Jornal de Proteômica e Bioinformática

Jornal de Proteômica e Bioinformática
Acesso livre

ISSN: 0974-276X


In silico Analysis of Non-Synonymous Coding Single Nucleotide Polymorphisms (SNPs) in Human BRCA2 Gene

Benyam Zenebe*, Hellen Nigussie, Gurja Belay, Nigussie Seboka

Single nucleotide polymorphism play a vital role in understanding the genetic basis of numerous complex human diseases but the identification of functional SNPs in a disease related gene still continuous to be a major challenge. In spite of the fact that recent advances in DNA sequencing techniques have led to an increase in the identification of SNPs in human BRCA genes, further information regarding the deleterious probability of many variants is not available for those SNPs classified as ‘variants of unknown significance’ and ‘variants with conflicting interpretations of pathogenicity’. The objective of this study was to analyze non synonymous coding SNPs of human BRCA2 gene stated as ‘SNPs with conflicting interpretations of pathogenicity’ in NCBI by i n silico method in order to predict deleterious SNPS which may potentially be manipulated for diagnostic and management purposes of different pathologic conditions. Human BRCA2 gene SNPs were retrieved from the National Center for Biotechnology Information (NCBI) database, dbSNP GeneMANIA ver. was applied to study interaction of BRCA2 with other genes. Sorting Intolerant From Tolerant (SIFT) online software was applied for the in silico prediction of functional effects of the BRCA2 coding missense SNPs and the molecular phenotypic effects of selected deleterious mutant SNPs on the resultant protein were analyzed by using hope online server. 302 non synonymous coding SNPs were retrieved from NCBI out of which 88 were found to deleterious with SIFT score of <0.05. 20 of the deleterious SNPs were very deleterious with SIFT score of 0.0. The phenotypic effect of selected very deleterious SNPs was predicted by project hope web server and the findings suggest that these SNPs can be correlated with different human clinical conditions.