Bio-Ontologies SIG Meeting at the 21st International conference on Intelligent Systems for Molecular Biology (ISMB) and 12th European Conference on Computational Biology (ECCB)
BioAssay Ontology (BAO): Modularization, Integration and Applications
Uma D. Vempati, Hande Küçük, Saminda Abeyruwan, Ubbo Visser, Vance Lemmon, Ahsan Mir, Stephan C. Schürer
The lack of established and widely-used standards to describe and annotate biological assays and screening results in the domain of chemical probe and drug discovery is a severe limitation to utilize these valuable datasets to their maximum potential. Lack of standardized metadata and common terms and definitions for all relevant details of high-throughput screening (HTS) assays 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 this problem, we created the BioAssay Ontology (BAO): http://bioassayontology.org. Since the first release of BAO, we have developed several collaborations, which are focused on the integration, application, and extension of BAO; these include the BioAssay Research Database (BARD, http://BARD.nih.gov) developed in the MLP (Molecular Libraries Program), Library of Integrated network-based Cellular Signatures (LINCS) Information FramEwork (LIFE, http://life.ccs.miami.edu/life/), RegenBase (http://regenbase.org), PubChem, ChEMBL, and groups in the pharmaceutical industry. Here we describe the evolution of BAO with a design that enables modeling complex assays (including profile and panel assays). We integrated many modules from established biomedical ontologies, and an upper level ontology. Our modularization approach defines several integral 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 views) that can suit particular use cases or software applications.
BioAssay Ontology (BAO) and the LINCS Information FramEwork (LIFE) to integrate and analyze diverse high throughput and cellular profiling assay data
June 6 – 7, 2013
BAO enables standardized description, integration and meta-‐analysis of various high throughput and profiling assay and screening results. We illustrate our ontology-‐driven approach using data generated in the Library of Integrated Cellular Network-‐based Signatures (LINCS) Program. LIFE is a semantically enabled software system to exchange, query and explore these datasets.
The lack of a established and accepted standards to describe and annotate biological assays and screening results in the domain of high throughput and high content screening (HTS, HCS) is a severe limitation to utilize these valuable datasets to their maximum potential. We developed BioAssay Ontology (BAO) to enable standardized description, integration and meta-‐ analysis of various high throughput and profiling assay and screening results. BAO leverages Description Logic and Web Ontology Language to capture and formalize knowledge about assays and enable computational systems to utilize this knowledge. We illustrate our approach using data generated in the Library of Integrated Cellular Network-‐based Signatures (LINCS) Program, a recent large-‐scale systems biology data production and analysis effort funded by the NIH. Leveraging the ontology, we developed a semantic model to describe and integrate LINCS data. We implemented a semantic web software system, the LINCS Information FramEwork (LIFE) to exchange, query, and explore these datasets. Our approach facilitates the linking and classification of diverse entities, such small molecules, cellular model systems, diseases, gene and protein kinase targets, based on the underlying cellular profiling results.