Integrating phenotype ontologies with PhenomeNET
Integrating phenotype ontologies with PhenomeNET
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DOI：
10.1186/s13326-017-0167-4
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发表时间：
2017-12-19
影响因子：
1.9
通讯作者：
Hoehndorf, Robert
中科院分区：
文献类型：
--
作者：
Angel Rodriguez-Garcia, Miguel;Gkoutos, Georgios V.;Hoehndorf, Robert
背景：来自人类和模式生物的表型数据的整合和分析是建立我们对正常生物学和病理生理学理解的关键挑战。然而，当试图匹配跨物种和跨表型（如行为和肿瘤）的类别时，临床和模型生物数据库中捕获的表型和解剖细节的范围提出了复杂的问题。我们之前开发了 PhenomeNET，这是一个用于疾病基因优先排序的系统，其中包括一个旨在整合表型本体的本体作为其组件之一。虽然不适用于匹配任意本体，但 PhenomeNET 可用于识别不同物种的相关表型，包括人类、小鼠、斑马鱼、线虫、果蝇和酵母。 结果：在这里，我们应用 PhenomeNET 使用自动推理从两个表型和两个疾病本体中识别相关类别。我们证明我们可以识别大量映射，其中一些映射需要自动推理，并且仅通过词汇方法无法轻松识别。将自动推理与词汇匹配相结合，进一步提高了本体对齐的结果。结论：PhenomeNET 可用于对齐和集成表型本体。结果可用于生物医学分析，其中在模型生物体中观察到的现象用于识别人类疾病的致病基因和突变。
Background: Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast.Results: Here, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies.Conclusions: PhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.