Development and validation of a computable knowledge framework for genomic medicine

基因组医学可计算知识框架的开发和验证

• 批准号: 10667600
• 负责人: Alex Handler Wagner
• 金额: $ 41.44万
• 依托单位: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-08 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10667600
• 关键词: Clinical; Collection; Consumption; Data; Data Sources; Decision Making; Development; Diagnosis; Discipline; Education; Evaluation; Genome; Genomic medicine; Genomics; Growth; Health; Individual; Intuition; Knowledge; Left; Literature; Malignant Neoplasms; Modernization; Patients; Privatization; Prognosis; Reporting; Research; Research Activity; Resources; Risk; Role; Services; Standardization; System; Therapeutic; Time; Validation; Variant; Work; clinical care; clinical decision support; clinically relevant; clinically significant; combinatorial; cost; data ecosystem; data harmonization; design; genetic variant; genomic data; improved; innovation; interoperability; knowledgebase; novel; research clinical testing; support tools; tool; tool development

Project Summary/Abstract Genomic medicine is the discipline of interpreting genomic information about an individual as part of their clinical care, for diagnosis, prognosis, or therapeutic decision-making. Integral to the practice of genome interpretation is the collection of multiple lines of evidence from knowledgebases to support or refute the clinical significance of evaluated variants. Modern clinical variant knowledgebases maintain literature and variant coverage that is mostly non-overlapping. This diversity of content causes a known problem in genome interpretation: analysts tasked with assembling a clinical variant report choose to spend considerable time navigating multiple resources and collating evidence, or risk missing critical information by selectively evaluating fewer resources. The resulting effort needed for an analyst to clinically interpret a variant list is known as the interpretation bottleneck, for its rate-limiting role in the clinical evaluation of patient genomes. Data integrators from public and private genomic medicine organizations work to alleviate this bottleneck by developing integrative clinical interpretation applications for use by genome analysts. As new knowledgebases are created, each of these public and private data integrators is left with the task of designing and maintaining another interface for each new resource, leading to combinatorial growth of data harmonization effort across the entire system. This approach is not scalable. This project will enable a transition to a scalable, interoperable, and federated genomic data ecosystem from the data integrators and knowledgebases already in existence today. To build an interoperable network of knowledgebases will require development and validation of a computable knowledge framework for genomic medicine. The framework will require the drafting of modern genomic knowledge standards, and development of the tools and services needed to support the implementation of those standards. These objectives will be carried out through coordination of several research activities with the Variant Interpretation for Cancer Consortium, ClinGen, and the Global Alliance for Genomics and Health. Research will involve conceptualization of genomic knowledge as precise computable concepts, designing schema for those concepts, developing framework support tools, and building intuitive user applications to leverage these advances. As a result of this research, new knowledgebases implementing this framework become immediately accessible to existing applications, while new applications and workflows implementing this framework are immediately able to leverage a wide breadth of data sources. In addition to these immediate benefits, the creation of a standardized, federated knowledge network reduces barriers for developing new products, spurring innovation towards novel applications for research, education, and clinical decision support tools.

项目总结/摘要 基因组医学是一门解释个体基因组信息的学科， 临床护理，用于诊断、预后或治疗决策。基因组实践的组成部分 解释是从知识库中收集多条证据，以支持或反驳 评价变异的临床意义。现代临床变异知识库维护文献和 变量覆盖范围大多不重叠。这种内容的多样性导致了基因组中的一个已知问题 解读：负责撰写临床变异报告的分析师选择花费大量时间 浏览多个资源和整理证据，或者有选择地错过关键信息， 评估更少的资源。分析员在临床上解释变体列表所需的工作量是 由于其在患者基因组的临床评估中的限速作用，被称为解释瓶颈。 来自公共和私人基因组医学组织的数据集成商通过以下方式努力缓解这一瓶颈： 开发供基因组分析师使用的综合临床解释应用程序。作为新的知识基础 创建后，这些公共和私有数据集成器中的每一个都将承担设计和维护 每个新资源都有另一个接口，导致跨部门数据协调工作的组合增长。 整个系统这种方法是不可扩展的。 该项目将实现从一个可扩展的，可互操作的和联合的基因组数据生态系统的过渡， 数据集成器和知识库已经存在。为了建立一个可互操作的网络， 知识库将需要开发和验证一个可计算的知识框架， 药该框架将要求起草现代基因组知识标准， 支持实施这些标准所需的工具和服务。这些目标将 通过与癌症变异解释的几项研究活动的协调进行 Consortium、ClinGen和Global Alliance for Genomics and Health。研究将涉及 将基因组知识概念化为精确的可计算概念， 概念，开发框架支持工具，并构建直观的用户应用程序来利用这些概念， 预付款。 作为这项研究的结果，新的知识库实施这一框架立即成为 现有应用程序可以访问，而实现此框架的新应用程序和工作流 立即能够利用广泛的数据源。除了这些直接的好处， 建立一个标准化的联合知识网络减少了开发新产品的障碍， 促进创新，为研究，教育和临床决策支持工具的新应用。

项目成果

Development and application of a computable genotype model in the GA4GH Variation Representation Specification.

GA4GH 变异表示规范中可计算基因型模型的开发和应用。

• 发表时间: 2023
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Goar W

Accessing clinical-grade genomic classification data through the ClinGen Data Platform

通过 ClinGen 数据平台访问临床级基因组分类数据

• 发表时间: 2022
• 期刊: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
• 作者: Karen P. Dalton;H. Rehm;M. W. Wright;Mark E. Mandell;Kilannin Krysiak;L. Babb;Kevin P. Riehle;T. Nelson;A. Wagner
• 通讯作者: A. Wagner