Intégration de données multi-échelles et extraction de connaissances en agronomie : exemples et perspectives. (Multi-scale Data Integration and Knowledge Extraction in Agronomy: Examples and Prospects)

Background: In recent years, a large amount of “-omics” data have been produced. However, these data are stored in many different species-specific databases that are managed by different institutes and laboratories. Biologists often need to find and assemble data from disparate sources to perform certain analyses. Searching for these data and assembling them is a time-consuming task. The Semantic Web helps to facilitate interoperability across databases. A common approach involves the development of wrapper systems that map a relational database schema onto existing domain ontologies. However, few attempts have been made to automate the creation of such wrappers. Results: We developed a framework, named BioSemantic, for the creation of Semantic Web Services that are applicable to relational biological databases. This framework makes use of both Semantic Web and Web Services technologies and can be divided into two main parts: (i) the generation and semi-automatic annotation of an RDF view; and (ii) the automatic generation of SPARQL queries and their integration into Semantic Web Services backbones. We have used our framework to integrate genomic data from different plant databases. Conclusions: BioSemantic is a framework that was designed to speed integration of relational databases. We present how it can be used to speed the development of Semantic Web Services for existing relational biological databases. Currently, it creates and annotates RDF views that enable the automatic generation of SPARQL queries. Web Services are also created and deployed automatically, and the semantic annotations of our Web Services are added automatically using SAWSDL attributes. BioSemantic is downloadable at http://southgreen.cirad.fr/? q=content/Biosemantic. Background 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 [1,2]. However, biological data integration faces challenges because of syntactic and semantic heterogeneity. In their reviews, Stein LD [3] and Goble C & Stevens R [4] 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”. The SW currently provides recommendations (RDF [5], SPARQL [6], OWL [7]) for enabling interoperability across databases. Furthermore, major plant databases, such as TAIR [8], Gramene [9], IRIS [10], MaizeGDB [11] and GnpIS [12], annotate their data using ontology terms to link different datasets and to facilitate queries across multiple databases. Guided by life science integration studies * Correspondence: julien.wollbrett@cirad.fr; manuel.ruiz@cirad.fr CIRAD, UMR AGAP, Montpellier F-34398, France Institut de Biologie Computationnelle, 95 rue de la Galéra, 34095, Montpellier, France Full list of author information is available at the end of the article © 2013 Wollbrett et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Wollbrett et al. BMC Bioinformatics 2013, 14:126 http://www.biomedcentral. com/1471-2105/14/126