RESUMO
New technologies, which constantly become available for mutation detection and gene analysis, have contributed to an exponential rate of discovery of disease genes and variation in the human genome. The task of collecting and documenting this enormous amount of data in genetic databases represents a major challenge for the future of biological and medical science. The Locus Specific Databases (LSDBs) are so far the most efficient mutation databases. This review presents the main types of databases available for the analysis of mutations responsible for genetic disorders, as well as open perspectives for new therapeutic research or challenges for future medicine. Accurate and exhaustive collection of variations in human genomes will be crucial for research and personalized delivery of healthcare.
Assuntos
Bases de Dados Genéticas , Doenças Genéticas Inatas/genética , Mutação , Doenças Raras/genética , Códon de Terminação , Etnicidade/genética , Previsões , Doenças Genéticas Inatas/classificação , Doenças Genéticas Inatas/terapia , Terapia Genética , Genética Médica/ética , Genótipo , Humanos , Internet , Fenótipo , RNA Antissenso/uso terapêutico , Doenças Raras/classificação , Doenças Raras/terapia , Terminologia como Assunto , Transcrição Gênica/efeitos dos fármacosRESUMO
Our knowledge about human genes and the consequences of mutations leading to human genetic diseases has drastically improved over the last few years. It has been recognized that many mutations are indeed pathogenic because they impact the mRNA rather than the protein itself. With our better understanding of the very complex mechanism of splicing, various bioinformatics tools have been developed. They are now frequently used not only to search for sequence motifs corresponding to splicing signals (splice sites, branch points, ESE, and ESS) but also to predict the impact of mutations on these signals. We now need to address the impact of mutations that affect the splicing process, as their consequences could vary from the activation of cryptic signals to the skipping of one or multiple exons. Despite the major developments of the bioinformatics field coupled to experimental data generated on splicing, it is today still not possible to efficiently predict the consequences of mutations impacting splicing signals, especially to predict if they will lead to exon skipping or to cryptic splice site activation.