Bringing Chemical Data onto the Semantic Web

Taylor, Kieron; Gledhill, Robert J.; Essex, Jonathan W.; Frey, Jeremy G.; Harris, Steven; Roure, David De

doi:10.1021/ci050378m

Cited by 51 publications

(47 citation statements)

References 17 publications

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“…The CombeChem [48,104], Mindswap [49,50], and VIEW [29,69] systems also use a Semantic Web approach for provenance collection and representation. While CombeChem and VIEW use relational RDF stores to manage provenance, Mindswap publishes workflow provenance on the Semantic Web.…”

Section: Storing and Querying Scientific Workflow Provenancementioning

confidence: 99%

RDFProv: A relational RDF store for querying and managing scientific workflow provenance

Chebotko¹,

Lu²,

Fei³

et al. 2010

Data & Knowledge Engineering

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Provenance metadata has become increasingly important to support scientific discovery reproducibility, result interpretation, and problem diagnosis in scientific workflow environments. The provenance management problem concerns the efficiency and effectiveness of the modeling, recording, representation, integration, storage, and querying of provenance metadata. Our approach to provenance management seamlessly integrates the interoperability, extensibility, and inference advantages of Semantic Web technologies with the storage and querying power of an RDBMS to meet the emerging requirements of scientific workflow provenance management. In this paper, we elaborate on the design of a relational RDF store, called RDFPROV, which is optimized for scientific workflow provenance querying and management. Specifically, we propose: i) two schema mapping algorithms to map an OWL provenance ontology to a relational database schema that is optimized for common provenance queries; ii) three efficient data mapping algorithms to map provenance RDF metadata to relational data according to the generated relational database schema, and iii) a schema-independent SPARQL-to-SQL translation algorithm that is optimized on-the-fly by using the type information of an instance available from the input provenance ontology and the statistics of the sizes of the tables in the database. Experimental results are presented to show that our algorithms are efficient and scalable. The comparison with two popular relational RDF stores, Jena and Sesame, and two commercial native RDF stores, AllegroGraph and BigOWLIM, showed that our optimizations result in improved performance and scalability for provenance metadata management. Finally, our case study for provenance management in a real-life biological simulation workflow showed the production quality and capability of the RDFPROV system. Although presented in the context of scientific workflow provenance management, many of our proposed techniques apply to general RDF data management as well.

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Section: Storing and Querying Scientific Workflow Provenancementioning

confidence: 99%

RDFProv: A relational RDF store for querying and managing scientific workflow provenance

Chebotko¹,

Lu²,

Fei³

et al. 2010

Data & Knowledge Engineering

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“…Other EBI ontologies in the chemistry domain are REX [94] and FIX [95], which describe physicochemical processes and methods respectively. Other groups have also reported efforts to model chemical structure [96], reactions [85,86], and laboratory processes [97][98][99]. Further ontologies for the general scientific domain encompass ontologies of the scientific experiment as such [100], as well as a number of upper ontologies, which are suitable for science (e.g., SUMO [101], General Formal Ontology [102]).…”

Section: The Semantic Web Of Polymer Datamentioning

confidence: 99%

Polymer Informatics

Adams

2010

Polymer Libraries

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Polymers are arguably the most important set of materials in common use. The increasing adoption of both combinatorial as well as high-throughput approaches, coupled with an increasing amount of interdisciplinarity, has wrought tremendous change in the field of polymer science. Yet the informatics tools required to support and further enhance these changes are almost completely absent. In the first part of the chapter, a critical analysis of the challenges facing modern polymer informatics is provided. It is argued, that most of the problems facing the field today are rooted in the current scholarly communication process and the way in which chemists and polymer scientists handle and publish data. Furthermore, the chapter reviews existing modes of representing and communicating polymer information and discusses the impact, which the emergence of semantic technologies will have on the way in which scientific and polymer data is published and transmitted. In the second part, a review of the use of informatics tools for the prediction of polymer properties and in silico design of polymers is offered.

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“…Although it has largely been driven by the interests of the pharmaceutical industry whose concerns lie with xenobiotics, it is obvious that the same methods can be applied to the computational systems biology of natural metabolic systems, and we need to integrate the ideas and knowledge of cheminformatics into metabolomics, just as is happening with chemometrics [112]. Recent developments are increasing the richness of the representations that we can exploit [113], and bring the hope of adding chemical structure mining [114] to the emerging possibilities in literature and text mining (e.g. Refs [115][116][117]).…”

Section: Future Directions: Bringing Cheminformatics To Metabolic Sysmentioning

confidence: 99%

Systems biology, metabolic modelling and metabolomics in drug discovery and development

Kell

2006

Drug Discovery Today

266

163

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Unlike signalling pathways, metabolic networks are subject to strict stoichiometric constraints. Metabolomics amplifies changes in the proteome, and represents more closely the phenotype of an organism. Recent advances enable the production (and computer-readable encoding as SBML) of metabolic network models reconstructed from genome sequences, as well as experimental measurements of much of the metabolome. There is increasing convergence between the number of human metabolites estimated via genomics ( 3000) and the number measured experimentally. It is thus both timely, and now possible, to bring these two approaches together as an integrated (if distributed) whole to help understand the genesis of metabolic biomarkers, the progress of disease, and the modes of action, efficacy, off-target effects and toxicity of pharmaceutical drugs. Systems biology and metabolic modelling in the 21st CenturyAlthough there are many individual definitions, most commentators (including this one [1,2]) take it that systems biology involves an iterative interplay between more or less high-throughput and high-content 'wet' experiments, technology development, theory and computational modelling, and that it is the involvement of computational modelling, in particular, in the process that sets systems biology apart from the more traditional and more reductionist molecular biology. Metabolomics illustrates this amply. There is also the view that the perceived decrease in the effectiveness of the target-based drug discovery process [3][4][5], including the still-high levels of attrition [6], means that we must move towards understanding organisms at something more akin to a whole-system level [7][8][9][10][11].The question then arises as to what part of a system one might first beneficially model? Although there has, unsurprisingly, been considerable interest in modelling the major signalling pathways (e.g. Refs [12,13]), there are several reasons why it is timely to turn our attention to the level of small molecule metabolism, which is the focus of this review. Metabolism is more discriminatingIt has long been known, and proven through the formalism of metabolic control analysis [7,10,[14][15][16], that whereas small changes in the concentrations of enzymes (and the transcripts that encode them) have only small effects on the fluxes through metabolic pathways, they have substantial effects on the concentrations of metabolic intermediates. Because the metabolome (nominally the concentrations of 'all' the metabolites measured in a system of interest [17]) is downstream of the proteome, it is thereby 'amplified' both in theory [18] and in practice [19,20] and represents a more sensitive level of organisation than do the macromolecular 'omes for understanding a complex biological system, and the changes in it that might be occasioned by disease or pharmaceutical intervention [21,22]. Metabolic reconstruction is now mature and timelyAn attractive feature for the purposes of modelling is that metabolism, in contrast to signalling pathways, ...

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Bringing Chemical Data onto the Semantic Web

Cited by 51 publications

References 17 publications

RDFProv: A relational RDF store for querying and managing scientific workflow provenance

RDFProv: A relational RDF store for querying and managing scientific workflow provenance

Polymer Informatics

Systems biology, metabolic modelling and metabolomics in drug discovery and development

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