INTRODUCTION: Each newly-sequenced genome reveals an average of 300,000 novel single nucleotide variations (SNVs). The NCBI database presents millions of described human mutations; however, many of them are still not classified according to their disease-causing potential. One of the main interests in human genome research is to discover whether specific non-synonymous SNVs affect human health. In this scenario, the computational approach of Bioinformatics comes as a great ally of experimental methodology. OBJECTIVE: The goals of this work are the development of a human-curated database called SNPMOL, which permits the user to interact with the mutant protein structures analyzed by our group. MATERIALS AND METHODS: The database was developed with HTML and JSmol code, including SNP prediction algorithms results (SNPs & GO, Polyphen2, SNAP, Pmut, Sift, PhD-SNP, nsSNP Analyzer, SNPeffect), and mutated structure models that were computationally predicted. The 3D visualization scripts, JSmol, assist the user to interact with the mutant protein structures. RESULTS AND DISCUSSION: All functional and structural results in our work can be searched on our free SNPMOL database. The data contained on the site were individually and manually performed for each algorithm. The database interface allows users to search for a mutation by its non-synonymous SNV. The SNPMOL database allows users to quickly retrieve and analyze the predicted effects of protein variants. CONCLUSION: The SNPMOL database (available at http://bioinfogroup.com/database) combines structural and functional analyses of SNVs; it is a vast resource for the molecular analysis of genetic diseases, which permits the user to better understand a disease and its molecular basis.
Comissão Organizadora
Ciências e Cognição
Comissão Científica
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