title: Computational SNP analysis: current approaches and future prospects.
abstract: 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.
authors.affiliation: Bioinformatics Division, School of Bio Sciences and Technology, Vellore Institute of Technology University, Vellore, 632014, Tamil Nadu, India.
authors.forename: Ambuj
authors.initials: A
authors.lastname: Kumar
authors.forename: Vidya
authors.initials: V
authors.lastname: Rajendran
authors.forename: Rao
authors.initials: R
authors.lastname: Sethumadhavan
authors.forename: Priyank
authors.initials: P
authors.lastname: Shukla
authors.forename: Shalinee
authors.initials: S
authors.lastname: Tiwari
authors.forename: Rituraj
authors.initials: R
authors.lastname: Purohit
mesh_terms: D019295:Computational Biology / Q000639:trends; D056726:Genetic Association Studies; D060148:HapMap Project; D006801:Humans; D056004:Molecular Dynamics Simulation; D009369:Neoplasms / Q000235:genetics / Q000473:pathology; D020641:Polymorphism, Single Nucleotide; D060388:Support Vector Machine