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
基本信息
- 批准号:10608894
- 负责人:
- 金额:$ 26.59万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 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:
- 关键词:AddressAdoptionAlgorithmsAnimal ModelBasic ScienceBiologyCandidate Disease GeneCase StudyCharacteristicsClinicalClinical DataCommunitiesComplexConflict (Psychology)DataData SourcesDatabasesDevelopmentDiagnosisDiagnosticDiseaseDisease modelEvaluationFaceFast Healthcare Interoperability ResourcesFrequenciesFunctional disorderGenesGenetic DiseasesGenomeGenomicsGenotypeHumanHuman GenomeInformation ResourcesJournalsKnowledgeLinkMainstreamingManualsMedical GeneticsMethodsModalityModelingModernizationOntologyOutcomePatient CarePatientsPhenotypeProcessRare DiseasesResearchResearch PersonnelResourcesSoftware EngineeringSourceStructureSystemTechniquesTechnologyTerminologyVariantWorkbaseclinical careclinically actionablecommunity partnershipcomputational platformdata integrationdata modelingdata standardsdatabase schemadisease diagnosisdisease phenotypegene functiongenetic variantgenomic variationheterogenous datahuman diseaseimprovedinteroperabilitynovelopen datapatient registryphenomicsphenotypic dataprecision medicineprototypetoolvariant of unknown significance
项目摘要
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.
一种解释变异的表型组学第一资源(PFR)
基因组学是精确医学的关键;然而,尽管测序很容易,临床解释仍然是
受阻,因为相关数据(疾病、表型和变异)复杂、异质和
按来源分列。此外,这种证据有时是不完整的、相互矛盾的和错误的。
因此,临床医生面临着一长串候选疾病、基因和无数未知变种的问题。
意义。如果不捕获和协调潜在的表型,这种情况就不会得到改善
信息;这些信息的可计算性是表观组学新兴领域的基石。从基础
从科学到临床护理,社区需要有组织的方式来表示和交换表型和
疾病定义。解决这些基本的表型组学需求使计算成为可能
评估并揭示疾病和变种之间的联系。我们之前已经展示了如何添加
使用人类表型本体论(HPO)的表型信息可以提高诊断效率
因此,HPO现在是“深度表型”的全球标准。我们有
论证了深度表型在评估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上保守的机制
模型生物。在协调了社区并建立了底层计算平台的原型之后,
我们现在将调整表型本体和临床术语,以实现更好的比较和
推断表型以提高诊断效率。我们建议开发一种表现学-First
资源(PfR)。具体而言,我们将:
1.创建一个社区驱动的跨物种可互操作表型定义框架()
2.协调人类疾病定义与Mondo疾病配对资源
3.创建社区范围内的临床和模式生物表型交换标准
(Phenopackets)
4.开发集成的表型组学平台,提供研究(如Biolink)和临床(FHIR)
通过编程访问表现学本体、数据和算法的社区
动态的互连技术套件将共同利用社区开发的知识,以
使变种解释更可靠,来源更好,临床可操作性更强。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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MELISSA A HAENDEL其他文献
MELISSA A HAENDEL的其他文献
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{{ truncateString('MELISSA A HAENDEL', 18)}}的其他基金
The Human Phenotype Ontology: Accelerating Computational Integration of Clinical Data for Genomics
人类表型本体论:加速基因组学临床数据的计算整合
- 批准号:
10681348 - 财政年份:2021
- 资助金额:
$ 26.59万 - 项目类别:
The Human Phenotype Ontology: Accelerating Computational Integration of Clinical Data for Genomics
人类表型本体论:加速基因组学临床数据的计算整合
- 批准号:
10269338 - 财政年份:2021
- 资助金额:
$ 26.59万 - 项目类别:
The Human Phenotype Ontology: Accelerating Computational Integration of Clinical Data for Genomics
人类表型本体论:加速基因组学临床数据的计算整合
- 批准号:
10491107 - 财政年份:2021
- 资助金额:
$ 26.59万 - 项目类别:
A phenomics-first resource for interpretation of variants
用于解释变异的表型组学优先资源
- 批准号:
10448140 - 财政年份:2021
- 资助金额:
$ 26.59万 - 项目类别:
A phenomics-first resource for interpretation of variants
用于解释变异的表型组学优先资源
- 批准号:
10642958 - 财政年份:2021
- 资助金额:
$ 26.59万 - 项目类别:
Adding Big Data Open Educational Resources to the ONC Health IT Curriculum
将大数据开放教育资源添加到 ONC Health IT 课程中
- 批准号:
8828784 - 财政年份:2014
- 资助金额:
$ 26.59万 - 项目类别:
Adding Big Data Open Educational Resources to the ONC Health IT Curriculum
将大数据开放教育资源添加到 ONC Health IT 课程中
- 批准号:
9132830 - 财政年份:2014
- 资助金额:
$ 26.59万 - 项目类别:
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