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Deep phenotyping: symptom annotation made simple with SAMS
Precision medicine needs precise phenotypes. The Human Phenotype Ontology (HPO) uses clinical signs instead of diagnoses and has become the standard annotation for patients’ phenotypes when describing single gene disorders. Use of the HPO beyond human genetics is however still limited. With SAMS (Sy...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252818/ https://www.ncbi.nlm.nih.gov/pubmed/35524573 http://dx.doi.org/10.1093/nar/gkac329 |
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author | Steinhaus, Robin Proft, Sebastian Seelow, Evelyn Schalau, Tobias Robinson, Peter N Seelow, Dominik |
author_facet | Steinhaus, Robin Proft, Sebastian Seelow, Evelyn Schalau, Tobias Robinson, Peter N Seelow, Dominik |
author_sort | Steinhaus, Robin |
collection | PubMed |
description | Precision medicine needs precise phenotypes. The Human Phenotype Ontology (HPO) uses clinical signs instead of diagnoses and has become the standard annotation for patients’ phenotypes when describing single gene disorders. Use of the HPO beyond human genetics is however still limited. With SAMS (Symptom Annotation Made Simple), we want to bring sign-based phenotyping to routine clinical care, to hospital patients as well as to outpatients. Our web-based application provides access to three widely used annotation systems: HPO, OMIM, Orphanet. Whilst data can be stored in our database, phenotypes can also be imported and exported as Global Alliance for Genomics and Health (GA4GH) Phenopackets without using the database. The web interface can easily be integrated into local databases, e.g. clinical information systems. SAMS offers users to share their data with others, empowering patients to record their own signs and symptoms (or those of their children) and thus provide their doctors with additional information. We think that our approach will lead to better characterised patients which is not only helpful for finding disease mutations but also to better understand the pathophysiology of diseases and to recruit patients for studies and clinical trials. SAMS is freely available at https://www.genecascade.org/SAMS/. |
format | Online Article Text |
id | pubmed-9252818 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-92528182022-07-05 Deep phenotyping: symptom annotation made simple with SAMS Steinhaus, Robin Proft, Sebastian Seelow, Evelyn Schalau, Tobias Robinson, Peter N Seelow, Dominik Nucleic Acids Res Web Server Issue Precision medicine needs precise phenotypes. The Human Phenotype Ontology (HPO) uses clinical signs instead of diagnoses and has become the standard annotation for patients’ phenotypes when describing single gene disorders. Use of the HPO beyond human genetics is however still limited. With SAMS (Symptom Annotation Made Simple), we want to bring sign-based phenotyping to routine clinical care, to hospital patients as well as to outpatients. Our web-based application provides access to three widely used annotation systems: HPO, OMIM, Orphanet. Whilst data can be stored in our database, phenotypes can also be imported and exported as Global Alliance for Genomics and Health (GA4GH) Phenopackets without using the database. The web interface can easily be integrated into local databases, e.g. clinical information systems. SAMS offers users to share their data with others, empowering patients to record their own signs and symptoms (or those of their children) and thus provide their doctors with additional information. We think that our approach will lead to better characterised patients which is not only helpful for finding disease mutations but also to better understand the pathophysiology of diseases and to recruit patients for studies and clinical trials. SAMS is freely available at https://www.genecascade.org/SAMS/. Oxford University Press 2022-05-07 /pmc/articles/PMC9252818/ /pubmed/35524573 http://dx.doi.org/10.1093/nar/gkac329 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Web Server Issue Steinhaus, Robin Proft, Sebastian Seelow, Evelyn Schalau, Tobias Robinson, Peter N Seelow, Dominik Deep phenotyping: symptom annotation made simple with SAMS |
title | Deep phenotyping: symptom annotation made simple with SAMS |
title_full | Deep phenotyping: symptom annotation made simple with SAMS |
title_fullStr | Deep phenotyping: symptom annotation made simple with SAMS |
title_full_unstemmed | Deep phenotyping: symptom annotation made simple with SAMS |
title_short | Deep phenotyping: symptom annotation made simple with SAMS |
title_sort | deep phenotyping: symptom annotation made simple with sams |
topic | Web Server Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9252818/ https://www.ncbi.nlm.nih.gov/pubmed/35524573 http://dx.doi.org/10.1093/nar/gkac329 |
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