Answering scienti c questions with linked European nanosafety data

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Nanomaterials are increasingly used in healthcare and consumer products. The European community seeks solutions to assess the safety of these materials with experimental research data. Ideally, read across and predictive toxicology approaches can then be used to answer questions if a class of metal oxides is genotoxic or not. If successful, this will replace animal testing in bringing new nanomaterials to the market.

The eNanoMapper project (http://enanomapper.net/) is an FP7 project developing an ontology and database solutions for the data generated in the EU NanoSafety Cluster [ 2, 3 ]. This includes extracts of experimental data from, for example, cell line experiments, environmental toxicity studies, and highthroughput screening results. More important, however, is that this data is no longer static but can be queried and analysed. That is, to make the best use of this data, integration with other life science databases is needed, such as protein sequence database like Uniprot and compound databases such as ChEMBL [ 7 ], UniProt [ 5 ] and PubChem [ 1 ]. Doing so allows us to test scienti c hypotheses such as about the genotoxicity of metal oxides, whether chemically similar nanomaterials have similar bioactivities, or whether protein coronas contain preferably proteins involved in speci c biological processes.

Semantic Web standards are an increasingly central interoperability layer linking experimental data to scienti c knowledge. eNanoMapper has been working on extending the semantics of the database software to import and export data in a serialization based on the Resource Description Framework (RDF) and the eNanoMapper ontology. The RDF data is made available as dereferenceable data and via a SPARQL endpoint (https://sparql.enanomapper.net/) and with a graphical query interface (https://query.enanomapper.net/). These technologies are then used to support the research data management in the community. First, data completeness [ 4 ] is checked by using SPARQL queries, thereby highlighting missing data, and allowing support of pattern recognition [ 6 ]. Second, the scienti c questions prede ned by the eNanoMapper project, such as mentioned earlier in this abstract, are supported by SPARQL queries aggregating the relevant data. Finally, the eNanoMapper RDF is enriched with links to other Linked Open Data Cloud resources (e.g. ChEMBL, PubChem) to support further nanosafety research.

Source code for various components of this work are available from GitHub at https://github.com/enanomapper/.

References

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