Cloud-Based Biomedical Data Storage and Analysis: Implications for Trustworthy Governance

基于云的生物医学数据存储和分析：对可信治理的影响

• 批准号: 10320454
• 负责人: Stephanie Malia Fullerton
• 金额: $ 19.44万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-18 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10320454
• 关键词: All of Us Research Program; Biomedical Research; Clinical; Clinical Data; Collaborations; Communities; Cost utility; Data; Data Analyses; Data Storage and Retrieval; Data Store; Decision Making; Development; Ensure; Equilibrium; Feedback; Genomic Data Commons; Genomic medicine; Genomics; Goals; Health; Human; Individual; Informatics; Informed Consent; Interview; Investigation; Link; Logistics; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; Outcome; Participant; Phenotype; Policies; Policy Analysis; Procedures; Recommendation; Research; Research Personnel; Role; Sampling; Structure; Time; Trans-Omics for Precision Medicine; Visualization; base; catalyst; cloud based; computerized data processing; data access; data sharing; database of Genotypes and Phenotypes; design; genomics cloud; informant; interest; novel; novel strategies; phenotypic data; precision medicine; public trust; tool; translational genomics; trustworthiness; All of Us Research Program; Biomedical Research; Clinical; Clinical Data; Collaborations; Communities; Cost utility; Data; Data Analyses; Data Storage and Retrieval; Data Store; Decision Making; Development; Ensure; Equilibrium; Feedback; Genomic Data Commons; Genomic medicine; Genomics; Goals; Health; Human; Individual; Informatics; Informed Consent; Interview; Investigation; Link; Logistics; National Heart, Lung, and Blood Institute; National Human Genome Research Institute; Outcome; Participant; Phenotype; Policies; Policy Analysis; Procedures; Recommendation; Research; Research Personnel; Role; Sampling; Structure; Time; Trans-Omics for Precision Medicine; Visualization; base; catalyst; cloud based; computerized data processing; data access; data sharing; database of Genotypes and Phenotypes; design; genomics cloud; informant; interest; novel; novel strategies; phenotypic data; precision medicine; public trust; tool; translational genomics; trustworthiness

PROJECT SUMMARY The storage, sharing, and analysis of individual-level genomic, environmental, and linked phenotypic and/or health outcome data poses profound technical and logistical challenges for precision medicine research. Accordingly, new cloud-based computing and storage platforms are being developed to support facile data processing and cloud-based analysis commons. However, the implications for responsible data governance of these mechanisms are currently unexamined. To promote responsible and trustworthy data governance (i.e. decision-making about how biomedical data are stored, accessed, and used by researchers, as well as communicated to research participants), we will conduct an in-depth qualitative analysis of the policies, practices, and procedures associated with three emerging cloud-based precision medicine platforms: the BioData Catalyst or BDCatalyst (NHLBI), the Analysis, Visualization, and Informatics Lab-space or AnVIL (NHGRI), and the Research Hub (All of Us Research Program). The immediate goal of this exploratory investigation will be to examine how the control and management of genomic and linked clinical data stored on these platforms differs from earlier data sharing efforts. We will also explore what research stakeholders, including platform developers; investigators (data contributors as well as data users); institutional officials; and funders regard as the most relevant governance tradeoffs associated with the new approaches. In particular, we will solicit views on mechanisms employed to protect participant data, ensure that research uses are aligned with informed consent, and make well-validated results available to interested participants. Draft recommendations based in these observations will be shared with the research community and form the basis for subsequent research, which will introduce these new platforms and the research practices they enable to diverse research participants for feedback and critical reflection. To achieve these research objectives, we will pursue the following Aims: (1) Characterize current approaches to the storage, sharing, and analysis of largescale genomic and linked data enabled by emerging cloud-based analysis platforms; (2) Explore stakeholder views on current and proposed approaches to the governance of genomic cloud-based analysis platforms; and (3) Propose and vet recommendations for the trustworthy governance of genomic and linked environmental and phenotypic data in the context of cloud-based platforms. The proposed investigation will generate novel, timely, and detailed information about the storage, access, and intended use of large-scale genomic and linked clinical data held in emerging cloud-based data storage and analysis platforms. These data will provide a robust basis from which to identify best practices for trustworthy data governance and provide enhanced transparency about a key precision medicine research tool.

项目摘要 个人级基因组，环境和链接表型和/或链接的存储，共享和分析 健康结果数据为精确医学研究带来了深远的技术和后勤挑战。 因此，正在开发新的基于云的计算和存储平台以支持便利数据 加工和基于云的分析共享。但是，对负责任的数据治理的影响 这些机制目前尚未审查。促进负责任和值得信赖的数据治理（即 关于研究人员如何存储，访问和使用生物医学数据的决策以及 与研究参与者传达），我们将对政策进行深入的定性分析， 实践和与三个基于云的精确医学平台相关的程序： 生物催化剂或BDCATALYST（NHLBI），分析，可视化和信息学实验室空间或砧 （NHGRI）和研究中心（我们所有的研究计划）。此探索的直接目标 研究将是研究如何在基因组和链接临床数据的控制和管理上 这些平台不同于早期的数据共享工作。我们还将探讨哪些研究利益相关者， 包括平台开发人员；调查人员（数据贡献者和数据用户）；机构官员；和 资助者认为是与新方法相关的最相关的治理权衡。尤其， 我们将征求对保护参与者数据的机制的看法，确保研究用途是 与知情同意书保持一致，并为有兴趣的参与者提供了良好的验证结果。草稿 基于这些观察结果的建议将与研究界共享，并形成基础 对于随后的研究，这将介绍这些新平台以及他们能够介绍的研究实践 多元化的研究参与者进行反馈和批判性反思。为了实现这些研究目标，我们将 追求以下目的：（1）表征存储，共享和分析的当前方法 通过新兴的基于云的分析平台启用了LargesCale基因组和链接数据； （2）探索 利益相关者对基于基因组分析的治理的当前和提议的方法的看法 平台； （3）提出和兽医建议基因组的可信赖治理 基于云平台的环境中的环境和表型数据。拟议的调查将 生成有关大规模存储，访问和预期用途的小说，及时和详细的信息 基于云的数据存储和分析平台中持有的基因组和链接的临床数据。这些 数据将为可信赖的数据治理和 提供有关关键精度医学研究工具的增强透明度。