The Integration of Trans-omics for Precision Medicine (TOPMED) and Other Heart, Lung, Blood and Sleep (HLBS) Data Sets with the Data Commons

精准医学跨组学 (TOPMED) 和其他心、肺、血液和睡眠 (HLBS) 数据集与数据共享的集成

• 批准号: 10267909
• 负责人: ROBERT L. GROSSMAN
• 金额: $ 875万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-30 至 2022-03-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10267909
• 关键词: Address; Architecture; Atlases; Automobile Driving; Award; Biological Sciences; Blood; California; Cells; Chicago; Clinical Data; Clinical Management; Complex; Computer software; Costs and Benefits; Creativeness; Data; Data Analyses; Data Commons; Data Science; Data Set; Ecosystem; Encapsulated; Ensure; Environment; Funding; Genome; Genotype-Tissue Expression Project; Goals; Heart; Human; Hybrids; Image; Individual; Institutes; Investigation; Lung; Medical Records; Metals; Modeling; Molecular Profiling; Participant; Philosophy; Production; Readability; Running; Scientist; Sleep; Standardization; Trans-Omics for Precision Medicine; United States National Institutes of Health; Universities; Vision; brain health; cloud based; cohesion; cohort; data format; design; diverse data; genome sequencing; high resolution imaging; human disease; insight; interoperability; model organisms databases; multiple data types; open source; programs; software development; working group

The life sciences are in the midst of a data revolution. Cheap and accurate genome sequencing is a reality, high-resolution imaging is becoming routine, and clinical data is increasingly stored in machine-readable formats. These breakthroughs have brought us to the threshold of a new era in biomedicine, one where the data sciences hold the potential to propel our understanding and treatment of human disease. Achieving this potential, however, will require creating software platforms that can support storing, sharing, and analyzing data at unlimited scale. In this application, we propose to address this unmet need by bringing together three groups — the University of Chicago, the Broad Institute, and the University of California at Santa Cruz — each with a strong track record of developing production-grade software platforms to support flagship scientific efforts, including the All of Us Cohort Program, the Genome Data Commons (GDC) and its affiliated NCI Cloud Pilots program, and the Human Cell Atlas Data Coordination Platform (HCA DCP). Our goal is to align and integrate our individual efforts at building data platforms, in order to build a cohesive environment that can serve the needs of the NIH Data Commons and beyond. Because these platforms were each developed to fulfill differing use cases, there is currently far more complementarity than overlap between them. For example, Dr. Grossman has extensive expertise in running a hybrid cloud at scale to support the needs of the GDC; this provides cost benefits around data transport and egress that would be invaluable to the NIH Data Commons. Similarly, Dr. Philippakis has developed a cloud-based model of collaborative workspaces (FireCloud) and software for management of secondary data use restrictions (DUOS), and Dr. Paten has long been a leader in developing and implementing standardized APIs as part of the GA4GH. It is this complementarity that motivates us to integrate our efforts. In the sections below, we present our plans for creating the Commons Alliance Platform. In addition to having a unified technical vision for what is needed, we are also aligned around a core set of guiding principles: (1) Open-source - All the software we develop, from user interfaces down to cloud metal, is open-source. This includes not only the software that would be funded via this awarding mechanism, but all software developed and deployed by our team. (2) Modular and interoperable - A design principle of all complex software undertakings is “separation of concerns,” i.e. the notion that there should be a clean division between architectural components, each encapsulated by well-defined interfaces. We are committed to building modular and interoperable software and, in doing so, encouraging the creation of an ecosystem around them. (3) Standards-driven - Our team is committed to creating and utilizing standardized APIs and data formats. We have been leaders in GA4GH since its founding, chairing various working groups and driver projects. (4) Healthy Competition - Our consortium’s philosophy is to collaborate on APIs to support interoperability, but compete on implementation to encourage creativity and diversity. (5) Diversity of data types - We have expertise in multiple data types beyond molecular profiling. In particular, a key goal of All of Us is to collect extensive clinical data in the form of participant-provided data and medical records. Similarly, through the Brain Health Commons, Dr. Grossman will be managing clinical and imaging data. These capabilities will be invaluable as the Commons expands to include additional data types. (6) Driven by scientific use cases - Our consortium includes many leading scientists, including PIs on awards for model organism databases, GTEx, and TOPMed. We will leverage their insights via driving use cases to ensure that our software enables flagship scientific investigations.

