The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data

By Sebastian Köhler, S. Doelken, C. Mungall, Sebastian Bauer, H. Firth, I. Bailleul-Forestier, G. Black, Danielle L. Brown, Michael Brudno, Jennifer Campbell and 37 more. Nucleic Acids Res., 2013

826 citations recorded by the source corpus.

The Human Phenotype Ontology (HPO) project, available at http://www.human-phenotype-ontology.org, provides a structured, comprehensive and well-defined set of 10,088 classes (terms) describing human phenotypic abnormalities and 13,326 subclass relations between the HPO classes. In addition we have developed logical definitions for 46% of all HPO classes using terms from ontologies for anatomy, cell types, function, embryology, pathology and other domains. This allows interoperability with several resources, especially those containing phenotype information on model organisms such as mouse and zebrafish. Here we describe the updated HPO database, which provides annotations of 7,278 human hereditary syndromes listed in OMIM, Orphanet and DECIPHER to classes of the HPO. Various meta-attributes such as frequency, references and negations are associated with each annotation. Several large-scale projects worldwide utilize the HPO for describing phenotype information in their datasets. We have therefore generated equivalence mappings to other phenotype vocabularies such as LDDB, Orphanet, MedDRA, UMLS and phenoDB, allowing integration of existing datasets and interoperability with multiple biomedical resources. We have created various ways to access the HPO database content using flat files, a MySQL database, and Web-based tools. All data and documentation on the HPO project can be found online.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/177d378e35a9d438488c1e4c4680884536c425c3

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Citations in the corpus

The Human Phenotype Ontology in 2024: phenotypes around the world (2023, methodology): “In this update to previous articles in the Nucleic Acids Research database issue ( 4–7 ) , we discuss recent additions to the project, including translations to 10 languages, new HPO terms and annotations for a number of medical fields, and efforts to integrate HPO with electronic health records (…”
The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species (2016, methodology): “Monarch has integrated data from a variety of human, model organism, and other biomedical sources such as OMIM (1), Orphanet (3), Clin-Var (2), HPO (30), KEGG (31), CTD (4), MyGene (32), BioGrid (33) and via orthology in PantherDB (34) we also incorporate Fgg gene data from ZFIN (35) and from MGI (3”
Open Targets: a platform for therapeutic target identification and validation (2016, methodology): “To enable this we have started mapping several existing ontologies like ORDO (Orphanet Rare Disease Ontology (24)), DO (disease ontology, (25)) and HPO (Human Phenotype Ontology,(26)) or widely-used terminologies like MeSH (http://www.”
The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease (2018, background): “The Leigh Map consists of 87 genes and 234 phenotypes expressed in Human Phenotypic Ontology (HPO) terms (31), and they provide sufficient phenotypic and genetic variation to test the network’s diagnostic capability.”
DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes (2015, background): “We used UMLS Metathesaurus concept structure to map MIM, MeSH, HDO, HPO and ICD9-CM terms, as well as disease names to UMLS Concept Unique Identifiers (CUIs).”
Genomic diagnosis for children with intellectual disability and/or developmental delay (2016, background): “Affected individuals displayed symptoms described by 333 unique HPO [22] terms with over 90% of individuals displaying intellectual disability, 69% with speech delay, 45% with seizures, and 20% with microcephaly or macrocephaly.”
Next-generation diagnostics and disease-gene discovery with the Exomiser (2015, methodology): “The human data come from the disease-gene associations maintained by Online Mendelian Inheritance in Man (OMIM) 39 and Orphanet 40 and the phenotype annotations maintained by us and encoded as HPO terms 38,41 .”
The Human Transcription Factors. (2018, neutral): “Figure 4B illustrates human disease phenotypes that involve a significant number of mutations within or near genes encoding TFs, as compiled by The Human Phenotype Ontology (Köhler et al., 2014).”
g:Profiler—a web server for functional interpretation of gene lists (2016 update) (2016, neutral): “Gene annotations of physiological and disease phenotypes from the HPO (2) and the Online Mendelian Inheritance in Man (OMIM) resource (8) allow users to interpret their gene lists in the context of human health.”
GeneMatcher: A Matching Tool for Connecting Investigators with an Interest in the Same Gene (2015, methodology): “GeneMatcher can send out queries to these databases based on gene, genomic location, OMIM number, and soon phenotypic features using PhenoDB terms that match to ICHPT (International Consortium for Human Phenotype Terminologies) terms and/or HPO terms [Köhler et al., 2014].”