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      Linked open drug data for pharmaceutical research and development

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          Abstract

          There is an abundance of information about drugs available on the Web. Data sources range from medicinal chemistry results, over the impact of drugs on gene expression, to the outcomes of drugs in clinical trials. These data are typically not connected together, which reduces the ease with which insights can be gained. Linking Open Drug Data (LODD) is a task force within the World Wide Web Consortium's (W3C) Health Care and Life Sciences Interest Group (HCLS IG). LODD has surveyed publicly available data about drugs, created Linked Data representations of the data sets, and identified interesting scientific and business questions that can be answered once the data sets are connected. The task force provides recommendations for the best practices of exposing data in a Linked Data representation. In this paper, we present past and ongoing work of LODD and discuss the growing importance of Linked Data as a foundation for pharmaceutical R&D data sharing.

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          The Universal Protein Resource (UniProt) in 2010

          emmanuel boutet, Claire O'Donovan, Amos Bairoch (2009)
          The primary mission of UniProt is to support biological research by maintaining a stable, comprehensive, fully classified, richly and accurately annotated protein sequence knowledgebase, with extensive cross-references and querying interfaces freely accessible to the scientific community. UniProt is produced by the UniProt Consortium which consists of groups from the European Bioinformatics Institute (EBI), the Swiss Institute of Bioinformatics (SIB) and the Protein Information Resource (PIR). UniProt is comprised of four major components, each optimized for different uses: the UniProt Archive, the UniProt Knowledgebase, the UniProt Reference Clusters and the UniProt Metagenomic and Environmental Sequence Database. UniProt is updated and distributed every 3 weeks and can be accessed online for searches or download at http://www.uniprot.org.
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            Bio2RDF: towards a mashup to build bioinformatics knowledge systems.

            Francois Belleau, Marc-Alexandre Nolin, Nicole Tourigny … (2008)
            Presently, there are numerous bioinformatics databases available on different websites. Although RDF was proposed as a standard format for the web, these databases are still available in various formats. With the increasing popularity of the semantic web technologies and the ever growing number of databases in bioinformatics, there is a pressing need to develop mashup systems to help the process of bioinformatics knowledge integration. Bio2RDF is such a system, built from rdfizer programs written in JSP, the Sesame open source triplestore technology and an OWL ontology. With Bio2RDF, documents from public bioinformatics databases such as Kegg, PDB, MGI, HGNC and several of NCBI's databases can now be made available in RDF format through a unique URL in the form of http://bio2rdf.org/namespace:id. The Bio2RDF project has successfully applied the semantic web technology to publicly available databases by creating a knowledge space of RDF documents linked together with normalized URIs and sharing a common ontology. Bio2RDF is based on a three-step approach to build mashups of bioinformatics data. The present article details this new approach and illustrates the building of a mashup used to explore the implication of four transcription factor genes in Parkinson's disease. The Bio2RDF repository can be queried at http://bio2rdf.org.
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              Chem2Bio2RDF: a semantic framework for linking and data mining chemogenomic and systems chemical biology data

              Bin Chen, Xiao Dong, Dazhi Jiao … (2010)
              Background Recently there has been an explosion of new data sources about genes, proteins, genetic variations, chemical compounds, diseases and drugs. Integration of these data sources and the identification of patterns that go across them is of critical interest. Initiatives such as Bio2RDF and LODD have tackled the problem of linking biological data and drug data respectively using RDF. Thus far, the inclusion of chemogenomic and systems chemical biology information that crosses the domains of chemistry and biology has been very limited Results We have created a single repository called Chem2Bio2RDF by aggregating data from multiple chemogenomics repositories that is cross-linked into Bio2RDF and LODD. We have also created a linked-path generation tool to facilitate SPARQL query generation, and have created extended SPARQL functions to address specific chemical/biological search needs. We demonstrate the utility of Chem2Bio2RDF in investigating polypharmacology, identification of potential multiple pathway inhibitors, and the association of pathways with adverse drug reactions. Conclusions We have created a new semantic systems chemical biology resource, and have demonstrated its potential usefulness in specific examples of polypharmacology, multiple pathway inhibition and adverse drug reaction - pathway mapping. We have also demonstrated the usefulness of extending SPARQL with cheminformatics and bioinformatics functionality.
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Cheminform
                Title: Journal of Cheminformatics
                Publisher: BioMed Central
                ISSN (Electronic): 1758-2946
                Publication date Collection: 2011
                Publication date (Electronic): 16 May 2011
                Volume: 3
                Page: 19
                Affiliations
                [1 ]Section for Medical Expert and Knowledge-Based Systems, Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
                [2 ]Information Retrieval Facility (IRF), Vienna, Austria
                [3 ]Digital Enterprise Research Institute (DERI), National University of Ireland Galway, IDA Business Park, Lower Dangan, Galway, Ireland
                [4 ]Web-based Systems Group, Freie Universität Berlin, Berlin, Germany
                [5 ]Entagen, LLC, Second Floor, 44 Merrimac Street, Newburyport, MA 01950, USA
                [6 ]H. Lundbeck A/S, Copenhagen, Denmark
                [7 ]Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden
                [8 ]Department of Medical Informatics, Stony Brook University School of Medicine, Stony Brook, New York, USA
                [9 ]University of Amsterdam, Amsterdam, The Netherlands
                [10 ]Leiden University Medical Center, Leiden, The Netherlands
                [11 ]W3C, Cambridge, MA, USA
                [12 ]Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
                [13 ]W3C HCLSIG. W3C, Cambridge, MA, USA
                [14 ]Johnson & Johnson Pharmaceutical Research & Development, L.L.C., Radnor, USA
                Article
                Publisher ID: 1758-2946-3-19
                DOI: 10.1186/1758-2946-3-19
                PMC ID: 3121711
                PubMed ID: 21575203
                SO-VID: 9c36ab69-cd6a-478f-9147-7c7b1edf2c74
                Copyright © Copyright ©2011 Samwald et al; licensee Chemistry Central Ltd.
                License:

                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.

                History
                Date received : 8 December 2010
                Date accepted : 16 May 2011
                Categories
                Subject: Preliminary Communication

                ScienceOpen disciplines: Chemoinformatics
                Data availability:
                ScienceOpen disciplines: Chemoinformatics

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