Ontology-driven indexing of public datasets for translational bioinformatics

Abstract

The volume of publicly available genomic scale data is increasing. Genomic datasets in public repositories are annotated with free-text fields describing the pathological state of the studied sample. These annotations are not mapped to concepts in any ontology, making it difficult to integrate these datasets across repositories. We have previously developed methods to map text-annotations of tissue microarrays to concepts in the NCI thesaurus and SNOMED-CT. In this work we generalize our methods to map text annotations of gene expression datasets to concepts in the UMLS. We demonstrate the utility of our methods by processing annotations of datasets in the Gene Expression Omnibus. We demonstrate that we enable ontology-based querying and integration of tissue and gene expression microarray data. We enable identification of datasets on specific diseases across both repositories. Our approach provides the basis for ontology-driven data integration for translational research on gene and protein expression data. Based on this work we have built a prototype system for ontology based annotation and indexing of biomedical data. The system processes the text metadata of diverse resource elements such as gene expression data sets, descriptions of radiology images, clinical-trial reports, and PubMed article abstracts to annotate and index them with concepts from appropriate ontologies. The key functionality of this system is to enable users to locate biomedical data resources related to particular ontology concepts.

Figure 1

Architecture of our prototype system comprising of different levels. The figure shows the architecture of the prototype consisting of different levels (0). The resource level, provides and abstraction for elements (E1...En) of public biomedical resources (such as GEO and PubMed). The annotation level, uses a concept recognition tool called mgrep (developed by Univ. of Michigan) to annotate (or tag) resource elements with terms from a dictionary; constructed by including all the concept names and synonyms from a set of ontologies accessible to the ontology level. The annotation tables contain information of the form "element E was annotated with concept T in context C". At the index level, a global index combines all the annotations to index them by ontology concepts and provides information for the form "Concept T annotates elements E1, E2,..., En". See main text for details.

Figure 2

User interface within BioPortal. The figure shows the view seen by a user browsing the NCIT in BioPortal and selecting an ontology concept (in this case, Hepatocellular carcinoma). The user can see the numbers of online resource elements that relate directly to that concept (and the concepts that it subsumes). The interface allows the user to directly access the original elements that are associated with Hepatocellular carcinoma for each of the indexed resources.