储的高成本、巨大的需求 用于处理单细胞和空间数据的计算资源，以及维护最先进技术的高成本 利用云计算和存储可以提高我们的计算能力和 存储，同时提供一个模型，使资源可供可能无法使用的小型机构使用 云上的档案存储成本比本地资源便宜得多。 存储成本和按需访问最新的云计算技术将减少分析师的等待时间 这将缩短向最终用户提供数据的周转时间。该提案的总体目标是 确定云计算满足日益增长的数据管理和分析需求的潜力 具有以下具体目标的生物医学研究人员：1) 在 Google Cloud Platform 学习路径中培训员工。 2) 设计数据缩减工作流程以在 Google Cloud Platform 云计算环境中运行。 评估在 Google Cloud Platform 环境中实施数据分析的成本节省和难易程度。 随着基因组数据变得更加复杂，分析变得更加计算密集型云 计算系统将成为优化数据分析基础设施解决方案的一部分。