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Computational approaches to study the effects of small genomic variations

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Abstract

Advances in DNA sequencing technologies have led to an avalanche-like increase in the number of gene sequences deposited in public databases over the last decade as well as the detection of an enormous number of previously unseen nucleotide variants therein. Given the size and complex nature of the genome-wide sequence variation data, as well as the rate of data generation, experimental characterization of the disease association of each of these variations or their effects on protein structure/function would be costly, laborious, time-consuming, and essentially impossible. Thus, in silico methods to predict the functional effects of sequence variations are constantly being developed. In this review, we summarize the major computational approaches and tools that are aimed at the prediction of the functional effect of mutations, and describe the state-of-the-art databases that can be used to obtain information about mutation significance. We also discuss future directions in this highly competitive field.

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Abbreviations

TCGA:

The Cancer Genome Atlas

ICGC:

International Cancer Genome Consortium

SNP:

Single nucleotide polymorphism

HGMD:

Human Gene Mutation Database

sSNP:

Nonsynonymous SNP

OMIM:

Online Mendelian Inheritance in Man

HGV:

Human Genome Variation

PMD:

Protein Mutant Database

EVS:

Exome Variant Server

COSMIC:

Collection of somatic mutations in cancer

NCBI:

National Center for Biotechnology Information

dbSNP:

SNP Database

LSDB:

Large number of locus-specific databases

HGVS:

Human Genome Variation Society

MAF:

Minor allele frequency

MSA:

Multiple sequence alignment

PDB:

Protein Data Bank

SS:

Secondary structure

CAGI:

Critical Assessment of Genome Interpretation

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Acknowledgments

This study was partially supported by RFBR, research project no. 15-04-04730, and grant no. RFMEFI60714X0098.

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The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation

    Kamil Khafizov, Maxim V. Ivanov, Olga V. Glazova & Sergei P. Kovalenko

  2. The Institute of Molecular Biology and Biophysics, Novosibirsk, Russian Federation

    Sergei P. Kovalenko

  3. Novosibirsk State University, Novosibirsk, Russian Federation

    Sergei P. Kovalenko

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  1. Kamil Khafizov
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  2. Maxim V. Ivanov
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  3. Olga V. Glazova
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  4. Sergei P. Kovalenko
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Correspondence to Kamil Khafizov.

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Khafizov, K., Ivanov, M.V., Glazova, O.V. et al. Computational approaches to study the effects of small genomic variations. J Mol Model 21, 251 (2015). https://doi.org/10.1007/s00894-015-2794-y

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