Human Disease Ontology 2018 update: classification, content and workflow expansion.

Lynn M Schriml ,Katharine Bisordi,Siobhan Kibbey,Becky Tauber,Brooke Hyman,Michelle Giglio,Mike Schor,Elvira Mitraka,Victor Felix,Nicole Campion,Lance Nickle,Poorna Sreekumar,Richard Lichenstein,James B Munro ,Cynthia F Bearer ,David B Kurland ,Linda Jo Bone Jeng ,X Chris Le ,Connor P Oates ,Carol L Greene

doi:10.1093/nar/gky1032

Abstract

The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification includes specific formal semantic rules to express meaningful disease models and has expanded from a single asserted classification to include multiple-inferred mechanistic disease classifications, thus providing novel perspectives on related diseases. Expansion of disease terms, alternative anatomy, cell type and genetic disease classifications and workflow automation highlight the updates for the DO since 2015. The enhanced breadth and depth of the DO’s knowledgebase has expanded the DO’s utility for exploring the multi-etiology of human disease, thus improving the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources and demonstrated by a 6.6× growth in DO’s user community since 2015. The DO’s continual integration of human disease knowledge, evidenced by the more than 200 SVN/GitHub releases/revisions, since previously reported in our DO 2015 NAR paper, includes the addition of 2650 new disease terms, a 30% increase of textual definitions, and an expanding suite of disease classification hierarchies constructed through defined logical axioms.

Highlights

The rapid growth of biomedical and clinical research in recent decades has begun to reveal novel cellular, molecular and environmental determinants of disease [1,2,3,4]
The opportunities for discovery and the transcendence of knowledge between research groups can only be realized in conjunction with the development of rigorous, standardized bioinformatics tools
These tools should be capable of addressing specific biomedical data nomenclature and standardization challenges posed by the vast variety of biomedical data resources, such as the 3 924 249 disease-associated articles published in the past three years (1 January 2015–9 October 2018)

Summary

Introduction

The rapid growth of biomedical and clinical research in recent decades has begun to reveal novel cellular, molecular and environmental determinants of disease [1,2,3,4]. D956 Nucleic Acids Research, 2019, Vol 47, Database issue ized descriptions of human diseases improves the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources. We report on the significant improvements and advances to the DO database since 2015 including a broadening of the DO license, expansion of disease terms, cross reference and logical definition content, automation of data loading and quality control (QC) methods, and development of multiple, alternative disease classifications.

Results

Conclusion