Motivation: Resource description framework (RDF) is an emerging technology for describing, publishing and linking life science data. As a major provider of bioinformatics data and services, the European Bioinformatics Institute (EBI) is committed to making data readily accessible to the community in ways that meet existing demand. The EBI RDF platform has been developed to meet an increasing demand to coordinate RDF activities across the institute and provides a new entry point to querying and exploring integrated resources available at the EBI. Availability: http://www.ebi.ac.uk/rdf Contact: jupp@ebi.ac.u

Birney, Ewan

Bolleman, Jerven

Brandizi, Marco

Davies, Mark

Garcia, Leyla

Gaulton, Anna

Gehant, Sebastien

Jenkinson, Andrew M.

Jupp, Simon

Laibe, Camille

Le Novère, Nicolas

Malone, James

Martin, Maria

Parkinson, Helen

Redaschi, Nicole

Wimalaratne, Sarala M.

RERO DOC Digital Library

Vol. 30 no. 9 2014, pages 1338–1339BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btt765Databases and ontologies Advance Access publication January 11, 2014The EBI RDF platform: linked open data for the life sciencesSimon Jupp1,*, James Malone1, Jerven Bolleman2, Marco Brandizi1, Mark Davies1,Leyla Garcia1, Anna Gaulton1, Sebastien Gehant2, Camille Laibe1, Nicole Redaschi2,Sarala M. Wimalaratne1, Maria Martin1, Nicolas Le Nove`re1, Helen Parkinson1, Ewan Birney1and Andrew M. Jenkinson11European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust GenomeCampus, Hinxton, Cambridge CB10 1SD, UK and 2SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire,1211 Geneve, SwitzerlandAssociate Editor: Jonathan WrenABSTRACTMotivation: Resource description framework (RDF) is an emergingtechnology for describing, publishing and linking life science data.As a major provider of bioinformatics data and services, theEuropean Bioinformatics Institute (EBI) is committed to making datareadily accessible to the community in ways that meet existingdemand. The EBI RDF platform has been developed to meet anincreasing demand to coordinate RDF activities across the instituteand provides a new entry point to querying and exploring integratedresources available at the EBI.Availability: http://www.ebi.ac.uk/rdfContact: jupp@ebi.ac.ukReceived on October 10, 2013; revised on November 22, 2013;accepted on December 23, 20131 INTRODUCTIONThe European Bioinformatics Institute (EBI) is the largestbioinformatics resource provider in Europe. Our databases areaccessible via dedicated interfaces, web services, data downloadand (in a few cases) direct database access. Modern research inthe life sciences necessitates an understanding of data at manydifferent levels: multi-omics, from cells to biological systems,across many different species and studying many different experi-mental conditions. The biology underpinning these researchquestions is intrinsically connected, yet data are often collectedand stored in technology or domain-specific repositories.Efforts in the Semantic Web community are already beginningto invest in technology that enables data to be readily integrated(Belleau et al., 2008; Katayama et al., 2010; Marshall et al.,2008). One method used among the Semantic Web communityis using the W3C’s resource description framework (RDF) modelto represent data. RDF provides a common mechanism fordescribing data and querying data using SPARQL.To better serve complex research questions across resources,and to meet an increased demand on the EBI to produce RDF,we have developed an RDF platform. The aim of such a plat-form is to offer users the ability to ask questions using multipleconnected resources that share common identifiers and have acommon format (RDF) and query interface (SPARQL). Thisplatform complements other existing data access modes such asour Web site and RESTful web services, but additionally con-tains explicit links between the different data resources. Thisenables a single query to be asked across multiple distributeddatasets and across a range of biological domains. This approachhas been applied for the following EBI resources: Gene Expres-sion Atlas (Kapushesky et al., 2012), ChEMBL (Gaulton et al.,2011), BioModels (Li et al., 2010), Reactome (Matthews et al.,2008), BioSamples (Gostev et al., 2012) and also includes a col-laboration with the UniProt Consortium to deliver UniProtRDF (Redaschi and UniProt Consortium, 2009).2 METHODSThe RDF platform presents a