NOSI cloud computing Supplement

项目总结 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将结果提供给用户。父母的首要目标 格兰特，数量生物学中心(CQB)：从生物到单细胞的组学研究的重点是建立一个 国家认可的多学科中心，将提升达特茅斯大学的研究形象和校外影响力 在单细胞组学领域的资金，无论是在实验上还是在计算上。在第一阶段， 达特茅斯CQB科布雷已经建立了数据分析核心(DAC)。它的核心已经非常 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 用于处理单个像元和空间数据的计算资源，以及维护最先进技术的高成本 计算基础设施。利用云计算和存储可以增加我们的计算能力并 存储，同时提供一种模式，使资源可供可能无法使用的较小机构使用 投资当地的资源。云上的归档存储成本比本地低得多 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 这将缩短数据到达最终用户的周转时间。这项提议的首要目标是 确定云计算的潜力，以满足不断增长的数据管理和分析需求 生物医学研究人员的具体目标如下：1)培训谷歌云平台学习路径中的工作人员。 2)设计在Google Cloud Platform云计算环境下运行的数据缩减工作流。3) 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的云 计算机系统将是优化数据分析基础设施的解决方案的一部分。

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.