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 该建议的目的是探索和测试机会，以增强数据分析和报告 通过导入云计算和存储功能来为用户提供结果。父母的总体目标 定量生物学中心（CQB）Grant：从生物到单细胞的关注 - 元素是建立一个 全国认可的多学科中心，将增强达特茅斯的研究概况和外壁外 在实验和计算上，在单细胞'Omics领域的资金。在第1阶段， 达特茅斯CQB Cobre已建立了数据分析核心（DAC）。核心一直非常 成功开发数据科学方法和尖端管道来处理和分析单个 细胞和空间转录组数据。这包括下游分析的最新状态。 核心面临的运营挑战包括长期数据存储的高成本，大量需求 用于计算资源处理单元格和空间数据，以及保持最新状态的高成本 计算基础架构。利用云计算和存储可能会增加我们的计算能力，并且 存储同时提供模型，以使资源可用于可能无法 投资当地资源。云上的档案存储成本明显低于本地 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 这将改善数据的周转时间到最终用户。该提议的总体目标是 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 具有以下特定目的的生物医学研究人员：1）在Google Cloud Platform学习路径中培训员工。 2）工程师数据减少工作流程以在Google Cloud Platform Cloud Compute环境中运行。 3） 评估节省成本和在Google Cloud平台环境中实施数据分析的便利性。 随着基因组数据变得越来越复杂，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架构的解决方案的一部分。