From whole exome sequencing to functional study: A bioinformatic framework

Genome-wide association (GWA) studies suggest that common genetic variants explain only a modest fraction of heritable risk for common diseases, raising the question of whether rare variants account for a significant fraction of unexplained heritability. Although the proportion of structural variants and small insertions and deletions (indels; When we perform WES for specific disease such as breast cancer it is possible that we found numerous variants in various genes, but if we want to figure out which of these variants actually associated with our interest disease, requires intricate bioinformatic works. First of all you have to filter your variants ,Respectively, based upon these items: allele frequencies, data mining, various software that predict effect of variant on protein (e.g. Polyphen, SIFT), Prioritize genes using bioinformatics tools (e.g. ToppGene, GPSy) , also by using bioinformatics tools such as i-tasser we can anticipate structure of protein product of interest gene and compare it with normal protein; when you filter your data and find those variants and genes which more likely associated with disease then you should perform functional study to proof this association. In this paper we want to introduce best practical software and databases for this framework and show you how to choose best case between founded variants and genes for functional study; we also illustrate how to choose The most appropriate vector and cell line for functional analysis moreover best software for in-silico cloning before any in-vivo analysis.

کلیدواژه ها:

WES ، variants ، disease ، bioinformatic ، functional analysis