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BioAssay Ontology Annotations Facilitate Cross-Analysis of Diverse High-Throughput Screening Data Sets
Author(s) -
Stephan C. Schürer,
Uma D. Vempati,
Robin P. Smith,
Mark R. Southern,
Vance Lemmon
Publication year - 2011
Publication title -
slas discovery
Language(s) - English
Resource type - Journals
eISSN - 2472-5560
pISSN - 2472-5552
DOI - 10.1177/1087057111400191
Subject(s) - pubchem , bioassay , drug discovery , computer science , high throughput screening , identification (biology) , flexibility (engineering) , ontology , computational biology , cheminformatics , chembl , set (abstract data type) , data mining , data science , bioinformatics , biology , mathematics , botany , programming language , philosophy , genetics , statistics , epistemology
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.

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