Linking rare and common disease: mapping clinical disease-phenotypes to ontologies in therapeutic target validation

Abstract

BackgroundThe Centre for Therapeutic Target Validation (CTTV - https://www.targetvalidation.org/) was established to generate therapeutic target evidence from genome-scale experiments and analyses. CTTV aims to support the validity of therapeutic targets by integrating existing and newly-generated data. Data integration has been achieved in some resources by mapping metadata such as disease and phenotypes to the Experimental Factor Ontology (EFO). Additionally, the relationship between ontology descriptions of rare and common diseases and their phenotypes can offer insights into shared biological mechanisms and potential drug targets. Ontologies are not ideal for representing the sometimes associated type relationship required. This work addresses two challenges; annotation of diverse big data, and representation of complex, sometimes associated relationships between concepts.MethodsSemantic mapping uses a combination of custom scripting, our annotation tool ‘Zooma’, and expert curation. Disease-phenotype associations were generated using literature mining on Europe PubMed Central abstracts, which were manually verified by experts for validity. Representation of the disease-phenotype association was achieved by the Ontology of Biomedical AssociatioN (OBAN), a generic association representation model. OBAN represents associations between a subject and object i.e., disease and its associated phenotypes and the source of evidence for that association. The indirect disease-to-disease associations are exposed through shared phenotypes. This was applied to the use case of linking rare to common diseases at the CTTV.ResultsEFO yields an average of over 80 % of mapping coverage in all data sources. A 42 % precision is obtained from the manual verification of the text-mined disease-phenotype associations. This results in 1452 and 2810 disease-phenotype pairs for IBD and autoimmune disease and contributes towards 11,338 rare diseases associations (merged with existing published work [Am J Hum Genet 97:111-24, 2015]). An OBAN result file is downloadable at http://sourceforge.net/p/efo/code/HEAD/tree/trunk/src/efoassociations/. Twenty common diseases are linked to 85 rare diseases by shared phenotypes. A generalizable OBAN model for association representation is presented in this study.ConclusionsHere we present solutions to large-scale annotation-ontology mapping in the CTTV knowledge base, a process for disease-phenotype mining, and propose a generic association model, ‘OBAN’, as a means to integrate disease using shared phenotypes.AvailabilityEFO is released monthly and available for download at http://www.ebi.ac.uk/efo/.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0051-7) contains supplementary material, which is available to authorized users.

Open Targets: a platform for therapeutic target identification and validation

“…The target can be a gene, transcript or protein (or indeed in principle any biomolecule) defined by standard nomenclature, while the disease is described by ontology terms from the experimental factor ontology (EFO) (17). The evidence is described in the Open Biomedical AssociatioN (OBAN) representation (18) and makes use of the Evidence Code Ontology (ECO) (19) (Figure 2) that in turn is part of EFO. For example, a similar approach has been used in nanopublications (20).…”

“…To enable this we have started mapping several existing ontologies like ORDO (Orphanet Rare Disease Ontology (24)), DO (disease ontology, (25)) and HPO (Human Phenotype Ontology,(26)) or widely-used terminologies like MeSH (http://www.ncbi.nlm.nih.gov/mesh) and OMIM (27) to EFO, and we incorporate new human disease terms as necessary. This is achieved by including existing ontology concepts or by cross-referencing external concepts as described in (18). In the platform, we separate human diseases from phenotypes and we use existing HPO or MP (Mammalian Phenotype, (28)) terms to represent phenotypes.…”

Knowledge Representation and Management, It’s Time to Integrate!

“…The section editors achieved a first selection of 100 papers based on titles and abstracts. After a second review of this set of papers, a selection of 15 candidate best papers was established [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Five reviewers reviewed these pre-selected papers to select the best four final papers [6][7][8][9].…”

Thematic issue of the Second combined Bio-ontologies and Phenotypes Workshop

“…Webulous is used to create terms in EFO for the work described by Sarntivijai et al [6]. In the context of the Centre for Therapeutic Target Validation (CTTV), they aim to represent disease-phenotype associations, with the objective of linking rare and common diseases to enable identification of potential therapeutic (drug) targets.…”