Augmenting the disease ontology improves and unifies disease annotations across species

Abstract

Model organisms are vital to uncovering the mechanisms of human disease and developing new therapeutic tools. Researchers collecting and integrating relevant model organism and/or human data often apply disparate terminologies (vocabularies and ontologies), making comparisons and inferences difficult. A unified disease ontology is required that connects data annotated using diverse disease terminologies, and in which the terminology relationships are continuously maintained. The Mouse Genome Database (MGD, http://www.informatics.jax.org), Rat Genome Database (RGD, http://rgd.mcw.edu) and Disease Ontology (DO, http://www.disease-ontology.org) projects are collaborating to augment DO, aligning and incorporating disease terms used by MGD and RGD, and improving DO as a tool for unifying disease annotations across species. Coordinated assessment of MGD's and RGD's disease term annotations identified new terms that enhance DO's representation of human diseases. Expansion of DO term content and cross-references to clinical vocabularies (e.g. OMIM, ORDO, MeSH) has enriched the DO's domain coverage and utility for annotating many types of data generated from experimental and clinical investigations. The extension of anatomy-based DO classification structure of disease improves accessibility of terms and facilitates application of DO for computational research. A consistent representation of disease associations across data types from cellular to whole organism, generated from clinical and model organism studies, will promote the integration, mining and comparative analysis of these data. The coordinated enrichment of the DO and adoption of DO by MGD and RGD demonstrates DO's usability across human data, MGD, RGD and the rest of the model organism database community.

Single-cell interactomes of the human brain reveal cell-type specific convergence of brain disorders

“…We collected, curated, and annotated gene-disease associations for brain-related disorders by matching the DisGeNET database (Piñero et al 2017) with annotations from the Monarch Disease Ontology (MONDO) (Bello et al 2018) . We organize diseases in three separate classes: (i) neurodegenerative, (ii) neuropsychiatric, and (iii) neoplastic disorders.…”

“…Disease-associated genes were collected from the DisGeNET database (Piñero et al 2017) , which aggregates data from GWAS catalogues, animal models, and the scientific literature; preserving the evidence type supporting each disease-gene association. All disorders were mapped to the Monarch Disease Ontology (MonDO) (Bello et al 2018) and brain-associated disorders corresponding to (i) neurodegenerative, (ii) neuropsychiatric, and (iii) brain cancers were selected. To this end, first diseases that are associated with nervous system (annotated with "nervous system disorder (MONDO_0005071)" term) were selected.…”

Sistemas de representación del conocimiento para la gestión de la terminología biomédica

“…La Disease Ontology (DO) (Bello et al 2018) se creó como una ontología estandarizada sobre las enfermedades humanas frecuentes y raras con la finalidad de proporcionarle a la comunidad biomédica descripciones sostenibles, reutilizables y consistentes sobre las enfermedades, sus características fenotípicas y otros conceptos relacionados. El objetivo de este proyecto era desarrollar una estructura única para la clasificación de las enfermedades, que unificase su representación entre las diversas terminologías y vocabularios por medio de una ontología que, además, permitiese hacer inferencias y razonamientos sobre las relaciones entre los distintos términos y conceptos referidos a las enfermedades.…”

AllEnricher: a comprehensive gene set function enrichment tool for both model and non-model species

“…Pathway-based database, like Kyoto Encyclopedia of Genes and Genomes (KEGG) [3] and Reactome [4], provide gene function interpretation through the perspective of biological reactions. Other databases like disease-based databases, such as Disease Ontology (DO) [5], DISEASE [6] and DisGeNET [7] were designed for molecular studies in disease. All these databases together provide comprehensive gene-function interpretations for the biologists.…”