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Rules and tools to predict the splicing effects of exonic and intronic mutations

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Development of next generation sequencing technologies has enabled detection of extensive arrays of germline and somatic single nucleotide variations (SNVs) in human diseases. SNVs affecting intronic GT‐AG dinucleotides invariably compromise pre‐mRNA splicing. Most exonic SNVs introduce missense/nonsense codons, but some affect auxiliary splicing cis‐elements or generate cryptic GT‐AG dinucleotides. Similarly, most intronic SNVs are silent, but some affect canonical and auxiliary splicing cis‐elements or generate cryptic GT‐AG dinucleotides. However, prediction of the splicing effects of SNVs is challenging. The splicing effects of SNVs generating cryptic AG or disrupting canonical AG can be inferred from the AG‐scanning model. Similarly, the splicing effects of SNVs affecting the first nucleotide G of an exon can be inferred from AG‐dependence of the 3′ splice site (ss). A variety of tools have been developed for predicting the splicing effects of SNVs affecting the 5′ ss, as well as exonic and intronic splicing enhancers/silencers. In contrast, only two tools, the Human Splicing Finder and the SVM‐BP finder, are available for predicting the position of the branch point sequence. Similarly, IntSplice and Splicing based Analysis of Variants (SPANR) are the only tools to predict the splicing effects of intronic SNVs. The rules and tools introduced in this review are mostly based on observations of a limited number of genes, and no rule or tool can ensure 100% accuracy. Experimental validation is always required before any clinically relevant conclusions are drawn. Development of efficient tools to predict aberrant splicing, however, will facilitate our understanding of splicing pathomechanisms in human diseases. WIREs RNA 2018, 9:e1451. doi: 10.1002/wrna.1451 This article is categorized under: RNA Processing > Splicing Regulation/Alternative Splicing RNA in Disease and Development > RNA in Disease RNA Methods > RNA Analyses In Vitro and In Silico
Essential and auxiliary splicing cis‐elements. (a) Essential splicing cis‐elements and the cognate splicing trans‐factors form the spliceosome complex E. The 5′ splice site (ss) (CAG/GUAAGU), the branch point sequence (BPS) (yUNAy), the polypyrimidine tract (PPT) (Yn), and the 3′ ss (NYAG/G) are recognized by U1snRNP, SF1, U2AF65, and U2AF35, respectively, in the earliest step of spliceosome formation (E complex). Y = C/U and N = any nucleotide. (b) Auxiliary splicing cis‐elements. These cis‐elements are categorized into positively modulating intronic/exonic splicing enhancers (ISEs/ESEs) and negatively modulating intronic/exonic splicing silencers (ISSs/ESSs). Recognition of the ss's is promoted by enhancer elements and repressed by silencer elements. Most alternative splicing events are regulated by multiple auxiliary splicing cis‐elements. Enhancers may antagonize the activity of silencers, and vice versa. Exon inclusion or skipping is finely regulated by the relative strength of these influential elements and the cognate splicing trans‐factors. (c) Representative rules (red) and tools (blue) to predict the splicing effects of single nucleotide variations (SNVs). Refer to the main text for the other tools. Int, intronic position; Ex, exonic position.
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IntSplice web service program at http://www.med.nagoya‐u.ac.jp/neurogenetics/IntSplice. The program accepts a variant call format (VCF) file, which includes seven features from CHROM to FILTER according to the VCF file format. Among these, CHROM, POS, REF, and ALT are required for IntSplice. Predicted results are shown in the ‘RESULT’ column. The rightmost ‘NOTE’ column indicates which exon in which ENSEMBL transcript is predicted to be abnormally or normally spliced.
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SD‐Score web service program at http://www.med.nagoya‐u.ac.jp/neurogenetics/SD_Score/sd_score.html. The program accepts wild‐type and mutant 5′ ss sequences spanning three nucleotides at the 3′ end of an exon and nine nucleotides at the 5′ end of an intron. (a) The program returns differences in the SD‐Score (ΔSD‐Score), the information contents (ΔRi), and the position weight matrix (ΔCV), as well as the predicted splicing consequence. (b) Clicking on the wild‐type 9‐nt sequence in (a) shows a simulation of all possible single nucleotide variations.
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RNA Processing > Splicing Regulation/Alternative Splicing
RNA in Disease and Development > RNA in Disease
RNA Methods > RNA Analyses In Vitro and In Silico

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