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Computational SNP Analysis: Current Approaches and Future Prospects

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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.

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Abbreviations

nsSNP:

Non-synonymous single nucleotide polymorphism

MDS:

Molecular dynamics simulation

ED:

Essential dynamics

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Acknowledgments

We (AK, VR, RS & RP) gratefully acknowledge the management of Vellore Institute of Technology University for providing the facilities to carry out this work. PS acknowledges financial support by the Austrian Science Fund (FWF, Grant SFB-28). All authors thank the anonymous reviewers for their helpful comments and critical reading of the manuscript.

Conflict of interest

Authors have no potential conflict of interest to disclose.

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Authors and Affiliations

  1. Bioinformatics Division, School of Bio Sciences and Technology, Vellore Institute of Technology University, Vellore, 632014, Tamil Nadu, India

    Ambuj Kumar, Vidya Rajendran, Rao Sethumadhavan & Rituraj Purohit

  2. Department of Biomedical Sciences, Institute of Animal Breeding and Genetics, University of Veterinary Medicine, Veterinaerplatz 1, 1210, Vienna, Austria

    Priyank Shukla & Shalinee Tiwari

  3. Human Genetics Foundation, via Nizza 52, 10126, Torino, Italy

    Rituraj Purohit

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  1. Ambuj Kumar
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  2. Vidya Rajendran
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  3. Rao Sethumadhavan
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  4. Priyank Shukla
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  5. Shalinee Tiwari
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Correspondence to Rituraj Purohit.

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Ambuj Kumar and Vidya Rajendran are the joint first authors.

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Kumar, A., Rajendran, V., Sethumadhavan, R. et al. Computational SNP Analysis: Current Approaches and Future Prospects. Cell Biochem Biophys 68, 233–239 (2014). https://doi.org/10.1007/s12013-013-9705-6

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