Expanding Adverse Outcome Pathway knowledge by creating AOP-Wiki RDF with semantic annotations to facilitate risk assessment of chemicals
Creators: name: Martens, Marvin
name: Willighagen, Egon
name: Evelo, Chris T.
Publisher: name: figshare
Year: 2019
Subjects: subject: 60102 Bioinformatics
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: 80604 Database Management

description: @page { margin: 0.79in } p { text-indent: 0.24in; margin-bottom: 0.1in; direction: ltr; color: #000000; line-height: 115%; text-align: justify; orphans: 2; widows: 2 } p.western { font-family: "Times", "Times New Roman"; font-size: 10pt; so-language: en-AU } p.cjk { font-family: "PMingLiU", "新細明體", serif; font-size: 10pt; so-language: zh-TW } p.ctl { font-family: "Times", "Times New Roman"; font-size: 10pt; so-language: ar-SA } a:link { color: #000000 } <b>1. Introduction</b> <br> With the ever-growing number of chemicals that require toxicological risk assessment, there is a need for faster, more efficient use of existing data to assemble effective assessment strategies [1]. Therefore, the concept of Adverse Outcome Pathways (AOPs) was introduced [2], a framework to organize existing mechanistic information about toxicological processes into a chain of smaller pieces of knowledge, called Key Events (KEs). These allow the structuring of toxicological knowledge and reduce the effort needed to capture all information before performing risk assessment [2, 3]. In order to facilitate a community effort in gathering toxicological knowledge, the AOP-Wiki was created by the European Commission JRC and the US EPA. To integrate this knowledge base more easily with other resources, we explored the use of semantic web technologies to link AOP-Wiki with other chemical and biological databases. <br> <br> <b>2. Approach</b> <br> The AOP-Wiki provides quarterly permanent downloads for the full database XML (https://aopwiki.org/downloads/). We parsed the AOP-Wiki knowledge with Python 3.5 and the ElementTree XML API and converted it into a semantic web RDF format, which allows for accurate description with ontological annotations, including the AOPO, CHEMINF, and Dublin Core. Chemical compounds are identified in the AOP-Wiki with CAS numbers and biological processes with a variety of ontologies, e.g. GO, Mammalian Phenotype Ontology, and Molecular Interactions ontology. These annotations are used to create Internationalized Resource Identifiers. <br> To integrate and test the RDF, a variety of federated SPARQL queries were written and executed in Blazegraph (build version 2.1.4). <br> <br> <b>3. Results</b> <br> We created an AOP-Wiki RDF scheme and converted the XML into Turtle syntax. The RDF was tested with a variety of SPARQL queries to answer biological question relevant to risk assessment, such as: - What measurement / test-method information is available for a given AOP? - Which of the stressor chemicals on the AOP-Wiki can be linked molecular pathways on WikiPathways? <br> <br> <b>4. Discussion</b> <br> The RDF transformation of AOP-Wiki content can assist in the accessibility and expansion of toxicological knowledge by allowing semantic interoperability. The created RDF of the AOP-Wiki allows the querying and providing of additional information for stressor chemicals, genes, and proteins involved in KEs, the underlying molecular pathways, but also for the applicability of AOPs by cell types or species. This semantic approach allows novel ways to explore the rapidly growing AOP knowledge with every new publication related to toxicological studies. <br> There is work in progress on a Virtuoso SPARQL endpoint Docker image to simplify the use of the data, and integrate the database in the OpenRiskNet e-infrastructure to provide AOP knowledge useful for automated risk assessment workflows.