生命科学正处于数据革命之中。廉价且准确的基因组测序是一种现实，高分辨率成像正变得常规，临床数据越来越多地以机器可读格式存储。这些突破使我们达到了生物医学新时代的门槛，其中数据科学具有推动我们对人类疾病的理解和治疗的潜力。但是，实现这一潜力将需要创建可以以无限规模支持存储，共享和分析数据的软件平台。 In this application, we propose to address this unmet need by bringing together three groups — the University of Chicago, the Broad Institute, and the University of California at Santa Cruz — each with a strong track record of developing production-grade software platforms to support flagship scientific efforts, including the All of Us Cohort Program, the Genome Data Commons (GDC) and its affiliated NCI Cloud Pilots program, and the Human Cell Atlas Data Coordination Platform (HCA DCP）。我们的目标是结盟和整合我们在构建数据平台上的个人努力，以建立一个可以满足NIH数据共享及其他地区需求的凝聚力环境。由于这些平台都是为了满足不同用例而开发的，因此目前的完整性远比它们之间的重叠性要多得多。例如，格罗斯曼博士在大规模运行混合云方面拥有广泛的专业知识，以支持GDC的需求；这为数据传输和出口提供了成本优势，这对NIH数据共享将是无价的。同样，Philippakis博士开发了一种基于云的协作工作区（Firecloud）和用于管理二级数据使用限制的软件（DUOS），Paten博士长期以来一直是开发和实施标准化API作为GA4GH的一部分的领导者。正是这种完整性激励我们整合我们的努力。在下面的部分中，我们介绍了创建Commons Alliance平台的计划。除了对所需内容具有统一的技术愿景外，我们还围绕着一组指导原则对齐：（1）开源 - 我们开发的所有软件，从用户界面到云金属的用户界面，都是开源的。这不仅包括（2）模块化和可互操作的软件 - 所有复杂软件承诺的设计原理是“关注点的分离”，即在建筑组件之间应该有一个干净的划分，每个构建组件都应由定义明确的接口安装。我们致力于构建模块化和可互操作的软件，并鼓励创建周围的生态系统。 （3）标准驱动 - 我们的团队致力于创建和利用标准化的API和数据格式。自成立以来，我们一直是GA4GH的领导者，主持了各种工作组和驾驶员项目。 （4）健康竞争 - 我们的财团的理念是在API上进行协作以支持互操作性，但在实施方面竞争以鼓励创造性和多样性。 （5）数据类型的多样性 - 我们在分子分析以外的多种数据类型中具有专业知识。特别是，我们所有人的关键目标是以参与者提供的数据和医疗记录的形式收集广泛的临床数据。同样，通过大脑健康共享，格罗斯曼博士将管理临床和成像数据。随着公共场所扩展以包括其他数据类型，这些功能将是无价的。 （6）在科学用例的驱动下 - 我们的财团包括许多领先的科学家，包括模型有机体数据库奖项，GTEX和Topmed的PI。我们将通过驾驶用例来利用他们的见解，以确保我们的软件可以实现旗舰科学研究。

项目成果

Progress Toward Cancer Data Ecosystems.

• DOI: 10.1097/ppo.0000000000000318
• 发表时间: 2018
• 期刊: Cancer journal (Sudbury, Mass.)
• 作者: Grossman RL

Personalized Pangenome References.

个性化泛基因组参考。

• DOI: 10.1101/2023.12.13.571553
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Sirén,Jouni;Eskandar,Parsa;Ungaro,MatteoTommaso;Hickey,Glenn;Eizenga,JordanM;Novak,AdamM;Chang,Xian;Chang,Pi-Chuan;Kolmogorov,Mikhail;Carroll,Andrew;Monlong,Jean;Paten,Benedict
• 通讯作者: Paten,Benedict

The Dockstore: enhancing a community platform for sharing reproducible and accessible computational protocols.

• DOI: 10.1093/nar/gkab346
• 发表时间: 2021-07-02
• 期刊: Nucleic acids research
• 影响因子: 14.9
• 作者: Yuen D;Cabansay L;Duncan A;Luu G;Hogue G;Overbeck C;Perez N;Shands W;Steinberg D;Reid C;Olunwa N;Hansen R;Sheets E;O'Farrell A;Cullion K;O'Connor BD;Paten B;Stein L
• 通讯作者: Stein L