PMC3680174

Article

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Clever generation of rich SPARQL queries from annotated relational schema: application to Semantic Web Service creation for biological databases

Currently, the large amount of plant high-throughput data that have been produced by different laboratories is distributed across many different crop-specific databases. Plant biologists and breeders often need to access several databases to perform tasks such as locating allelic variants for genetic markers in different crop populations and in a given environment or investigating the consequences of a mutation at the transcriptome, proteome, metabolome and phenome levels. The integration of these disparate databases would make complex analyses easier and could also reveal hidden knowledge CiteRef::PMC2483364CiteRef::PMID19933213.

However, biological data integration faces challenges because of syntactic and semantic heterogeneity. In their reviews, Stein LD CiteRef::PMID18714290 and Goble C & Stevens R CiteRef::PMID18358788 provide a fair criticism of the lack of integrated approaches and provide a similar vision for the future, which is that the Semantic Web (SW) can aid in data integration. According to the W3C, “the SW provides a common framework that allows data to be shared and reused across applications, enterprises, and community boundaries”a. The SW currently provides recommendations (RDF CiteRef::W3CRDF, SPARQL CiteRef::W3CSPARQL, OWL CiteRef::W3COWL) for enabling interoperability across databases. Furthermore, major plant databases, such as TAIR CiteRef::PMC2238962, Gramene CiteRef::PMC2238951, IRIS CiteRef::PMC1255983, MaizeGDB CiteRef::PMC2518694 and GnpIS CiteRef::PMC165507, annotate their data using ontology terms to link different datasets and to facilitate queries across multiple databases. Guided by life science integration studies CiteRef::PMID18077472CiteRef::PMC3114010, annotating data with ontologies promotes the development of ontology-driven integration platforms CiteRef::PMC2375972CiteRef::PMC3355756.

In parallel, Web Services (WS) are becoming an increasingly popular way of establishing robust remote access to major bioinformatics resources, such as EMBL-EBI, KEGG and NCBI. WS are virtually platform-independent and are easily reusable. Indeed, analysis and data retrieval WSs can be rapidly combined and integrated into complex workflows.

The common use of the SW and WS standards has the promise of achieving integration and interoperability among the currently disparate bioinformatics resources on the Web CiteRef::Wilkinson, Vandervalk, and McCarthy 2009. There are currently existing efforts to describe Web Services with semantic annotations by using ontologies, such as SSWAP CiteRef::PMC2761904, SADI CiteRef::PMC3212890 and BioMoby CiteRef::PMID18238804. However, none of these approaches are focused on the automation of business logic CiteRef::PMC3040533. The implementation of new Semantic Web Services (SWS) can be time-consuming and requires the developer to know how to manipulate SW and WS standards and to have expertise on the database schema. To our knowledge, there are currently no ongoing efforts in the context of the automation of SWS creation that are both specific to relational databases and based only on W3C standards.