Computational approaches to study the effects of small genomic variations.
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.
Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russian Federation, kkhafizov@gmail.com.
Kamil
K
Khafizov
Maxim V
MV
Ivanov
Olga V
OV
Glazova
Sergei P
SP
Kovalenko
D019295:Computational Biology / Q000379:methods*; D017124:Conserved Sequence; D030561:Databases, Nucleic Acid; D019143:Evolution, Molecular; D014644:Genetic Variation; D023281:Genomics / Q000379:methods*; D006801:Humans; D009154:Mutation; D020641:Polymorphism, Single Nucleotide; D013329:Structure-Activity Relationship; D064878:Web Browser; D057188:Workflow