Translational research combining orthologous genes and human diseases with the OGOLOD dataset

Miòarro-Giménez, José Antonio; Aranguren, Mikel Egaòa; Villazón-Terrazas, Boris; Fernández-Breis, Jesualdo Tomás

doi:10.3233/sw-130109

Cited by 6 publications

(2 citation statements)

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“…We have used SWIT to generate the integrated OGOLOD Linked Open Dataset [ 58 ]. The first version of OGOLOD was created with the purpose of providing an integrated resource of information about genetic human diseases and orthologous genes, given the increasing interest in orthologs in research [ 59 ].…”

Section: Resultsmentioning

confidence: 99%

Generation of open biomedical datasets through ontology-driven transformation and integration processes

et al. 2016

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BackgroundBiomedical research usually requires combining large volumes of data from multiple heterogeneous sources, which makes difficult the integrated exploitation of such data. The Semantic Web paradigm offers a natural technological space for data integration and exploitation by generating content readable by machines. Linked Open Data is a Semantic Web initiative that promotes the publication and sharing of data in machine readable semantic formats.MethodsWe present an approach for the transformation and integration of heterogeneous biomedical data with the objective of generating open biomedical datasets in Semantic Web formats. The transformation of the data is based on the mappings between the entities of the data schema and the ontological infrastructure that provides the meaning to the content. Our approach permits different types of mappings and includes the possibility of defining complex transformation patterns. Once the mappings are defined, they can be automatically applied to datasets to generate logically consistent content and the mappings can be reused in further transformation processes.ResultsThe results of our research are (1) a common transformation and integration process for heterogeneous biomedical data; (2) the application of Linked Open Data principles to generate interoperable, open, biomedical datasets; (3) a software tool, called SWIT, that implements the approach. In this paper we also describe how we have applied SWIT in different biomedical scenarios and some lessons learned.ConclusionsWe have presented an approach that is able to generate open biomedical repositories in Semantic Web formats. SWIT is able to apply the Linked Open Data principles in the generation of the datasets, so allowing for linking their content to external repositories and creating linked open datasets. SWIT datasets may contain data from multiple sources and schemas, thus becoming integrated datasets.

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Section: Resultsmentioning

confidence: 99%

Generation of open biomedical datasets through ontology-driven transformation and integration processes

et al. 2016

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“…Data heterogeneity challenges have been addressed (at least in part) in the Semantic Web (or Web of Data) field through various recommendations including the Resource Description Framework (RDF) [3]. RDF has been adopted in some scientific communities [8,9,10,11,12], including in our own previous work [13]. While it helps with the heterogeneity challenges, the adoption of RDF exacerbates the volume challenges.…”

Section: Introductionmentioning

confidence: 99%

Toward a data scalable solution for facilitating discovery of science resources

et al. 2014

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Data-intensive science simultaneously derives from and creates the need for large quantities of data. As such, scientists increasingly need to discover and analyze new datasets from diverse sources. Beyond the sheer volume of data, issues posed by the resultant data heterogeneity are often overlooked. We postulate that heterogeneity challenges can be solved (at least in part) with the adoption of the Resource Description Framework (RDF), a graphbased data model. In turn, this requires scalable graph query systems for discovering and analyzing data. Consequently, we investigate GEMS, a graph engine for large-scale clusters. We describe the features of GEMS that make it suitable for answering graph queries and scaling to larger quantities of data. We evaluate GEMS' ability to answer real science-based queries over real-world, curated, science metadata. We also demonstrate GEMS' ability to scale to larger datasets using a benchmark.

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The Orthology Ontology: development and applications

et al. 2016

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BackgroundComputational comparative analysis of multiple genomes provides valuable opportunities to biomedical research. In particular, orthology analysis can play a central role in comparative genomics; it guides establishing evolutionary relations among genes of organisms and allows functional inference of gene products. However, the wide variations in current orthology databases necessitate the research toward the shareability of the content that is generated by different tools and stored in different structures. Exchanging the content with other research communities requires making the meaning of the content explicit.DescriptionThe need for a common ontology has led to the creation of the Orthology Ontology (ORTH) following the best practices in ontology construction. Here, we describe our model and major entities of the ontology that is implemented in the Web Ontology Language (OWL), followed by the assessment of the quality of the ontology and the application of the ORTH to existing orthology datasets. This shareable ontology enables the possibility to develop Linked Orthology Datasets and a meta-predictor of orthology through standardization for the representation of orthology databases. The ORTH is freely available in OWL format to all users at http://purl.org/net/orth.ConclusionsThe Orthology Ontology can serve as a framework for the semantic standardization of orthology content and it will contribute to a better exploitation of orthology resources in biomedical research. The results demonstrate the feasibility of developing shareable datasets using this ontology. Further applications will maximize the usefulness of this ontology.

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Translational research combining orthologous genes and human diseases with the OGOLOD dataset

Cited by 6 publications

References 16 publications

Generation of open biomedical datasets through ontology-driven transformation and integration processes

Generation of open biomedical datasets through ontology-driven transformation and integration processes

Toward a data scalable solution for facilitating discovery of science resources

The Orthology Ontology: development and applications

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