A computational approach to prioritize functionally significant variations in whole exome sequencing

Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variants which are broadly used for studying common and complex diseases. However, the tremendous number of SNPs in the human genome poses challenges to perform extensive analysis on all SNPs. Exome sequencing strategies are capable of identifying unknown SNPs which have an impact on the protein function and cause various diseases conditions. However, identifying genuine disease mutations or variants is still laborious and challenging. Here, we propose a prioritization model in order to predict functionally significant SNPs in whole exome sequencing. Our experimental results show that the proposed SNP prioritization model is effective in reliable identification of functionally significant SNPs which are more likely to be associated with disease conditions or functional impairments in massive amount of exome sequencing data. The proposed model will enable researchers and geneticists to conduct their follow up studies easily by reducing their experimental and analysis overhead.