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

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.

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype‐weighted knowledge in the CAGI SickKids5 clinical genomes challenge

“…A number of strategies have been developed for the incorporation of patient‐specific gene prioritization information. The information may come from various biomedical ontologies, including human‐specific ontologies, like HPO (Köhler et al, ), Disease Ontology (DO, Schriml et al, ), Gene Ontology (GO, Blake et al, ; such as used in Phevor; Singleton et al, ) and other model organism‐specific ontologies, such as Mammalian Phenotype Ontology (MPO, Smith & Eppig, ), Zebrafish Phenotype Ontology (ZPO, van Slyke, Bradford, Westerfield, & Haendel, ; used in Exomiser; Smedley et al, ). Several computational tools leverage gene‐disease‐phenotype relationships and phenotype information, for instance, phenolyzer (Yang, Robinson, & Wang, ) and Phenotype Driven Ranking (PDR, Krämer, Shah, Rebres, Tang, & Richards, ).…”

Wikidata as a FAIR knowledge graph for the life sciences

“…However, centralized resources are very difficult and expensive to maintain and 30 expand [4,5], in large part because of limited bandwidth and resources of the technical team and the 31 bottlenecks that introduces. 32 33 At the other end of the spectrum, distributed approaches to data integration leave in place a broad 34 landscape of individual resources, focusing on technical infrastructure to query and integrate across 35 them for each query. These approaches lower the barriers to adding new data by enabling anyone to 36 publish data by following community standards.…”

“…The Wikidata Biomedical Knowledge Graph 33 The original effort behind this work focused on creating and annotating Wikidata items for human and 34 mouse genes and proteins [10], and was subsequently expanded to include microbial reference 35 genomes from NCBI RefSeq [13]. Since then, the Wikidata community (including our team) has 36 significantly expanded the depth and breadth of biological information within Wikidata, resulting in a 37 rich, heterogeneous knowledge graph (Figure 1).…”

“…37 38 Diseases. Wikidata has items for over 16 thousand diseases, the majority of which were created based 39 on imports from the Human Disease Ontology [34], with additional disease terms added from the 40…”

Integration of genomic variation and phenotypic data using HmtPhenome

“…Terms are arranged in a hierarchical manner, with terms referring to much broader concepts located as top nodes, and more specific elements can be found traversing this tree-like structure until reaching the final leaves; each node usually contains other additional information about the described element, with links pointing to further external resources. The Human Phenotype Ontology (HPO) 19 and the Disease Ontology (DO) 20 are probably the most commonly used examples of such schema as related to phenotypes and diseases, respectively, but many other similar services exist, like the Mammalian Phenotype Ontology (MPO) 21 , PhenomeNET 22 , Medical Subject Headings (MeSH) 23 and the Unified Medical Language System (UMLS) 24 .…”