Mondo Disease Ontology: harmonizing
disease concepts across the world
Nicole Vasilevskya1, Shahim Essaida, Nico Matentzoglub, Nomi L. Harrisc, Melissa
Haendela,d, Peter Robinsone, Christopher J. Mungallc
aOregon Health & Science University, Portland, OR, USAb
European Bioinformatics Institute, Hinxton, UKcLawrence Berkeley National Laboratory, Berkeley, CA, USA
dOregon State University, Corvallis, OR, USAe
The Jackson Laboratory, Farmington, CT, USA
1

Corresponding Author: Nicole Vasilevsky, Email: vasilevs@ohsu.edu
Keywords. Disease, Rare disease, Ontology, Clinical Terminology

1. Introduction
Many terminologies and ontologies currently exist for classifying and describing
diseases, each of which has been designed for a particular purpose, and as such hasdifferent strengths. Examples include the National Cancer Institute Thesaurus (NCIt),
the Online Mendelian Inheritance in Man (OMIM), SNOMED CT, ICD, ICD-O,OncoTree, MedGen, Disease Ontology, and numerous others. Our census of disease
ontologies identified 37 such resources. [1] However, these standards partially overlapand often conflict, making it difficult to align knowledge sources. This need to integrate
information has resulted in a proliferation of mappings and cross-references between
disease entries in different resources; these mappings lack completeness, accuracy, andprecision, and are often inconsistent between resources.
In order to computationally leverage the wide array of available knowledge sources
for diagnostics and to reveal underlying mechanisms of diseases, we need to understandwhich terms are truly equivalent across different resources. This will allow integrationof associated information, such as treatments, genetics, phenotypes, etc. We therefore
created the Mondo Disease Ontology to provide a logic-based structure for unifying
multiple disease resources. The goal of Mondo is to integrate the classifications andrelationships of commonly used disease ontologies into a single semantically coherent
resource to enable the aggregation and analysis of disparate clinical data repositories andfacilitate the discovery of relationships between disease concepts across ontologies
2. Methods
Mondo was created by a combination of algorithmic equivalency determination
using the k-BOOM algorithm [2], and expert curation. Mondo spans both rare and‘common’ disease, comprising monogenic and polygenic (complex, common) diseases,
infectious diseases, trauma, and cancer. It provides equivalence mappings to otherdisease resources, but in contrast to other mapping sets, Mondo precisely annotates eachmapping using strict semantics, so that we know when two diseases are precisely

Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

equivalent or merely closely related - allowing computational integration of associateddata.
Mondo provides a hierarchical structure which can be used to annotate data at
different levels of precision. Terms are classified under multiple parents, as needed, andexternal ontologies are imported for use in equivalence and subclass of axioms.
3. Results and Discussion

Mondo currently contains over 20,000 disease classes. It is iteratively developedwith the community and is under continuous revision, with future plans to further
revise the top-level classes. Mondo is an OBO Foundry ontology and can beviewed on the OBO website (obofoundry.org/ontology/mondo.html), or with otherontology browsers such as the Ontology Lookup Service (OLS)(ebi.ac.uk/ols/ontologies/mondo). The ontology files and current releases areavailable on GitHub (https://github.com/monarch-initiative/mondo), with releases
made on a monthly basis. We invite the community to contribute to Mondo; visit
our website, GitHub repository and/or join our mailing list (mondousers@googlegroups.com).
Mondo is being utilized in diverse applications and resources such as the Monarch
Initiative [3], ClinGen [4], and Gabriella Miller Kids First Data Resource [5],which is a curated database of clinical and genetic sequence data from pediatricpatients with structural abnormalities or childhood cancers. The Experimental
Factor Ontology (EFO) employs Mondo’s classifications and axioms to facilitate
annotation of disease information in applications that use EFO. By integrating
information from multiple disease resources, amassed through years of work byresearchers, clinicians, ontologists, and other scientists from around the world,
Mondo aims to make this knowledge readily available to the scientific communityand grow its value through logical connections across resources.
References[1] Haendel MA, McMurry JA, Relevo R, Mungall CJ, Robinson PN, Chute CG. A Census of Disease
Ontologies. Annu Rev Biomed Data Sci. 2018;1: 305–331.
[2] Mungall CJ, Koehler S, Robinson P, Holmes I, Haendel M. k-BOOM: A Bayesian approach toontology structure inference, with applications in disease ontology construction. bioRxiv. 2019. p.
048843. doi:10.1101/048843[3] Shefchek KA, Harris NL, Gargano M, Matentzoglu N, Unni D, Brush M, et al. The Monarch Initiativein 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.
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[4] Clinical Genome Resource. Welcome to ClinGen. Available: https://clinicalgenome.org/[5] Working Together to Put Kids First. Available: https://kidsfirstdrc.org/