Computational SNP Analysis: Current Approaches and Future Prospects

The computational approaches in determining disease-associated Non-synonymous single nucleotide polymorphisms (nsSNPs) have evolved very rapidly. Large number of deleterious and disease-associated nsSNP detection tools have been developed in last decade showing high prediction reliability. Despite of all these highly efficient tools, we still lack the accuracy level in determining the genotype–phenotype association of predicted nsSNPs. Furthermore, there are enormous questions that are yet to be computationally compiled before we might talk about the prediction accuracy. Earlier we have incorporated molecular dynamics simulation approaches to foster the accuracy level of computational nsSNP analysis roadmap, which further helped us to determine the changes in the protein phenotype associated with the computationally predicted disease-associated mutation. Here we have discussed on the present scenario of computational nsSNP characterization technique and some of the questions that are crucial for the proper understanding of pathogenicity level for any disease associated mutations.     

Gibberellic Acid Induces Unique Molecular Responses in ‘Thompson Seedless’ Grapes as Revealed by Non-targeted Metabolomics

Journal of Plant Growth Regulation - Compact clusters and small berry size are the major problems associated with the commercialization of table grapes. The application of gibberellic acid 3 (GA3)...