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.

Coupling between Protein Stability and Catalytic Activity Determines Pathogenicity of G6PD Variants

“…Because structural location (and therefore sequence position) is a major contributor to mutation severity (Adzhubei et al, 2010; Kumar et al, 2014; Ng and Henikoff, 2003), we examined how many of these uncharacterized variants occur at the same amino acid position as known pathogenic variants. Overlap between uncharacterized and pathogenic variants would suggest that the overlapping uncharacterized variants are also likely to cause pathology.…”

Cytochrome b shows signs of adaptive protein evolution in Gerbillus species from Egypt

“…The Bioinformatics Resource Portal (http://web.expasy.org/translate/) was used to translate the DNA sequence of cytb gene into its respective protein. Functional effect predictions of nonsynonymous single nucleotide polymorphisms (nsSNPs) were achieved using PROVEAN (Protein Variation Effect Analyzer) (http://provean.jcvi.org/) (Kumar et al 2014), which measures the damaging effect of variations in protein sequences ). The prediction is based on the change in the similarity of the sequence to related protein sequences in a MSA by a delta alignment score of the reference and the variant carrying protein sequence with respect to the alignment of homologous sequences (Choi 2012).…”

Comparison of in silico prediction and experimental assessment of ABCB4 variants identified in patients with biliary diseases

“…A number of bioinformatic programs have been developed as a surrogate. These tools are based on different features (Thusberg and Vihinen, 2009) including the physicochemical and structural properties of amino acid residues (Ng and Henikoff, 2006) or the evolutionary conservation (Kumar et al, 2014). The relevance of these tools to predict the degree of damaging of a given substitution of ABCB4 has never been explored.…”