A Collaboration for the NIH Data Commons

实现研究效率目标的数据共享需要转变我们的方式 访问、使用和生成数据。这一愿景将需要多学科、多机构的努力 调查团队在生物医学、云架构、 软件工程、分析工具和数据协调。八把钥匙中的每一把 能力(KCs)解决了科学家在大规模 生物医学数据。拟议项目的设计使每个幼稚园都有独特的目标 以及以独立的最低可行产品(MVP)的形式交付的产品，但加在一起， KCS形成了一个连续的洞察力和方法，捕获了数据的五个V并反映了 公平原则。 具体的科学用途 KC8的案例是性作为 生物变量(SABV)。 SABV是尊重的不可知论者 对任何疾病或医学 条件，清单横跨 多临床、多模式 系统，与所有人相关 数据和数据集的类型 在RFA中强调， 需要数据模型，并且 数据协调范围 数据和地址 科学严谨性面临的挑战 和透明度，就像最近一样 由美国国立卫生研究院强调。 此外，用例还包括 通过以下方式实现这些目标 测算 在数据源上 由NIH确认，即， TOPMed、GTEx和MODS。 此外，SABV作为一种用途 案例使贡献成为可能 要检查其中每个KC的KCS 现实生活的背景 挑战。事实上，跨多个知识领域的数据集成的一个关键挑战是 确定可以预测性使用的共性和趋势。SABV 作为需要最大化数据效用以用于计算用途的范例。 为了通过提议的工作演示跨KC连接性，请考虑一个协作团队， 有兴趣确定具体的饮食干预措施是否因性别而在有效性上有所不同。vbl.使用 KC8.MVP1，研究小组检查了SABV对胰腺基因表达的影响 (GTEx)和代谢基因产物(MODS)。将结果移动到云环境中 由KC4 Pivot提供，并由KC5数据科学堆栈提供计算能力 和CWL执行工具，其中团队利用全基因组测序和 通过KC8.MVP2并使用KC3API和工具套件、KC2GUID从TOPMed获得的表型数据 最佳做法和登记处，以及KC7索引/搜索能力，以促进这一进程。 该团队进行分析，以确定协变量并开发模型，以便进一步分析 表现出性别二态和/或与饮食干预的性别交互作用的基因座。所有结果 存储到数据公地中，并用于指导随机设计的高效 对照临床试验。团队的资源和产品使用Kc1 FIRE-TLC进行评估 指标，KC6治理委员会监督团队活动，以确保所有道德、 已经考虑了安全和隐私问题，并执行了要求。 我们设想了一套独立但可互操作的KC，旨在无缝地解决 SABV背景下的生物医学数据挑战，可能与KCs相辅相成 由其他团体提出。我们的团队是专门为其协作和开放而组建的 Science重视并已在合作伙伴中分发了一份财团协议草案 机构。

A Data Commons that realizes the goal of efficiency in research needs to transform the way we access, use, and generate data. This vision will require the efforts of a multidisciplinary, multiinstitutional investigative team with complementary expertise in biomedicine, cloud architecture, software engineering, analytical tools, and data harmonization. Each of the eight Key Capabilities (KCs) addresses specific challenges faced by scientists working with large-scale biomedical data. The proposed projects are designed such that each KC has unique objectives and deliverables in the form of stand-alone Minimum Viable Products (MVPs), yet together, the KCs form a continuum of insights and approaches that capture the five V’s of data and reflect FAIR principles. The specific scientific use case for KC8 is sex as biological variable (SABV). SABV is agnostic with respect to any disease or medical condition, manifests across multiple clinical and model systems, is relevant to all types of data and datasets emphasized in the RFA, requires a data model and data harmonization across data, and addresses challenges in scientific rigor and transparency, as recently emphasized by NIH. Moreover, the use case achieves these goals by examining and computing over the data sources identified by NIH, namely, TOPMed, GTEx, and MODs. Furthermore, SABV as a use case enables contributing KCs to examine each KC in the context of real-life challenges. Indeed, a key challenges in data integration across multiple knowledge domains is the identification of commonalities and trends that can be used in a predictive manner. SABV serves as an exemplar that requires maximizing data utility for computational use. To exemplify cross-KC connectivity with the proposed work, consider a collaborative team with interest in determining whether specific dietary interventions differ in effectiveness by sex. Using KC8.MVP1, the team examines the impact of SABV on gene expression in the pancreas (GTEx) and on metabolic gene products (MODs). Results are moved into the cloud environment provided by KC4 PIVOT and the compute capabilities provided by KC5 Data Science Stacks and CWL Execution Tools, where the team leverages whole-genome sequencing and phenotypic data from TOPMed via KC8.MVP2 and using KC3 API and Tool Suite, KC2 GUIDs Best Practices and Registry, and KC7 Indexing/Search Capabilities to facilitate the process. The team conducts analyses to identify covariates and develop models for further analysis of loci that exhibit sexual dimorphisms and/or sex interactions with dietary interventions. All results are deposited into the Data Commons and used to guide the efficient design of randomized controlled clinical trials. The team’s resources and products are assessed using KC1 FAIR-TLC METRICS, and KC6 Governance Council oversees team activities to ensure that all ethical, security, and privacy issues have been considered and requirements are enforced. We envision a set of independent, yet interoperable, KCs designed to seamlessly address biomedical data challenges in the context of SABV, with likely complementarity to the KCs proposed by other groups. Our team was assembled specifically for its collaborative and open science values and has circulated a draft Consortium Agreement among the partnering institutions.