Pathogenicity prediction of non-synonymous single @entity4334 variants in XXXX .

@entity1 @entity296 @entity5 @entity384 @entity4334 @entity65
UNASSIGNED: Non-synonymous single @entity4334 variants (nsSNVs) in coding DNA regions can result in phenotypic differences between individuals; however, only some nsSNVs are causative for a certain disease. As just a fraction of respective nsSNVs is annotated in databases, computational biology tools are applied to predict the pathogenicity in silico. In addition to applications in oncology, novel molecular diagnostic tests have been developed for @entity65 as a leading cause of morbidity and mortality in industrialized nations. We explored the concordance and performance of 13 nsSNV pathogenicity prediction tools on panel sequencing results of @entity384 . The analyzed data set from the INHERITANCE study contained 842 nsSNVs discovered in 639 @entity1 , screened for the full sequence of 76 genes related to @entity296 . The single tools prediction revealed a surprisingly high heterogeneity and discordance based on the implemented prediction method. Known disease associations were not reported by the tools, limiting usability in clinics. Because different tools have different advantages, we combined their results. By clustering of correlated methods using similar prediction strategies and calculating a majority vote-based consensus, we found that the prediction accuracy and sensitivity can be further improved. Although challenges remain, different in silico tools bear the potential to predict the @entity5 , especially if different algorithms are combined. Most tools rely mainly on sequence features; beyond these, structural information is important to analyze the relationship of nsSNVs with disease phenotypes. Likewise, current tools consider single nsSNVs, which may, however, show a cumulative effect and turn neutral mutations in an ensemble into pathogenic variants.
