New release of Mondo Disease Ontology

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Announcing the latest release of the Mondo Disease Ontology (2019–05–28 release), available here:

What: Mondo is a unified disease ontology, encompassing many disease terminologies, which aims to harmonize disease definitions across the world.

Why did we create Mondo?
Numerous sources for disease definitions and data models currently exist, which include HPO, OMIM, SNOMED CT, ICD, PhenoDB, MedDRA, MedGen, ORDO, DO, GARD etc; however, these sources partially overlap and sometimes conflict, making it difficult to know definitively how they relate to each other. This has resulted in a proliferation of mappings between disease entries in different resources; however mappings are problematic: collectively, they are expensive to create and maintain. Most importantly, the mappings lack completeness, accuracy, and precision; as a result, mapping calls are often inconsistent between resources. The UMLS provides intermediate concepts through which other resources can be mapped, but these mappings suffer from the same challenges: they are not guaranteed to be one-to-one, especially in areas with evolving disease concepts such as rare disease.

In order to address the lack of a unified disease terminology that provides precise equivalences between disease concepts, we created Mondo, which provides a logic-based structure for unifying multiple disease resources.

Mondo’s development is coordinated with the Human Phenotype Ontology (HPO), which describes the individual phenotypic features that constitute a disease. Like the HPO, Mondo provides a hierarchical structure which can be used for classification or “rolling up” diseases to higher level groupings. It provides mappings to other disease resources, but in contrast to other mappings between ontologies, we precisely annotate each mapping using strict semantics, so that we know when two disease names or identifiers are equivalent or one-to-one, in contrast to simply being closely related.

How was it created?
To develop the Mondo ontology we used a combination of automated algorithms combined with human curation. We devised a method called k-cluster Bayesian OWL Ontology Merging (k-BOOM) that combines logic-based reasoning with probabilistic inference. This was used to detect initial consistent mappings between ontologies, and to detect inconsistencies between resources. We now employ manual expert curation to develop the ontology, and leverage logical-based OWL reasoning for consistency checking and semi-automated classification. Each Mondo term has a unique persistent identifier and includes the full provenance from sources.

Who uses Mondo?
We use Mondo within our own Monarch Initiative knowledge graph, a system that allows for cross-species disease discovery, and are exploring its use in our applications such as Exomiser, a rare-disease diagnostic tool. Mondo is used to facilitate a global alignment of disease concepts in the Experimental Factor Ontology (EFO) for disease annotations in EBI resources, Open Targets, and Euro-Bioimaging. In addition, Mondo is being utilized in diverse applications and resources outside of Monarch, such as ClinGen and Gabriella Miller Kids First.

How can I help?
We invite the community to help us improve and extend Mondo by joining our mailing list, or submitting tickets on our GitHub tracker.

More info below:
Format: OWL and OBO formats
License: CC BY 3.0
Mailing list:

OBO Foundry:
Ontology Lookup Service:

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