coordinated effort to bring together RDFresources from multiple services and databases at the EBI. The develop-ment of the platform began by collecting requirements from both ascientific and a technical perspective. The scientific requirements weregathered as a series of use cases and competency questions collectedfrom research scientists and users of EBI services. In particular, wewere looking for questions that required data to be integrated from mul-tiple resources and that are not trivial to answer with our existing infra-structure due to the disparate nature of the data. These questions wereused to identify points of integration between resources. The scientific usecases informed the technical requirements on what infrastructure, interms of both software and hardware, would be needed to deliver astable and scalable platform. Given RDF technology is still maturing,there are open questions on how to deliver such a platform on this scale;our existing infrastructure is delivered after evaluation of various tech-nologies that will be the subject of another paper.Data from UniProt, ChEMBL, Reactome and BioModels representscurated knowledge from protein sequence and function, bio-active mol-ecules and their targets, to biochemical pathways and computationalmodels of molecular interactions. The Gene Expression Atlas databaseprovides differential gene expression data from a variety of samples thatare highly annotated and curated using the Experimental Factor Ontology(EFO) (Malone et al., 2010). Generating linked RDF for these resourcesprovides a new entry point for exploring the data, such as putting geneexpression in the context of protein function, pathways and drug targets.An outline of how resources are connected is shown in Figure 1.The graph-based nature of the RDF data model provides a natural fitfor explicitly publishing how data are connected. In RDF, resources areidentified using uniform resource identifiers (URIs), which provide aweb-based global identification system. Guidelines for minting newURIs for EBI resources were established using the new rdf.ebi.ac.uk*To whom correspondence should be addressed. The Author 2013. Published by Oxford University Press.This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercialre-use, please contact journals.permissions@oup.comdomain (details can be found at http://www.ebi.ac.uk/rdf/documentation/uris-ebi-data). Canonical URIs are used when existing databases, such asUniProt, already provide stable URIs. In cases where no canonical URIsare provided by external resources, the Identifiers.org registry of scientificidentifiers (Juty et al., 2012) was used to provide a referencing URI. Aspart of the URI strategy, every effort has been made to ensure all EBIRDF datasets only use URIs that can be dereferenced using http, sup-porting content negotiation for human-orientated HTML views, along-side machine processable versions in various RDF syntaxes.Using common URI schemes assists data integration with RDF. Inaddition, ontologies provide a mechanism to semantically describe thedata, and the OWL ontology language can be serialized in RDF. The EBImakes extensive use of ontologies to annotate data, however, the richnessof these annotations is rarely available in native RDF for exploitation byexternal applications. The EBI RDF platform adopts a range of commonvocabularies and ontologies to annotate data. The ontologies used spancommon biomedical terminologies such as the Gene Ontology, ChemicalEntities of Biological Interest, UBERON, Cell Type Ontology, BiologicalPathways Exchange, EFO and more. Additionally, we adopted metadatastandards for describing datasets and provenance such as Dublin Core,Data Catalog Vocabulary and Vocabulary of Interlinked Datasets.3 RESULTSComplete dumps of the RDF data are available via FTP down-loads. These are published in line with existing production andrelease cycles, ensuring the most up-to-date data are readily avail-able. We are also using triple store technology to index the RDFfiles and make them available for querying and exploration viaSPARQL endpoints and our linked data browser. The underlyinginfrastructure at the EBI is built on open source triple store tech-nology provided by OpenLink, (http://www.openlinksw.com/),whereas the UniProt data are served by the SIB’s Vital-IT HPCplatform using technology from OntoText (http://www.ontotext.com/). We developed LODEStar (http://www.ebi.ac.uk/fgpt/sw/lodestar/) as a generic SPARQL endpoint and linked data brow-ser to provide a consistent interface and some enhanced function-ality for querying and browsing EBI-based datasets.In addition to providing access to the underlying data, anequally important component of the platform is the Web siteat http://www.ebi.ac.uk/rdf that provides an entry point to dis-cover all RDF resources being served by the EBI. This site in-cludes documentation on how to find the datasets and providesexamples of how to query the data using the SPARQL endpoints(http://www.ebi.ac.uk/rdf/example-sparql-queries). We also pro-vide examples showing developers how they can use theSPARQL API programmatically from common programmingenvironments like Perl, Java and R.4 CONCLUSIONThe EBI RDF platform allows explicit links to be made betweendatasets using shared semantics from standard ontologies andvocabularies, facilitating a greater degree of data integration.SPARQL provides a standard query language for queryingRDF data. Data that have been annotated using ontologies,such as EFO and the Gene Ontology, enable data integrationwith other community datasets and provides the semantics toperform rich queries. Publishing these datasets as RDF alongwith their ontologies provides both the syntactic and semanticintegration of data long promised by semantic web technologies.As the trend toward publishing life science data in RDF in-creases, we anticipate a rise in the number of applications con-suming such data. This is evident in efforts such as the OpenPHACTS platform (http://www.openphacts.org) and theAtlasRDF-R package (https://github.com/jamesmalone/AtlasRDF-R). Our aim is that the EBI RDF platform enablessuch applications to be built by releasing production quality ser-vices with semantically described RDF to enable pertinent bio-medical use cases to be addressed.ACKNOWLEDGEMENTSThe authors thank the EBI Industry program, MichelDumontier and staff at OpenLink and OntoText.Funding: EMBL and European Union grants Diachron (601043)and BioMedBridges (284209). OpenPHACTS (115191). NationalInstitutes of Health grants [1U41HG006104-03] and NCBO[U54-HG004028] and the Swiss Federal Government throughthe Federal Office of Education and Science.Conflict of Interest: none declared.REFERENCESBelleau,F. et al. (2008) Bio2RDF: towards a mashup to build bioinformatics know-ledge systems. J. Biomed. Inform., 41, 706–716.Li,C. et al. (2010) BioModels Database: an enhanced, curated and annotated re-source for published quantitative kinetic models. BMC. Syst. Biol., 4, 92.Gaulton,A. et al. (2011) ChEMBL: a large-scale bioactivity database for drug dis-covery. Nucleic Acids Res., 40, D1100–D1107.Gostev,M. et al. (2012) The BioSample Database (BioSD) at the EuropeanBioinformatics Institute. Nucleic Acids Res., 40, D64–D70.Juty,N. et al. (2012) Identifiers.org and MIRIAMRegistry: community resources toprovide persistent identification. Nucleic Acids Res., 40, D580–D586.Kapushesky,M. et al. (2012) Gene Expression Atlas update–a value-added databaseof microarray and sequencing-based functional genomics experiments. NucleicAcids Res., 40, D1077–D1081.Katayama,T. et al. (2010) The 3rd DBCLS BioHackathon: improving life sciencedata integration with Semantic Web technologies. J. Biomed. Semantics, 4, 6.Malone,J. et al. (2010) Modeling sample variables with an experimental factorontology. Bioinformatics, 26, 1112–1118.Marshall,M.S. et al. (2008) A Prototype Knowledge Base for the Life Sciences.World Wide Web Consortium (W3C) Interest Group Note. http://www.w3.org/TR/hcls-kb/ (11 January 2014, date last accessed).Matthews,L. et al. (2008) Reactome knowledgebase of human biological pathwaysand processes. Nucleic Acids Res., 37 (Suppl. 1), D619–D622.Redaschi,N. (2009) Consortium,UniProt. (2009) UniProt in RDF: Tackling dataintegration and distributed annotation with the semantic web. Available fromNature Precedings http://dx.doi.org/10.1038/npre.2009.3193.1.Fig. 1. Connections between services (boxes) and ontologies (circles). Thegraph illustrates how the data are linked within the RDF platform,enabling queries to span all data. Asterisk: ENSEMBL to UniProt(gray line) mappings are included via expression atlas1339EBI RDF platform

The EBI RDF platform: linked open data for the life sciences

The EBI RDF platform: linked open data for the life sciences

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