Improvements to the LinkML framework to support the Phenomics First open science resource

改进 LinkML 框架以支持 Phenomics First 开放科学资源

• 批准号: 10608894
• 负责人: MELISSA A HAENDEL
• 金额: $ 26.59万
• 依托单位: UNIVERSITY OF COLORADO DENVER
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-09 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10608894
• 关键词: Address; Adoption; Algorithms; Animal Model; Basic Science; Biology; Candidate Disease Gene; Case Study; Characteristics; Clinical; Clinical Data; Communities; Complex; Conflict (Psychology); Data; Data Sources; Databases; Development; Diagnosis; Diagnostic; Disease; Disease model; Evaluation; Face; Fast Healthcare Interoperability Resources; Frequencies; Functional disorder; Genes; Genetic Diseases; Genome; Genomics; Genotype; Human; Human Genome; Information Resources; Journals; Knowledge; Link; Mainstreaming; Manuals; Medical Genetics; Methods; Modality; Modeling; Modernization; Ontology; Outcome; Patient Care; Patients; Phenotype; Process; Rare Diseases; Research; Research Personnel; Resources; Software Engineering; Source; Structure; System; Techniques; Technology; Terminology; Variant; Work; base; clinical care; clinically actionable; community partnership; computational platform; data integration; data modeling; data standards; database schema; disease diagnosis; disease phenotype; gene function; genetic variant; genomic variation; heterogenous data; human disease; improved; interoperability; novel; open data; patient registry; phenomics; phenotypic data; precision medicine; prototype; tool; variant of unknown significance

A Phenomics-First Resource (PFR) for interpretation of variants Genomics is key to precision medicine; however, despite the ease of sequencing, clinical interpretation is still thwarted because relevant data (disease, phenotype, and variant) is complex, heterogeneous, and disaggregated across sources. Moreover, this evidence is sometimes incomplete, conflicting, and erroneous. Consequently, clinicians face long lists of candidate diseases, genes, and countless variants of unknown significance. This situation will not improve without capturing and harmonizing the underlying phenotypic information; computability of this information is the bedrock for the emerging field of ​phenomics​. From basic science to clinical care, communities need structured ways to represent and exchange phenotypes and disease definitions. Addressing these fundamental phenomics needs makes it possible to computationally assess and reveal links between diseases and variants. We have previously shown how the addition of phenotypic information using the Human Phenotype Ontology (HPO) can improve the diagnostic yield for hard-to-diagnose patients, and HPO is therefore now a global standard for “deep phenotyping”. We have demonstrated the applicability of deep phenotyping in the evaluation of rare diseases which have overlapping mechanistic underpinnings with common/complex diseases as well as evolutionarily conserved mechanisms in model organisms. Having coordinated the community and prototyped the underlying computational platforms, we will now align both phenotype ontologies and clinical terminologies, enabling better comparison and inference of phenotypes for improved diagnostic efficacy. We propose to develop a Phenomics-First Resource (PFR). ​Specifically we will: 1. Create a community-driven framework of interoperable phenotype definitions across species​ (uPheno) 2. Harmonize human disease definitions with the ​MONDO​ disease alignment resource 3. Create a community-wide exchange standard for clinical and model-organism phenotypes (​Phenopackets​) 4. Develop an integrated phenomics platform ​to provide the research ​(e.g. BioLink) and clinical (​FHIR​) communities with programmatic access to phenomics ontologies, data, and algorithms The dynamic suite of interlinked technologies will together leverage community-developed knowledge in order to make variant interpretation more reliable, better provenanced, and more clinically actionable.

用于解释变体的表型学优先资源（PFR） 基因组学是精准医学的关键;然而，尽管测序很容易，但临床解释仍然是 由于相关数据（疾病、表型和变异）复杂、异质， 按来源分列。此外，这些证据有时是不完整的，相互矛盾的，错误的。 因此，临床医生面临着一长串的候选疾病、基因和无数未知的变异。 意义如果不捕获和协调潜在的表型，这种情况将不会改善。 信息;这种信息的可计算性是表型组学新兴领域的基石。从基本 从科学到临床护理，社区需要结构化的方式来表示和交换表型， 疾病定义。解决这些基本的表型组学需求， 评估和揭示疾病和变异之间的联系。我们之前已经展示了如何添加 使用人类表型本体（HPO）的表型信息可以提高诊断率， 因此，HPO现在是“深度表型”的全球标准。我们有 证明了深度表型在评价具有重叠的罕见疾病中的适用性 常见/复杂疾病的机制基础以及进化保守的机制， 模式生物在协调了社区并建立了底层计算平台的原型之后， 现在，我们将对齐表型本体和临床术语，从而实现更好的比较， 推断表型以提高诊断效率。我们建议开发一个现象学优先 资源（PFR）。 具体而言，我们将： 1.创建一个社区驱动的跨物种互操作表型定义框架（uPheno） 2.将人类疾病定义与MONDO疾病调整资源相统一 3.为临床和模式生物表型创建社区范围的交换标准 （Phenopackets） 4.开发一个综合的表型组学平台，以提供研究（例如BioLink）和临床（FHIR） 可编程访问表型组学本体、数据和算法的社区 相互关联的动态技术套件将共同利用社区开发的知识， 使变异解释更可靠，更好的出处，更临床可行。