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Sima AC, Mendes de Farias T, Zbinden E, Anisimova M, Gil M, Stockinger H, Stockinger K, Robinson-Rechavi M, Dessimoz C. Enabling semantic queries across federated bioinformatics databases. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2019:5614223. [PMID: 31697362 PMCID: PMC6836710 DOI: 10.1093/database/baz106] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 08/01/2019] [Accepted: 08/02/2019] [Indexed: 11/23/2022]
Abstract
Motivation: Data integration promises to be one of the main catalysts in enabling new insights to be drawn from the wealth of biological data available publicly. However, the heterogeneity of the different data sources, both at the syntactic and the semantic level, still poses significant challenges for achieving interoperability among biological databases. Results: We introduce an ontology-based federated approach for data integration. We applied this approach to three heterogeneous data stores that span different areas of biological knowledge: (i) Bgee, a gene expression relational database; (ii) Orthologous Matrix (OMA), a Hierarchical Data Format 5 orthology DS; and (iii) UniProtKB, a Resource Description Framework (RDF) store containing protein sequence and functional information. To enable federated queries across these sources, we first defined a new semantic model for gene expression called GenEx. We then show how the relational data in Bgee can be expressed as a virtual RDF graph, instantiating GenEx, through dedicated relational-to-RDF mappings. By applying these mappings, Bgee data are now accessible through a public SPARQL endpoint. Similarly, the materialized RDF data of OMA, expressed in terms of the Orthology ontology, is made available in a public SPARQL endpoint. We identified and formally described intersection points (i.e. virtual links) among the three data sources. These allow performing joint queries across the data stores. Finally, we lay the groundwork to enable nontechnical users to benefit from the integrated data, by providing a natural language template-based search interface.
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Affiliation(s)
- Ana Claudia Sima
- ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland.,Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Tarcisio Mendes de Farias
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.,Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland
| | - Erich Zbinden
- ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Maria Anisimova
- ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Manuel Gil
- ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Heinz Stockinger
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Kurt Stockinger
- ZHAW Zurich University of Applied Sciences, Obere Kirchgasse 2, 8400 Winterthur Switzerland
| | - Marc Robinson-Rechavi
- SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.,Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland
| | - Christophe Dessimoz
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland.,Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.,SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.,Department of Genetics, Evolution, and Environment, University College London, Gower St, London WC1E 6BT, UK.,Department of Computer Science, University College London, Gower St, London WC1E 6BT, UK
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Oulas A, Minadakis G, Zachariou M, Sokratous K, Bourdakou MM, Spyrou GM. Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches. Brief Bioinform 2019; 20:806-824. [PMID: 29186305 PMCID: PMC6585387 DOI: 10.1093/bib/bbx151] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 10/17/2017] [Indexed: 02/01/2023] Open
Abstract
Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine.
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Affiliation(s)
- Anastasis Oulas
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George Minadakis
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Margarita Zachariou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Kleitos Sokratous
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - Marilena M Bourdakou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
| | - George M Spyrou
- Bioinformatics European Research Area Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
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