Abstract

BackgroundDespite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson’s disease. In the area of Parkinson’s research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism.MethodsThe Parkinson’s disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology.ResultsThe Parkinson’s disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson’s knowledge map. The ontology was able to answer specific questions related to Parkinson’s when evaluated by experts. Finally, the added value of the Parkinson’s disease ontology is demonstrated by ontology-driven modeling of PINK1 and re-annotation of gene expression datasets relevant to Parkinson’s disease.ConclusionsParkinson’s disease ontology delivers the knowledge domain of Parkinson’s disease in a compact, computer-readable form, which can be further edited and enriched by the scientific community and also to be used to construct, represent and automatically extend Parkinson’s-related computable models. A practical version of the Parkinson’s disease ontology for browsing and editing can be publicly accessed at http://bioportal.bioontology.org/ontologies/PDON.Electronic supplementary materialThe online version of this article (doi:10.1186/s12976-015-0017-y) contains supplementary material, which is available to authorized users.

Highlights

  • Despite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson’s disease

  • A practical version of the Parkinson’s disease ontology for browsing and editing can be publicly accessed at http://bioportal.bioontology.org/ontologies/PDON

  • For the construction of a large, integrative knowledge base on neurodegenerative diseases, PDON can be used for metadata annotation of various omics data sets available in the public domain

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Summary

Introduction

Despite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson’s disease. In the area of Parkinson’s research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism. Several attempts at elucidating the molecular etiology of PD have generated large omics data sets [2]. The emerging systems view on the pathology of neurodegenerative diseases (NDDs) requires an efficient strategy to aggregate, standardize, represent, and communicate biomedical information through controlled vocabularies and ontologies [3]. Ontologies are the basis for automated reasoning [5], for large-scale annotation of entire genomes [6, 7], for data mining in microarray data [8], for prediction of biomolecular interactions [9], and for semantic and ontological search in poorly structured information sources [10, 11]

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