The Human Phenotype Ontology in 2017

By Sebastian Köhler, N. Vasilevsky, M. Engelstad, Erin Foster, J. McMurry, Ségolène Aymé, Gareth Baynam, S. Bello, C. Boerkoel, K. Boycott and 49 more. Nucleic Acids Res., 2016

742 citations recorded by the source corpus.

Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d673612c02e8b12922109aae9ade76ea373aab06

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Citations in the corpus

Revisiting biomarker discovery by plasma proteomics (2017, methodology): “…proper study design as well as precise and deep clinical phenotyping of participants, using systematic classifications such as the International Statistical Classification of Diseases (http://apps.who.int/clas sifications/icd10/browse/2016/en) or the human phenome ontology (Kohler et al, 2017).”
Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources (2018, methodology): “In the 2 years since the previous Nucleic Acids Research database article (12), the HPO has continued to grow in both reach and scope.”
ClinVar: improving access to variant interpretations and supporting evidence (2017, background): “Phenotypes may be submitted as Human Phenotype Ontology (HPO) identifiers or terms; terms for clinical features not in HPO may be submitted and are assigned an identifier in MedGen (10).”
Mouse Genome Database (MGD)-2018: knowledgebase for the laboratory mouse (2017, methodology): “MGD has also implemented a new browser created by MGI that features the Human Phenotype Ontology (17) developed by Peter Robinson and colleagues (http://www. informatics.jax.org/vocab/hp ontology).”
Human Disease Ontology 2018 update: classification, content and workflow expansion (2018, background): “For example, observable characteristics or traits (phenotypic traits) from the Human Phenotype Ontology (HPO) (44) will define logical axioms with the ‘has phenotype’ relation for phenotypes of syndromes and genetic diseases.”
Settling the score: variant prioritization and Mendelian disease (2017, background): “A key product of the HPO is the annotation of phenotype-to-gene and phenotype-to-condition files that are used in many downstream prioritization tools.”
SymMap: an integrative database of traditional Chinese medicine enhanced by symptom mapping (2018, background): “0) and TCMSP databases, and the target genes of modern diseases from the HPO (version 2017) (18), DrugBank (version 5.”
The Human Phenotype Ontology in 2021 (2020, background): “Disorders of the immune system, central nervous system, the respiratory system, and the kidney were among the areas where additional work was deemed desirable (3).”
Open Targets Platform: new developments and updates two years on (2018, neutral): “net) or Human Phenotype Ontology (HP) (8).”
OMIM.org: leveraging knowledge across phenotype–gene relationships (2018, background): “The clinical features in the synopses are linked to controlled vocabularies such as the Unified Medical Language System (UMLS) (4), Human Phenotype Ontology (HPO) (5), International Classification of Diseases (ICD), Disease Ontology (DO) (6) and Orphanet (7) codes.”