Technische Universität München, Chair of Genome Oriented Bioinformatics, Center of Life and Food Science, D-85350, Freising-Weihenstephan, Germany.
Institute for Bioinformatics and Systems Biology (IBIS), Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), D-85764, Neuherberg, Germany.
Orphanet J Rare Dis. 2018 Jan 25;13(1):22. doi: 10.1186/s13023-018-0765-y.
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.
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.
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.
在精准医学领域,针对疾病的表型依赖分析和诊断,彻底注释的数据资源是关键要求。最近的工作表明,对人类表型数据进行整理和系统注释,可以显著提高遗传性疾病解释的质量和选择性。因此,我们开发了 PhenoDis,这是一个全面的、手动注释的数据库,提供了有关罕见心脏疾病的症状、遗传和印迹信息。
PhenoDis 包含了来自 Orphanet 的 214 种罕见心脏疾病和来自 OMIM 的 94 种更多疾病。为了对疾病进行表型特征描述,我们使用生物医学文献中的文章对疾病进行了手动注释。详细描述疾病症状需要使用人类表型本体(HPO)中的 2247 个不同术语。PhenoDis 中列出的疾病通常涵盖广泛的症状,其中 28%来自“心血管异常”分支,其他来自神经(11.5%)和代谢(6%)等领域。我们收集了关于各疾病中症状频率以及与疾病相关基因和印迹数据的广泛信息。对患者研究中症状丰富度的分析表明,注释的症状中有 71%(71%)出现在特定疾病的不到一半患者中。对症状进行全面系统的特征描述,包括其频率,是基于计算机预测疾病和致病遗传变异的关键前提。为此,PhenoDis 为一整套罕见疾病提供了深入的注释,包括 ClinVar 中列出的 206 种疾病的致病性和可能致病性遗传变异信息。我们将所有结果集成到一个在线数据库(http://mips.helmholtz-muenchen.de/phenodis/)中,该数据库具有多种搜索选项,并提供完整的数据集下载。
PhenoDis 提供了一套全面的手动注释罕见心脏疾病,使通过决策支持系统进行疾病预测的计算方法和基于表型的策略能够识别致病基因。