BioAssay Ontology (BAO): a semantic description of bioassays and high-throughput screening results
1 Department of Computer Science, University of Miami, Coral Gables, FL, USA
2 Center for Computational Science, University of Miami, Miami, FL, USA
3 The Miami Project to Cure Paralysis, Department of Neurological Surgery, University of Miami, Miami, FL, USA
4 Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, FL, USA
BMC Bioinformatics 2011, 12:257 doi:10.1186/1471-2105-12-257Published: 24 June 2011
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 webcite.
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.