Rules and tools to predict the splicing effects of exonic and intronic mutations. (26th September 2017)

• Record Type: Journal Article
• Authors: Ohno, Kinji; Takeda, Jun‐ichi; Masuda, Akio
• Is Part Of: Wiley interdisciplinary reviews. Volume 9:Number 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-09-26
• Subjects: RNA -- Periodicals; 572.8805
• DOI: 10.1002/wrna.1451
• Languages: English
• ISSNs: 1757-7004

Abstract

Abstract : 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 Abstract : Representative rules (red) and tools (blue) to predict the splicing effects of exonic and intronic mutations. Ex, exonic position; Int, intronic position.