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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

基本信息

批准号：
10269338
负责人：
MELISSA A HAENDEL
金额：
$ 77.64万
依托单位：
JACKSON LABORATORY
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：
2021-09-20 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10269338
关键词：
Academia Address Adopted Adoption Age of Onset Algorithms Automated Annotation Basic Science Biology Candidate Disease Gene Cardiology Case Study Classification Clinic Clinical Clinical Data Clinical Management Clinical Medicine Collaborations Communities Computational algorithm Computer Analysis Computer software Data Data Reporting Development Diagnosis Diagnostic Differential Diagnosis Discipline Disease Disease model Electronic Health Record Ensure Event Face Foundations Frequencies Genes Genetic Diseases Genome Genomics Goals Guidelines Health Human Immunology Industry International Intervention Laboratories Language Link Location Maintenance Malignant Childhood Neoplasm Manuals Medical Medical Genetics Modeling Modernization Nature Nephrology Neurology Online Systems Ontology Ophthalmology Patients Performance Pharmacogenomics Phenotype Process Publishing Rare Diseases Research Resources Seizures Sex Bias Standardization Structure Terminology Text Time Translations Variant biobank bioinformatics resource biomedical ontology causal variant clinical care clinical decision-making cohort cost effective data exchange disease natural history disease phenotype experience genetic variant genomic data genomic variation health care delivery human disease improved interoperability ontology development patient registry phenotypic data precision medicine programs quality assurance tool user-friendly variant of unknown significance web portal

项目摘要

PROJECT SUMMARY/ABSTRACT To improve diagnostic yield for rare diseases, we developed the Human Phenotype Ontology (HPO) in 2008 as a comprehensive bioinformatic resource that provides a standardized terminology of phenotypic abnormalities for the analysis of human diseases. HPO reduces ambiguity in disease descriptions—thus enabling more robust differential diagnosis and clinical care—and enables phenotypic contextualization of genomic data for diagnostics and precision medicine. The performance of computational algorithms for differential diagnostics with HPO terms depends critically on the comprehensiveness and depth of HPO annotations for diseases. However, the current manual nature of our biocuration process has limited the quality, depth, and coverage of these annotations. Therefore, this proposal's objectives are to greatly expand the corpus of disease-phenotype annotations by automating portions of the curation and expanding the computational disease model. This project, HPO: Accelerating Computational Integration of Clinical Data for Genomics, will maintain and advance HPO resources to address the needs of a growing number of medical disciplines that have adopted the HPO. We will achieve this goal by 1) automating HPO development, maintenance, and release processes, 2) developing representations of rare disease treatments and interventions, and 3) extending our current computational disease models to represent time course, sex biases, and frequency of events, and to incorporate case report data. We also provide a sustainable solution to community contribution with a user-friendly, web- based portal to enable contributors to vet and suggest improvements to the ontology and the annotations and grow the HPO contributor community. In summary, our project addresses the most pressing needs for advancements of the HPO to ensure sustainable, robust, and rigorous development, to enable HPO resources to support new communities, new applications, and more medical disciplines.

项目总结/摘要为了提高罕见病的诊断率，我们在2008年开发了人类表型本体（HPO），一个全面的生物信息资源，提供了一个标准化的术语表型异常用于分析人类疾病。HPO减少了疾病预防中的模糊性，从而使更强大的鉴别诊断和临床护理，并使基因组数据的表型背景化，诊断学和精准医学。具有HPO项的微分诊断的计算算法的性能关键取决于 HPO疾病注释的全面性和深度。然而，我们目前的手动性质生物定位过程限制了这些注释的质量、深度和覆盖范围。因此，这一提议目标是通过自动化疾病表型注释的部分来大大扩展疾病表型注释的语料库。策展和扩展计算疾病模型。这个项目，HPO：加速基因组学临床数据的计算集成，将保持和推进HPO资源，以满足越来越多的采用 HPO我们将通过以下方式实现这一目标：1）自动化HPO开发、维护和发布过程; 2）开发罕见疾病治疗和干预措施的代表性，以及3）扩展我们当前的计算疾病模型，以代表时间过程，性别偏见和事件的频率，并结合病例报告数据。我们还提供了一个可持续的解决方案，社区贡献与用户友好，网络- 的门户，使贡献者能够审查和建议改进本体和注释，发展HPO贡献者社区。总之，我们的项目解决了HPO发展的最迫切需求，以确保可持续发展，强大而严格的开发，使HPO资源能够支持新的社区、新的应用程序，更多医学学科