Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 May 2;7:23.
doi: 10.1186/s13326-016-0068-y. eCollection 2016.

OBIB-a Novel Ontology for Biobanking

Affiliations
Free PMC article

OBIB-a Novel Ontology for Biobanking

Mathias Brochhausen et al. J Biomed Semantics. .
Free PMC article

Abstract

Background: Biobanking necessitates extensive integration of data to allow data analysis and specimen sharing. Ontologies have been demonstrated to be a promising approach in fostering better semantic integration of biobank-related data. Hitherto no ontology provided the coverage needed to capture a broad spectrum of biobank user scenarios.

Methods: Based in the principles laid out by the Open Biological and Biomedical Ontologies Foundry two biobanking ontologies have been developed. These two ontologies were merged using a modular approach consistent with the initial development principles. The merging was facilitated by the fact that both ontologies use the same Upper Ontology and re-use classes from a similar set of pre-existing ontologies.

Results: Based on the two previous ontologies the Ontology for Biobanking (http://purl.obolibrary.org/obo/obib.owl) was created. Due to the fact that there was no overlap between the two source ontologies the coverage of the resulting ontology is significantly larger than of the two source ontologies. The ontology is successfully used in managing biobank information of the Penn Medicine BioBank.

Conclusions: Sharing development principles and Upper Ontologies facilitates subsequent merging of ontologies to achieve a broader coverage.

Keywords: Biobanking; Biorepository; Ontologies; Terminology.

Figures

Fig. 1
Fig. 1
Representation of edta_plasma, buffy_edta, nacit_plasma, buffy_nacit, plasma, and buffy specimens according to the OBIB strategy. Blue boxes represent classes; red boxes represent individuals; red arrows represent rdfs:subClassOf; green arrows represent rdf:type; blue arrows represent OWL object properties (the labels are specified). While all OWL object properties link instance to instance, in this figure there are object properties connecting OWL classes to each other. This represents a property restriction on the source class with existential quantification (all-some restriction)
Fig. 2
Fig. 2
Selection of central classes of OBIB and their superclasses. The leftmost four BFO classes are subclasses of further BFO classes which are not shown here for readability
Fig. 3
Fig. 3
The process used for building the prototype RDF search system to answer the Penn Medicine Biobank case/control competency question. 1. Semantic Modeling-Ontology models are developed to model the semantics of the relational data and any OBO ontologies that are relevant to the data sources and potential queries. 2. Data Mapping and Instantiation-The models developed in step 1 are used to write mapping files to concretely map the relational data as RDF. Software tools to use these maps to instantiate the relational data as RDF data. 3. Domain Knowledge Linking-The instantiated RDF data and any relevant OBO Foundry Ontologies are loaded into a graph database. 4. Querying and Testing-Queries over the graph data can be created by referencing the OBIB model. To test, equivalent queries against the graph data and relational data are constructed and run to ensure data correctness

Similar articles

See all similar articles

Cited by 4 articles

References

    1. Cho SY, Hong EJ, Nam JM, Han B, Chu C, Park O. Opening of the national biobank of Korea as the infrastructure of future biomedical science in Korea. Osong Public Health Res Perspect. 2012;3:177–84. doi: 10.1016/j.phrp.2012.07.004. - DOI - PMC - PubMed
    1. Watts G. UK Biobank opens it data vaults to researchers. BMJ. 2012;344 doi: 10.1136/bmj.e2459. - DOI - PubMed
    1. Yuille M, van Ommen GJ, Bréchot C, et al. Biobanking for Europe. Brief Bioinform. 2008;9:14–24. doi: 10.1093/bib/bbm050. - DOI - PubMed
    1. Pang C, Hendriksen D, Dijkstra M, et al. BiobankConnect: software to rapidly connect data elements for pooled analysis across biobanks using ontological and lexical indexing. J Am Med Inform Assoc. 2015;22:65–75. - PMC - PubMed
    1. Izzo M, Mortola F, Arnulfo G, Fato MM, Varesio L. A digital repository with an extensible data model for biobanking and genomic analysis management. BMC Genomics. 2014;15(Suppl 3):S3. doi: 10.1186/1471-2164-15-S3-S3. - DOI - PMC - PubMed

MeSH terms

LinkOut - more resources

Feedback