- The Human Phenotype Ontology in 2017. [PMID: 27899602]
Sebastian Köhler, Nicole A Vasilevsky, Mark Engelstad, Erin Foster, Julie McMurry, Ségolène Aymé, Gareth Baynam, Susan M Bello, Cornelius F Boerkoel, Kym M Boycott, Michael Brudno, Orion J Buske, Patrick F Chinnery, Valentina Cipriani, Laureen E Connell, Hugh J S Dawkins, Laura E DeMare, Andrew D Devereau, Bert B A de Vries, Helen V Firth, Kathleen Freson, Daniel Greene, Ada Hamosh, Ingo Helbig, Courtney Hum, Johanna A Jähn, Roger James, Roland Krause, Stanley J F Laulederkind, Hanns Lochmüller, Gholson J Lyon, Soichi Ogishima, Annie Olry, Willem H Ouwehand, Nikolas Pontikos, Ana Rath, Franz Schaefer, Richard H Scott, Michael Segal, Panagiotis I Sergouniotis, Richard Sever, Cynthia L Smith, Volker Straub, Rachel Thompson, Catherine Turner, Ernest Turro, Marijcke W M Veltman, Tom Vulliamy, Jing Yu, Julie von Ziegenweidt, Andreas Zankl, Stephan Züchner, Tomasz Zemojtel, Julius O B Jacobsen, Tudor Groza, Damian Smedley, Christopher J Mungall, Melissa Haendel, Peter N Robinson
Nucleic acids research 2017:45(D1)
1 Citations (Google Scholar as of 2017-02-20)
Abstract: 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. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
- The Human Phenotype Ontology: Semantic Unification of Common and Rare Disease. [PMID: 26119816]
Tudor Groza, Sebastian Köhler, Dawid Moldenhauer, Nicole Vasilevsky, Gareth Baynam, Tomasz Zemojtel, Lynn Marie Schriml, Warren Alden Kibbe, Paul N Schofield, Tim Beck, Drashtti Vasant, Anthony J Brookes, Andreas Zankl, Nicole L Washington, Christopher J Mungall, Suzanna E Lewis, Melissa A Haendel, Helen Parkinson, Peter N Robinson
American journal of human genetics 2015:97(1)
49 Citations (Google Scholar as of 2017-02-20)
Abstract: The Human Phenotype Ontology (HPO) is widely used in the rare disease community for differential diagnostics, phenotype-driven analysis of next-generation sequence-variation data, and translational research, but a comparable resource has not been available for common disease. Here, we have developed a concept-recognition procedure that analyzes the frequencies of HPO disease annotations as identified in over five million PubMed abstracts by employing an iterative procedure to optimize precision and recall of the identified terms. We derived disease models for 3,145 common human diseases comprising a total of 132,006 HPO annotations. The HPO now comprises over 250,000 phenotypic annotations for over 10,000 rare and common diseases and can be used for examining the phenotypic overlap among common diseases that share risk alleles, as well as between Mendelian diseases and common diseases linked by genomic location. The annotations, as well as the HPO itself, are freely available. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.
- The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. [PMID: 24217912]
Sebastian Köhler, Sandra C Doelken, Christopher J Mungall, Sebastian Bauer, Helen V Firth, Isabelle Bailleul-Forestier, Graeme C M Black, Danielle L Brown, Michael Brudno, Jennifer Campbell, David R FitzPatrick, Janan T Eppig, Andrew P Jackson, Kathleen Freson, Marta Girdea, Ingo Helbig, Jane A Hurst, Johanna Jähn, Laird G Jackson, Anne M Kelly, David H Ledbetter, Sahar Mansour, Christa L Martin, Celia Moss, Andrew Mumford, Willem H Ouwehand, Soo-Mi Park, Erin Rooney Riggs, Richard H Scott, Sanjay Sisodiya, Steven Van Vooren, Ronald J Wapner, Andrew O M Wilkie, Caroline F Wright, Anneke T Vulto-van Silfhout, Nicole de Leeuw, Bert B A de Vries, Nicole L Washingthon, Cynthia L Smith, Monte Westerfield, Paul Schofield, Barbara J Ruef, Georgios V Gkoutos, Melissa Haendel, Damian Smedley, Suzanna E Lewis, Peter N Robinson
Nucleic acids research 2014:42(Database issue)
322 Citations (Google Scholar as of 2017-02-20)
Abstract: 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.
- The human phenotype ontology. [PMID: 20412080]
P N Robinson, S Mundlos
Clinical genetics 2010:77(6)
206 Citations (Google Scholar as of 2017-02-20)
Abstract: A standardized, controlled vocabulary allows phenotypic information to be described in an unambiguous fashion in medical publications and databases. The Human Phenotype Ontology (HPO) is being developed in an effort to provide such a vocabulary. The use of an ontology to capture phenotypic information allows the use of computational algorithms that exploit semantic similarity between related phenotypic abnormalities to define phenotypic similarity metrics, which can be used to perform database searches for clinical diagnostics or as a basis for incorporating the human phenome into large-scale computational analysis of gene expression patterns and other cellular phenomena associated with human disease. The HPO is freely available at http://www.human-phenotype-ontology.org.
- The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease. [PMID: 18950739]
Peter N Robinson, Sebastian Köhler, Sebastian Bauer, Dominik Seelow, Denise Horn, Stefan Mundlos
American journal of human genetics 2008:83(5)
397 Citations (Google Scholar as of 2017-02-20)
Abstract: There are many thousands of hereditary diseases in humans, each of which has a specific combination of phenotypic features, but computational analysis of phenotypic data has been hampered by lack of adequate computational data structures. Therefore, we have developed a Human Phenotype Ontology (HPO) with over 8000 terms representing individual phenotypic anomalies and have annotated all clinical entries in Online Mendelian Inheritance in Man with the terms of the HPO. We show that the HPO is able to capture phenotypic similarities between diseases in a useful and highly significant fashion.