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Exome sequencing used for molecular diagnosis of Mendelian disorders considerably increases the number of missense variants of unclear significance, whose pathogenicity can be assessed by a variety of prediction tools. As the performance of algorithms may vary according to the datasets, complementary specific resources are needed to improve variant interpretation. As a model, we were interested in the cystic fibrosis transmembrane conductance regulator gene (CFTR) causing cystic fibrosis, in which at least 40% of missense variants are reported. Cystic fibrosis missense analysis (CYSMA) is a new web server designed for online estimation of the pathological relevance of CFTR missense variants. CYSMA generates a set of computationally derived data, ranging from evolutionary conservation to functional observations from three-dimensional structures, provides all available allelic frequencies, clinical observations, and references for functional studies. Compared to software classically used in analysis pipelines on a dataset of 141 well-characterized missense variants, CYSMA was the most efficient tool to discriminate benign missense variants, with a specificity of 85%, and very good sensitivity of 89%. These results suggest that such integrative tools could be adapted to numbers of genes involved in Mendelian disorders to improve the interpretation of missense variants identified in the context of diagnosis., (© 2019 Wiley Periodicals, Inc.)

Most of the 2,000 variants identified in the CFTR (cystic fibrosis transmembrane regulator) gene are rare or private. Their interpretation is hampered by the lack of available data and resources, making patient care and genetic counseling challenging. We developed a patient-based database dedicated to the annotations of rare CFTR variants in the context of their cis- and trans-allelic combinations. Based on almost 30 years of experience of CFTR testing, CFTR-France (https://cftr.iurc.montp.inserm.fr/cftr) currently compiles 16,819 variant records from 4,615 individuals with cystic fibrosis (CF) or CFTR-RD (related disorders), fetuses with ultrasound bowel anomalies, newborns awaiting clinical diagnosis, and asymptomatic compound heterozygotes. For each of the 736 different variants reported in the database, patient characteristics and genetic information (other variations in cis or in trans) have been thoroughly checked by a dedicated curator. Combining updated clinical, epidemiological, in silico, or in vitro functional data helps to the interpretation of unclassified and the reassessment of misclassified variants. This comprehensive CFTR database is now an invaluable tool for diagnostic laboratories gathering information on rare variants, especially in the context of genetic counseling, prenatal and preimplantation genetic diagnosis. CFTR-France is thus highly complementary to the international database CFTR2 focused so far on the most common CF-causing alleles., (© 2017 Wiley Periodicals, Inc.)

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., (Copyright © 2009 Elsevier Masson SAS. All rights reserved.)

With the increasing knowledge of cystic fibrosis (CF) and CFTR-related diseases (CFTR-RD), the number of sequence variations in the CFTR gene is constantly raising. CF and particularly CFTR-RD provide a particular challenge because of many unclassified variants and identical genotypes associated with different phenotypes. Using the Universal Mutation Database (UMD) software we have constructed UMD-CFTR (freely available at the URL: http://www.umd.be/CFTR/), the first comprehensive relational CFTR database that allows an in-depth analysis and annotation of all variations identified in individuals whose CFTR genes have been analyzed extensively. The system has been tested on the molecular data from 757 patients (540 CF and 217 CBAVD) including disease-causing, unclassified, and nonpathogenic alterations (301 different sequence variations) representing 3,973 entries. Tools are provided to assess the pathogenicity of mutations. UMD-CFTR also offers a number of query tools and graphical views providing instant access to the list of mutations, their frequencies, positions and predicted consequences, or correlations between genotypes, haplotypes, and phenotypes. UMD-CFTR offers a way to compile not only disease-causing genotypes but also haplotypes. It will help the CFTR scientific and medical communities to improve sequence variation interpretation, evaluate the putative influence of haplotypes on mutations, and correlate molecular data with phenotypes., (Copyright 2010 Wiley-Liss, Inc.)