BioAssay Ontology Annotations Facilitate Cross-Analysis of Diverse High-Throughput Screening Data Sets

Author:

Schürer Stephan C.12,Vempati Uma1,Smith Robin1,Southern Mark3,Lemmon Vance14

Affiliation:

1. Center for Computational Science, University of Miami, Miami, Florida

2. Department of Molecular and Cellular Pharmacology, Miller School of Medicine, University of Miami, Miami, Florida

3. The Scripps Research Molecular Screening Center, The Scripps Research Institute, Jupiter, Florida

4. The Miami Project to Cure Paralysis, Department of Neurological Surgery, Miller School of Medicine, University of Miami, Miami, Florida

Abstract

High-throughput screening data repositories, such as PubChem, represent valuable resources for the development of small-molecule chemical probes and can serve as entry points for drug discovery programs. Although the loose data format offered by PubChem allows for great flexibility, important annotations, such as the assay format and technologies employed, are not explicitly indexed. The authors have previously developed a BioAssay Ontology (BAO) and curated more than 350 assays with standardized BAO terms. Here they describe the use of BAO annotations to analyze a large set of assays that employ luciferase- and β-lactamase–based technologies. They identified promiscuous chemotypes pertaining to different subcategories of assays and specific mechanisms by which these chemotypes interfere in reporter gene assays. Results show that the data in PubChem can be used to identify promiscuous compounds that interfere nonspecifically with particular technologies. Furthermore, they show that BAO is a valuable toolset for the identification of related assays and for the systematic generation of insights that are beyond the scope of individual assays or screening campaigns.

Publisher

Elsevier BV

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