Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

By Sebastian Köhler, Leigh Carmody, N. Vasilevsky, Julius O. B. Jacobsen, D. Danis, J. Gourdine, M. Gargano, N. Harris, Nicolas Matentzoglu, J. McMurry and 59 more. Nucleic Acids Res., 2018

616 citations recorded by the source corpus.

Abstract The Human Phenotype Ontology (HPO)—a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases—is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/c9feb0501d5c69f2438cd6923c783ce211b9bfab

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Citations in the corpus

Artificial intelligence in clinical and genomic diagnostics (2019, background): “The human phenotype ontology lists 1007 distinct terms defining different abnormalities of the face [73].”
Spatiotemporal analysis of human intestinal development at single-cell resolution (2021, positive): “We then used the curated HPO phenotypes and ontology annotations to link selected phenotypes to Online Mendelian Inheritance in Man (OMIM) and OrphaNet human diseases and their associated causative disease genes.”
The DisGeNET knowledge platform for disease genomics: 2019 update (2019, methodology): “…attributes that cover a wide variety of biomedical resources: diseases are coded using UMLS ® (16) concept unique identifiers (CUIs), and annotated with the UMLS ® semantic type, the MeSH class, and the top level concepts from the Human Disease Ontology (17) and the Human Phenotype Ontology (18).”
The Human Phenotype Ontology in 2021 (2020, methodology): “Counts of terms added to the ontology after the previous article in this series (19) are shown in dark blue (added between 25 July 2018 and 18 August 2020).”
HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine (2020, positive): “Then, we standardized the disease information to MeSH terms (https://www.nlm.nih.gov/mesh/meshhome.html) and the DisGeNet database (18), and cross-referenced them with the HPO and Disease Ontology (20) databases.”
Gene Set Knowledge Discovery with Enrichr (2021, methodology): “Gene set libraries in the Ontology category were created from ontology trees including the Gene Ontology (The Gene Ontology Consortium, 2019) and the Human Phenotype Ontology (HPO; Köhler et al., 2019).”
gprofiler2 -- an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler (2020, methodology): “g:Profiler’s in-house database includes only reliable annotation data sources that are regularly updated such as Gene Ontology (GO) 3 , KEGG 4 , Reactome 5 , WikiPathways 17 , miRTarBase 18 , TRANSFAC 19 , Human Protein Atlas 20 , protein complexes from CORUM 21 and Human Phenotype Ontology 22 .”
TCM network pharmacology: A new trend towards combining computational, experimental and clinical approaches. (2021, background): “OMIM ([43]), HPO ([44]), UMLS ([45]), and other disease databases provide abundant and reliable annotation information for exploring disease-related phenotypes and pathogenic genes.”
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 International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease (2022, methodology): “These phenotypic similarity computations between mouse knockouts and known human disorders are performed by the PhenoDigm algorithm (34), which relies on the availability of standardised phenotypic annotations (2,3).”
g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update) (2019, background): “This covers currently KEGG (19), Reactome (20) and WikiPathways (21); miRNA targets from miRTar-Base (22) and regulatory motif matches from TRANSFAC (23); tissue specificity based on expression data from Human Protein Atlas (24); protein complexes data from CO-RUM (25) and human disease phenotype as”