Matentzoglu Nicolas, Bello Susan M, Stefancsik Ray, Alghamdi Sarah M, Anagnostopoulos Anna V, Balhoff James P, Balk Meghan A, Bradford Yvonne M, Bridges Yasemin, Callahan Tiffany J, Caufield Harry, Cuzick Alayne, Carmody Leigh C, Caron Anita R, de Souza Vinicius, Engel Stacia R, Fey Petra, Fisher Malcolm, Gehrke Sarah, Grove Christian, Hansen Peter, Harris Nomi L, Harris Midori A, Harris Laura, Ibrahim Arwa, Jacobsen Julius O B, Köhler Sebastian, McMurry Julie A, Munoz-Fuentes Violeta, Munoz-Torres Monica C, Parkinson Helen, Pendlington Zoë M, Pilgrim Clare, Robb Sofia Mc, Robinson Peter N, Seager James, Segerdell Erik, Smedley Damian, Sollis Elliot, Toro Sabrina, Vasilevsky Nicole, Wood Valerie, Haendel Melissa A, Mungall Christopher J, McLaughlin James A, Osumi-Sutherland David
Semanticly, Athens, Greece.
The Jackson Laboratory, Bar Harbor, ME.
bioRxiv. 2024 Sep 22:2024.09.18.613276. doi: 10.1101/2024.09.18.613276.
Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.
表型数据对于理解基因组变异的生物学机制和后果至关重要，并且对于疾病诊断和治疗开发等临床应用案例也起着关键作用。一个多世纪以来，在许多不同背景下针对各种生物体收集了大量的表型数据。新兴的表型组学领域专注于整合和解释这些数据，以为生物学假设提供信息。表型组学的一个主要障碍是记录生物体可观察特征的方法种类繁多且相互脱节。表型数据是使用自由文本、单个术语或术语组合，通过多种词汇表、术语集或本体来收集和整理的。整合这些异构且通常孤立的数据能够在物种内部和跨物种应用生物学知识。现有的整合工作通常仅限于术语对之间的映射；非常需要一种能够捕获跨物种表型组学数据全范围的通用知识表示。我们已经开发了统一表型本体（uPheno）框架，这是一项社区工作，旨在在特定领域的表型本体之上提供一个整合层，作为一个单一、统一的逻辑表示。uPheno 包括：（1）一个使用本体设计模式对表型术语进行一致计算定义的系统，作为社区库进行维护；（2）一个物种中性表型术语的层次词汇表，其特定物种的对应术语归在该词汇表之下；以及（3）特定物种本体之间的映射表。这种统一的表示支持不同生物体基因型 - 表型关联的跨物种整合以及跨物种信息丰富的变异优先级排序等用例。
