Dataset Information

BioAssay ontology annotations facilitate cross-analysis of diverse high-throughput screening data sets.

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 ?-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.

SUBMITTER: Schurer SC

PROVIDER: S-EPMC3167204 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

BioAssay ontology annotations facilitate cross-analysis of diverse high-throughput screening data sets.

Schürer Stephan C SC Vempati Uma U Smith Robin R Southern Mark M Lemmon Vance V

Journal of biomolecular screening 20110401 4

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 standardize ...[more]

PMID: 21471461

Similar Datasets

Project description:MotivationOntologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other analytical activities. The bio-ontologies community, in particular the Open Biomedical Ontologies (OBO) community, have provided many other ontologies and an increasingly large volume of annotations of gene products that can be exploited in query and analysis. As many annotations with different ontologies centre upon gene products, there is a possibility to explore gene products through multiple ontological perspectives at the same time. Questions could be asked that link a gene product's function, process, cellular location, phenotype and disease. Current tools, such as AmiGO, allow exploration of genes based on their GO annotations, but not through multiple ontological perspectives. In addition, the semantics of these ontology's representations should be able to, through automated reasoning, afford richer query opportunities of the gene product annotations than is currently possible.ResultsTo do this multi-perspective, richer querying of gene product annotations, we have created the Logical Gene Ontology, or GOAL ontology, in OWL that combines the Gene Ontology, Human Disease Ontology and the Mammalian Phenotype Ontology, together with classes that represent the annotations with these ontologies for mouse gene products. Each mouse gene product is represented as a class, with the appropriate relationships to the GO aspects, phenotype and disease with which it has been annotated. We then use defined classes to query these protein classes through automated reasoning, and to build a complex hierarchy of gene products. We have presented this through a Web interface that allows arbitrary queries to be constructed and the results displayed.ConclusionThis standard use of OWL affords a rich interaction with Gene Ontology, Human Disease Ontology and Mammalian Phenotype Ontology annotations for the mouse, to give a fine partitioning of the gene products in the GOAL ontology. OWL in combination with automated reasoning can be effectively used to query across ontologies to ask biologically rich questions. We have demonstrated that automated reasoning can be used to deliver practical on-line querying support for the ontology annotations available for the mouse.AvailabilityThe GOAL Web page is to be found at http://owl.cs.manchester.ac.uk/goal.

Project description:BackgroundThere are three main problems associated with the virtual screening of bioassay data. The first is access to freely-available curated data, the second is the number of false positives that occur in the physical primary screening process, and finally the data is highly-imbalanced with a low ratio of Active compounds to Inactive compounds. This paper first discusses these three problems and then a selection of Weka cost-sensitive classifiers (Naive Bayes, SVM, C4.5 and Random Forest) are applied to a variety of bioassay datasets.ResultsPharmaceutical bioassay data is not readily available to the academic community. The data held at PubChem is not curated and there is a lack of detailed cross-referencing between Primary and Confirmatory screening assays. With regard to the number of false positives that occur in the primary screening process, the analysis carried out has been shallow due to the lack of cross-referencing mentioned above. In six cases found, the average percentage of false positives from the High-Throughput Primary screen is quite high at 64%. For the cost-sensitive classification, Weka's implementations of the Support Vector Machine and C4.5 decision tree learner have performed relatively well. It was also found, that the setting of the Weka cost matrix is dependent on the base classifier used and not solely on the ratio of class imbalance.ConclusionsUnderstandably, pharmaceutical data is hard to obtain. However, it would be beneficial to both the pharmaceutical industry and to academics for curated primary screening and corresponding confirmatory data to be provided. Two benefits could be gained by employing virtual screening techniques to bioassay data. First, by reducing the search space of compounds to be screened and secondly, by analysing the false positives that occur in the primary screening process, the technology may be improved. The number of false positives arising from primary screening leads to the issue of whether this type of data should be used for virtual screening. Care when using Weka's cost-sensitive classifiers is needed - across the board misclassification costs based on class ratios should not be used when comparing differing classifiers for the same dataset.

Project description:The lack of established standards to describe and annotate biological assays and screening outcomes in the domain of drug and chemical probe discovery is a severe limitation to utilize public and proprietary drug screening data to their maximum potential. We have created the BioAssay Ontology (BAO) project (http://bioassayontology.org) to develop common reference metadata terms and definitions required for describing relevant information of low-and high-throughput drug and probe screening assays and results. The main objectives of BAO are to enable effective integration, aggregation, retrieval, and analyses of drug screening data. Since we first released BAO on the BioPortal in 2010 we have considerably expanded and enhanced BAO and we have applied the ontology in several internal and external collaborative projects, for example the BioAssay Research Database (BARD). We describe the evolution of BAO with a design that enables modeling complex assays including profile and panel assays such as those in the Library of Integrated Network-based Cellular Signatures (LINCS). One of the critical questions in evolving BAO is the following: how can we provide a way to efficiently reuse and share among various research projects specific parts of our ontologies without violating the integrity of the ontology and without creating redundancies. This paper provides a comprehensive answer to this question with a description of a methodology for ontology modularization using a layered architecture. Our modularization approach defines several distinct BAO components and separates internal from external modules and domain-level from structural components. This approach facilitates the generation/extraction of derived ontologies (or perspectives) that can suit particular use cases or software applications. We describe the evolution of BAO related to its formal structures, engineering approaches, and content to enable modeling of complex assays and integration with other ontologies and datasets.

Dataset Information

BioAssay ontology annotations facilitate cross-analysis of diverse high-throughput screening data sets.

Publications

BioAssay ontology annotations facilitate cross-analysis of diverse high-throughput screening data sets.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets