Formalization, Annotation and Analysis of Diverse Drug and Probe Screening Assay Datasets Using the BioAssay Ontology (BAO)

Abstract

Huge amounts of high-throughput screening (HTS) data for probe and drug development projects are being generated in the pharmaceutical industry and more recently in the public sector. The resulting experimental datasets are increasingly being disseminated via publically accessible repositories. However, existing repositories lack sufficient metadata to describe the experiments and are often difficult to navigate by non-experts. The lack of standardized descriptions and semantics of biological assays and screening results hinder targeted data retrieval, integration, aggregation, and analyses across different HTS datasets, for example to infer mechanisms of action of small molecule perturbagens. To address these limitations, we created the BioAssay Ontology (BAO). BAO has been developed with a focus on data integration and analysis enabling the classification of assays and screening results by concepts that relate to format, assay design, technology, target, and endpoint. Previously, we reported on the higher-level design of BAO and on the semantic querying capabilities offered by the ontology-indexed triple store of HTS data. Here, we report on our detailed design, annotation pipeline, substantially enlarged annotation knowledgebase, and analysis results. We used BAO to annotate assays from the largest public HTS data repository, PubChem, and demonstrate its utility to categorize and analyze diverse HTS results from numerous experiments. BAO is publically available from the NCBO BioPortal at http://bioportal.bioontology.org/ontologies/1533. BAO provides controlled terminology and uniform scope to report probe and drug discovery screening assays and results. BAO leverages description logic to formalize the domain knowledge and facilitate the semantic integration with diverse other resources. As a consequence, BAO offers the potential to infer new knowledge from a corpus of assay results, for example molecular mechanisms of action of perturbagens.

The opportunities of mining historical and collective data in drug discovery

“…what was the assay target, what technology was used, which endpoint was measured). For example, BioAssay Ontology (BAO) [91,92] provides a detailed description of bioassay experiments and screening results and BAO terms are organized into the categories 'assay format', 'assay design', 'detection technology', 'meta target', 'perturbagen' and 'endpoint'. Among other projects, BAO has been applied to describe experimental contexts and bioactivity data in the BioAssay Research Database (BARD) [89], a new bioassay database from the NIH Molecular Libraries Program [93].…”

Using the BioAssay Ontology for Analyzing High-Throughput Screening Data

“…[10][11][12] The assay design and detection methods of the in-house HTS assays have been analyzed together with 233 primary PubChem HTS assays. 7 Annotated PubChem assays have been provided by the BAO team and are mainly biochemical assays, G protein-coupled receptor (GPCR) assays, and various assays detected by luminescence. We have also evaluated BARD as an external source of annotated PubChem assay data.…”

Screening of cell cycle fusion proteins to identify kinase signaling networks

“…That resulted in the total of 126 unique SIDs. The SIDs were matched to the previously annotated luciferase assays 65,66 and 20 screening data points were identified.…”