Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis.

Published online

Journal Article

Bio-ontologies provide terminologies for the scientific community to describe biomedical entities in a standardized manner. There are multiple initiatives that are developing biomedical terminologies for the purpose of providing better annotation, data integration and mining capabilities. Terminology resources devised for multiple purposes inherently diverge in content and structure. A major issue of biomedical data integration is the development of overlapping terms, ambiguous classifications and inconsistencies represented across databases and publications. The disease ontology (DO) was developed over the past decade to address data integration, standardization and annotation issues for human disease data. We have established a DO cancer project to be a focused view of cancer terms within the DO. The DO cancer project mapped 386 cancer terms from the Catalogue of Somatic Mutations in Cancer (COSMIC), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium, Therapeutically Applicable Research to Generate Effective Treatments, Integrative Oncogenomics and the Early Detection Research Network into a cohesive set of 187 DO terms represented by 63 top-level DO cancer terms. For example, the COSMIC term 'kidney, NS, carcinoma, clear_cell_renal_cell_carcinoma' and TCGA term 'Kidney renal clear cell carcinoma' were both grouped to the term 'Disease Ontology Identification (DOID):4467 / renal clear cell carcinoma' which was mapped to the TopNodes_DOcancerslim term 'DOID:263 / kidney cancer'. Mapping of diverse cancer terms to DO and the use of top level terms (DO slims) will enable pan-cancer analysis across datasets generated from any of the cancer term sources where pan-cancer means including or relating to all or multiple types of cancer. The terms can be browsed from the DO web site (http://www.disease-ontology.org) and downloaded from the DO's Apache Subversion or GitHub repositories. Database URL: http://www.disease-ontology.org

Full Text

Duke Authors

Cited Authors

  • Wu, T-J; Schriml, LM; Chen, Q-R; Colbert, M; Crichton, DJ; Finney, R; Hu, Y; Kibbe, WA; Kincaid, H; Meerzaman, D; Mitraka, E; Pan, Y; Smith, KM; Srivastava, S; Ward, S; Yan, C; Mazumder, R

Published Date

  • 2015

Published In

Volume / Issue

  • 2015 /

Start / End Page

  • bav032 -

PubMed ID

  • 25841438

Pubmed Central ID

  • 25841438

Electronic International Standard Serial Number (EISSN)

  • 1758-0463

Digital Object Identifier (DOI)

  • 10.1093/database/bav032

Language

  • eng

Conference Location

  • England