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Enhancing the utility of the ECOTOX knowledgebase via ontology-based semantics mapping
Citation:
Fay, K., C. Elonen, D. Hoff, M. Skopinski, A. Pilli, R. Wang, AND C. LaLone. Enhancing the utility of the ECOTOX knowledgebase via ontology-based semantics mapping. SETAC North America, Sacramento, CA, November 04 - 08, 2018.
Impact/Purpose:
The EPA’s ECOTOXicology knowledgebase (ECOTOX) is a database which contains single chemical toxicity effects in more than 12,000 ecological species. The objective of this work is to enhance the interoperability of ECOTOX with several other EPA tools, including: the Computational Toxicology (CompTox) dashboard, Adverse Outcome Pathway Wiki (AOP wiki), and Sequence Alignment to Predict Across Species Susceptibility (SeqAPASS). The AOP discovery and development task on taxonomic applicability recognizes the need to leverage existing data resources, including the in vivo effects data contained in ECOTOX, and the potential advantages of computational approaches to predict chemical hazard and species susceptibility. In addition to mapping species and chemical identifiers contained in the ECOTOX database to validated, unique keys used in other data systems, this work aims to lay the groundwork for computational modeling of phenotype effects, species sensitivity and putative adverse outcome pathway by mapping ECOTOX codes to ontology classes.
Description:
The US Environmental Protection Agency’s Ecotoxicology (ECOTOX) Knowledgebase contains more than 30 years of reported single chemical toxicity effects data on aquatic and terrestrial organisms. Approximately 900,000 test results covering more than 11,000 chemicals and 12,000 species are available in ECOTOX. While the database is currently used by many sectors for a variety of purposes, a future goal is to allow for computational modeling of the data to identify novel adverse outcome pathways and networks, and assist in predicting chemical hazard and species sensitivity. One obstacle is that ECOTOX captures the study designs and test results using author-reported descriptions, resulting in more than 4000 codes. Relationships among these codes are often not apparent in the current design (e.g., aryl hydrocarbon hydrolase and cytochrome P450 1A), and some codes are uniquely specific to the study of its derivation (e.g., 3rd generation male). To enhance the query capability of the data within and external to the ECOTOX knowledgebase, and to prepare for future computational functionality, the ECOTOX codes were mapped to existing biological ontology classes. A Java-based lookup tool was developed using the ontology browser BioPortal (https://bioportal.bioontology.org/) REST API to semi-automate the code mapping. This tool was designed to allow for batch processing and to make use of BioPortal’s annotator and recommender functions so that all ontological class identifiers relevant to a particular ECOTOX term would be returned and specific ontologies recommended. Using this approach, the majority of the ECOTOX codes were mapped to ontological class identifiers; some terms required multiple identifiers to properly describe them. A set of unmapped terms unique to the ECOTOX database were also identified. Manual curation of the results was also conducted to ensure proper context for the mapped classes.