RESUMO
Mutational hotspots are DNA regions with an abnormally high frequency of genetic variants. Identifying whether a variant is located in a mutational hotspot is critical for determining the variant's role in disorder predisposition, development, and treatment response. Despite their significance, current databases on mutational hotspots are limited to the oncology domain. However, identifying mutational hotspots is critical for any disorder in which genetics plays a role. This is true for the world's leading cause of death: cardiac disorders. In this work, we present CardioHotspots, a literature-based database of manually curated hotspots for cardiac diseases. This is the only database we know of that provides high-quality and easily accessible information about hotspots associated with cardiac disorders. CardioHotspots is publicly accessible via a web-based platform (https://genomics-hub.pros.dsic.upv.es:3099/). Database URL: https://genomics-hub.pros.dsic.upv.es:3099/.
Assuntos
Bases de Dados Genéticas , Cardiopatias , Mutação , Humanos , Cardiopatias/genéticaRESUMO
The use in the clinical practice of the vast amount of genomic data generated by current sequencing technologies constitutes a bottleneck for the progress of Precision Medicine (PM). Various problems inherent to the genomics domain (i.e., dispersion, heterogeneity, discrepancies, lack of standardization, and data quality issues) remain unsolved. In this paper, we present the Delfos platform, a conceptual model-based solution developed following a rigorous methodological and ontological background, whose main aim is to minimize the impact of these problems when transferring the research results to clinical practice. This paper presents the SILE method that provides methodological support for the Delfos platform, the Conceptual Schema of the Genome that provides a shared understanding of the domain, and the technological architecture behind the implementation of the platform. This paper also exemplifies the use of the Delfos platform through two use cases that involve the study of the DNA variants associated with the risk of developing Dilated Cardiomyopathies and Neuroblastoma.
RESUMO
BACKGROUND: Genomics-based clinical diagnosis has emerged as a novel medical approach to improve diagnosis and treatment. However, advances in sequencing techniques have increased the generation of genomics data dramatically. This has led to several data management problems, one of which is data dispersion (i.e., genomics data is scattered across hundreds of data repositories). In this context, geneticists try to remediate the above-mentioned problem by limiting the scope of their work to a single data source they know and trust. This work has studied the consequences of focusing on a single data source rather than considering the many different existing genomics data sources. METHODS: The analysis is based on the data associated with two groups of disorders (i.e., oncology and cardiology) accessible from six well-known genomic data sources (i.e., ClinVar, Ensembl, GWAS Catalog, LOVD, CIViC, and CardioDB). Two dimensions have been considered in this analysis, namely, completeness and concordance. Completeness has been evaluated at two levels. First, by analyzing the information provided by each data source with regard to a conceptual schema data model (i.e., the schema level). Second, by analyzing the DNA variations provided by each data source as related to any of the disorders selected (i.e., the data level). Concordance has been evaluated by comparing the consensus among the data sources regarding the clinical relevance of each variation and disorder. RESULTS: The data sources with the highest completeness at the schema level are ClinVar, Ensembl, and CIViC. ClinVar has the highest completeness at the data level data source for the oncology and cardiology disorders. However, there are clinically relevant variations that are exclusive to other data sources, and they must be considered in order to provide the best clinical diagnosis. Although the information available in the data sources is predominantly concordant, discordance among the analyzed data exist. This can lead to inaccurate diagnoses. CONCLUSION: Precision medicine analyses using a single genomics data source leads to incomplete results. Also, there are concordance problems that threaten the correctness of the genomics-based diagnosis results.