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PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases
BACKGROUND: Thoroughly annotated data resources are a key requirement in phenotype dependent analysis and diagnosis of diseases in the area of precision medicine. Recent work has shown that curation and systematic annotation of human phenome data can significantly improve the quality and selectivity...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5785853/ https://www.ncbi.nlm.nih.gov/pubmed/29370821 http://dx.doi.org/10.1186/s13023-018-0765-y |
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author | Adler, Angela Kirchmeier, Pia Reinhard, Julian Brauner, Barbara Dunger, Irmtraud Fobo, Gisela Frishman, Goar Montrone, Corinna Mewes, H.-Werner Arnold, Matthias Ruepp, Andreas |
author_facet | Adler, Angela Kirchmeier, Pia Reinhard, Julian Brauner, Barbara Dunger, Irmtraud Fobo, Gisela Frishman, Goar Montrone, Corinna Mewes, H.-Werner Arnold, Matthias Ruepp, Andreas |
author_sort | Adler, Angela |
collection | PubMed |
description | BACKGROUND: Thoroughly annotated data resources are a key requirement in phenotype dependent analysis and diagnosis of diseases in the area of precision medicine. Recent work has shown that curation and systematic annotation of human phenome data can significantly improve the quality and selectivity for the interpretation of inherited diseases. We have therefore developed PhenoDis, a comprehensive, manually annotated database providing symptomatic, genetic and imprinting information about rare cardiac diseases. RESULTS: PhenoDis includes 214 rare cardiac diseases from Orphanet and 94 more from OMIM. For phenotypic characterization of the diseases, we performed manual annotation of diseases with articles from the biomedical literature. Detailed description of disease symptoms required the use of 2247 different terms from the Human Phenotype Ontology (HPO). Diseases listed in PhenoDis frequently cover a broad spectrum of symptoms with 28% from the branch of ‘cardiovascular abnormality’ and others from areas such as neurological (11.5%) and metabolism (6%). We collected extensive information on the frequency of symptoms in respective diseases as well as on disease-associated genes and imprinting data. The analysis of the abundance of symptoms in patient studies revealed that most of the annotated symptoms (71%) are found in less than half of the patients of a particular disease. Comprehensive and systematic characterization of symptoms including their frequency is a pivotal prerequisite for computer based prediction of diseases and disease causing genetic variants. To this end, PhenoDis provides in-depth annotation for a complete group of rare diseases, including information on pathogenic and likely pathogenic genetic variants for 206 diseases as listed in ClinVar. We integrated all results in an online database (http://mips.helmholtz-muenchen.de/phenodis/) with multiple search options and provide the complete dataset for download. CONCLUSION: PhenoDis provides a comprehensive set of manually annotated rare cardiac diseases that enables computational approaches for disease prediction via decision support systems and phenotype-driven strategies for the identification of disease causing genes. |
format | Online Article Text |
id | pubmed-5785853 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-57858532018-02-07 PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases Adler, Angela Kirchmeier, Pia Reinhard, Julian Brauner, Barbara Dunger, Irmtraud Fobo, Gisela Frishman, Goar Montrone, Corinna Mewes, H.-Werner Arnold, Matthias Ruepp, Andreas Orphanet J Rare Dis Research BACKGROUND: Thoroughly annotated data resources are a key requirement in phenotype dependent analysis and diagnosis of diseases in the area of precision medicine. Recent work has shown that curation and systematic annotation of human phenome data can significantly improve the quality and selectivity for the interpretation of inherited diseases. We have therefore developed PhenoDis, a comprehensive, manually annotated database providing symptomatic, genetic and imprinting information about rare cardiac diseases. RESULTS: PhenoDis includes 214 rare cardiac diseases from Orphanet and 94 more from OMIM. For phenotypic characterization of the diseases, we performed manual annotation of diseases with articles from the biomedical literature. Detailed description of disease symptoms required the use of 2247 different terms from the Human Phenotype Ontology (HPO). Diseases listed in PhenoDis frequently cover a broad spectrum of symptoms with 28% from the branch of ‘cardiovascular abnormality’ and others from areas such as neurological (11.5%) and metabolism (6%). We collected extensive information on the frequency of symptoms in respective diseases as well as on disease-associated genes and imprinting data. The analysis of the abundance of symptoms in patient studies revealed that most of the annotated symptoms (71%) are found in less than half of the patients of a particular disease. Comprehensive and systematic characterization of symptoms including their frequency is a pivotal prerequisite for computer based prediction of diseases and disease causing genetic variants. To this end, PhenoDis provides in-depth annotation for a complete group of rare diseases, including information on pathogenic and likely pathogenic genetic variants for 206 diseases as listed in ClinVar. We integrated all results in an online database (http://mips.helmholtz-muenchen.de/phenodis/) with multiple search options and provide the complete dataset for download. CONCLUSION: PhenoDis provides a comprehensive set of manually annotated rare cardiac diseases that enables computational approaches for disease prediction via decision support systems and phenotype-driven strategies for the identification of disease causing genes. BioMed Central 2018-01-25 /pmc/articles/PMC5785853/ /pubmed/29370821 http://dx.doi.org/10.1186/s13023-018-0765-y Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Adler, Angela Kirchmeier, Pia Reinhard, Julian Brauner, Barbara Dunger, Irmtraud Fobo, Gisela Frishman, Goar Montrone, Corinna Mewes, H.-Werner Arnold, Matthias Ruepp, Andreas PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases |
title | PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases |
title_full | PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases |
title_fullStr | PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases |
title_full_unstemmed | PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases |
title_short | PhenoDis: a comprehensive database for phenotypic characterization of rare cardiac diseases |
title_sort | phenodis: a comprehensive database for phenotypic characterization of rare cardiac diseases |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5785853/ https://www.ncbi.nlm.nih.gov/pubmed/29370821 http://dx.doi.org/10.1186/s13023-018-0765-y |
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