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Evolutionary GenomicsStatistical and Computational Methods
Type of publication
Peer-reviewed
Publikationsform
Book (peer-reviewed)
Author
Sima Ana Claudia, Stockinger Kurt, de Farias Tarcisio Mendes, Gil Manuel,
Project
Bio-SODA: Enabling Complex, Semantic Queries to Bioinformatics Databases through Intuitive Searching over Data
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Book (peer-reviewed)
Publisher
Springer New York, New York NY
ISBN
978-1-4939-9073-3
DOI
10.1007/978-1-4939-9074-0_22
Open Access
URL
http://doi.org/10.1007/978-1-4939-9074-0
Type of Open Access
Publisher (Gold Open Access)
Abstract
Biological databases are growing at an exponential rate, currently being among the major producers of Big Data, almost on par with commercial generators, such as YouTube or Twitter. While traditionally biological databases evolved as independent silos, each purposely built by a different research group in order to answer specific research questions; more recently significant efforts have been made toward integrating these heterogeneous sources into unified data access systems or interoperable systems using the FAIR principles of data sharing. Semantic Web technologies have been key enablers in this process, opening the path for new insights into the unified data, which were not visible at the level of each independent database. In this chapter, we first provide an introduction into two of the most used database models for biological data: relational databases and RDF stores. Next, we discuss ontology-based data integration, which serves to unify and enrich heterogeneous data sources. We present an extensive timeline of milestones in data integration based on Semantic Web technologies in the field of life sciences. Finally, we discuss some of the remaining challenges in making ontology-based data access (OBDA) systems easily accessible to a larger audience. In particular, we introduce natural language search interfaces, which alleviate the need for database users to be familiar with technical query languages. We illustrate the main theoretical concepts of data integration through concrete examples, using two well-known biological databases: a gene expression database, Bgee, and an orthology database, OMA.
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