Variation Interpretation Predictors: Principles, Types, Performance, and Choice

Abstract

Next-generation sequencing methods have revolutionized the speed of generating variation information. Sequence data have a plethora of applications and will increasingly be used for disease diagnosis. Interpretation of the identified variants is usually not possible with experimental methods. This has caused a bottleneck that many computational methods aim at addressing. Fast and efficient methods for explaining the significance and mechanisms of detected variants are required for efficient precision/personalized medicine. Computational prediction methods have been developed in three areas to address the issue. There are generic tolerance (pathogenicity) predictors for filtering harmful variants. Gene/protein/disease-specific tools are available for some applications. Mechanism and effect-specific computer programs aim at explaining the consequences of variations. Here, we discuss the different types of predictors and their applications. We review available variation databases and prediction methods useful for variation interpretation. We discuss how the performance of methods is assessed and summarize existing assessment studies. A brief introduction is provided to the principles of the methods developed for variation interpretation as well as guidelines for how to choose the optimal tools and where the field is heading in the future.

PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants

“…In the last few years, several methods have been developed for predicting the impact of single nucleotide variants (SNVs) on human health, nevertheless only few of them are capable of assessing the effect of SNVs in non-coding regions (8). …”

Internet databases and resources for cytogenetics and cytogenomics

“…A review of L. Chin gives an overview of the current state if cancer genomics (L. Chin et al, 2011). Regardless of a wide spectrum of references, the topic of cytogenetic resources is absent (Pavlopoulou A et al, 2015 ;Klonowska K et al, 2016 ;Brookes AJ, Robinson PN and Brookes AJ, 2015 ;Yang Y et al, 2015 ;Niroula A and Vihinen M, 2016 ;Diehl AG and Boyle AP, 2016;Martincorena I, et al 2015). There are also many descriptions of database (and particularly in cancer) in all special issues of Nucleic Acid Reseach (each year in January).…”

Clinical Interpretation of Genomic Variations

“…International guidelines and recommendations developed to standardize and regulate deep sequencing are good references for researchers and clinicians [37]. …”