University of Birmingham Medical School.
University of Cambridge.
Brief Bioinform. 2018 Sep 28;19(5):1008-1021. doi: 10.1093/bib/bbx035.
The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological sciences. At the heart of computational phenotype analysis are the phenotype ontologies. A large number of these ontologies have been developed across many domains, and we are now at a point where the knowledge captured in the structure of these ontologies can be used for the integration and analysis of large interrelated data sets. The Phenotype And Trait Ontology framework provides a method for formal definitions of phenotypes and associated data sets and has proved to be key to our ability to develop methods for the integration and analysis of phenotype data. Here, we describe the development and products of the ontological approach to phenotype capture, the formal content of phenotype ontologies and how their content can be used computationally.
在过去的十年中，医学、生态学、畜牧业和植物育种等领域的基因型数据收集呈爆炸式增长。随之而来的是处理相关表型数据的挑战，这些数据不仅规模庞大，而且具有高度多维性。因此，对表型进行计算分析对于我们理解生物科学中基因组数据的生物学意义至关重要。计算表型分析的核心是表型本体。在许多领域都开发了大量的这些本体，现在我们已经到了可以利用这些本体结构中捕获的知识来整合和分析大型相互关联的数据集的地步。表型和特征本体框架为表型和相关数据集的正式定义提供了一种方法，事实证明，这是我们开发整合和分析表型数据方法的关键。在这里，我们描述了本体论方法在表型捕获方面的发展和成果，表型本体的正式内容以及如何在计算上使用其内容。
