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

Tsung-Jung Wu; Lynn M Schriml; Qing-Rong Chen; Maureen Colbert; Daniel J Crichton; Richard Finney; Ying Hu; Warren A Kibbe; Heather Kincaid; Daoud Meerzaman; Elvira Mitraka; Yang Pan; Krista M Smith; Sudhir Srivastava; Sari Ward; Cheng Yan; Raja Mazumder

doi:10.1093/database/bav032

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

Database (Oxford). 2015 Apr 4:2015:bav032. doi: 10.1093/database/bav032. Print 2015.

Authors

Tsung-Jung Wu¹, Lynn M Schriml¹, Qing-Rong Chen¹, Maureen Colbert¹, Daniel J Crichton¹, Richard Finney¹, Ying Hu¹, Warren A Kibbe¹, Heather Kincaid¹, Daoud Meerzaman¹, Elvira Mitraka¹, Yang Pan¹, Krista M Smith¹, Sudhir Srivastava¹, Sari Ward¹, Cheng Yan¹, Raja Mazumder²

Affiliations

¹ Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA.
² Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA Department of Biochemistry and Molecular Medicine, George Washington University, Washington, DC 20037, USA, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA, Center for Bioinformatics and Information Technology, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, NASA Jet Propulsion Laboratory, Pasadena, CA, USA, Division of Cancer Prevention, National Cancer Institute, 9609 Medical Center Drive, Rockville, MD 20892-9760, USA, Wellcome Trust Sanger Institute, Cambridge, UK and McCormick Genomic and Proteomic Center, George Washington University, Washington, DC 20037, USA mazumder@gwu.edu.

Abstract

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

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Animals
Biological Ontologies*
Data Mining*
Databases, Factual*
Humans
Neoplasms*

Grants and funding

156620/PHS HHS/United States