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Deep phenotyping: symptom annotation made simple with SAMS.
Steinhaus, Robin; Proft, Sebastian; Seelow, Evelyn; Schalau, Tobias; Robinson, Peter N; Seelow, Dominik.
Afiliação
  • Steinhaus R; Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung, Berlin 10117, Germany.
  • Proft S; Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 13353, Germany.
  • Seelow E; Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung, Berlin 10117, Germany.
  • Schalau T; Institut für Medizinische Genetik und Humangenetik, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 13353, Germany.
  • Robinson PN; Medizinische Klinik mit Schwerpunkt Nephrologie und Internistische Intensivmedizin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 10117, Germany.
  • Seelow D; Exploratory Diagnostic Sciences, Berliner Institut für Gesundheitsforschung, Berlin 10117, Germany.
Nucleic Acids Res ; 50(W1): W677-W681, 2022 07 05.
Article em En | MEDLINE | ID: mdl-35524573
ABSTRACT
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/.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Bases de Dados Genéticas Tipo de estudo: Diagnostic_studies Limite: Child / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Software / Bases de Dados Genéticas Tipo de estudo: Diagnostic_studies Limite: Child / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article