BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results.
BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results.
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DOI：
10.1186/1471-2105-12-257
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发表时间：
2011-06-24
影响因子：
3
通讯作者：
Schürer SC
中科院分区：
文献类型：
--
作者：
Visser U;Abeyruwan S;Vempati U;Smith RP;Lemmon V;Schürer SC
高通量筛选（HTS）是确定小分子化学探针和药物开发新切入点的主要策略之一，现在公共部门研究普遍可以使用。在HTS活动中产生的大量数据被提交到公共存储库，如PubChem，这正在以指数级的速度增长。现有HTS分析和筛选结果的多样性和数量对数据集的组织、标准化、整合和分析构成了巨大挑战，从而对为实施公共部门HTS能力而进行的巨额投资最大限度地发挥科学影响并最终发挥公共卫生影响构成了巨大挑战。为了应对这些挑战，需要采用新的方法来组织、标准化和访问HTS数据。我们开发了第一个使用表达性描述逻辑来描述HTS实验和筛选结果的本体。生物检测本体（BAO）是HTS检测和数据标准化的基础，也是语义知识模型。在本文中，我们展示了形式化HTS领域知识的重要例子，并指出了这种方法的优点。该本体可在NCBO生物门户网站http://bioportal.bioontology.org/ontologies/44531上在线获得。经过大量的手工管理工作后，我们将BAO映射的数据三元组加载到RDF数据库存储中，并在几个案例研究中使用推理器来演示BAO中形式化领域知识表示的好处。这些示例说明了语义查询功能，其中BAO支持检索与给定查询相关但未显式定义的推断搜索结果。因此，BAO为注释、查询和分析HTS数据集开辟了新的功能，并有可能通过推理发现新知识。
High-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges. We developed the first ontology to describe HTS experiments and screening results using expressive description logic. The BioAssay Ontology (BAO) serves as a foundation for the standardization of HTS assays and data and as a semantic knowledge model. In this paper we show important examples of formalizing HTS domain knowledge and we point out the advantages of this approach. The ontology is available online at the NCBO bioportal http://bioportal.bioontology.org/ontologies/44531. After a large manual curation effort, we loaded BAO-mapped data triples into a RDF database store and used a reasoner in several case studies to demonstrate the benefits of formalized domain knowledge representation in BAO. The examples illustrate semantic querying capabilities where BAO enables the retrieval of inferred search results that are relevant to a given query, but are not explicitly defined. BAO thus opens new functionality for annotating, querying, and analyzing HTS datasets and the potential for discovering new knowledge by means of inference.
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