Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Wiley Interdiscip Rev RNA. 2018 Jan;9(1). doi: 10.1002/wrna.1451. Epub 2017 Sep 26.
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.
下一代测序技术的发展使得能够在人类疾病中检测到广泛的种系和体细胞单核苷酸变异 (SNV)。影响内含子 GT-AG 二核苷酸的 SNV 总是会损害前体 mRNA 的剪接。大多数外显子 SNV 引入错义/无义密码子，但有些影响辅助剪接顺式元件或产生隐藏的 GT-AG 二核苷酸。同样，大多数内含子 SNV 是沉默的，但有些影响规范和辅助剪接顺式元件或产生隐藏的 GT-AG 二核苷酸。然而，SNV 剪接效应的预测具有挑战性。产生隐藏 AG 或破坏规范 AG 的 SNV 的剪接效应可以从 AG 扫描模型推断出来。同样，影响外显子第一个核苷酸 G 的 SNV 的剪接效应可以从 3' 剪接位点 (ss) 对 AG 的依赖性推断出来。已经开发了多种工具来预测影响 5' ss 的 SNV 的剪接效应，以及外显子和内含子剪接增强子/沉默子。相比之下，只有两种工具，Human Splicing Finder 和 SVM-BP finder，可用于预测分支点序列的位置。同样，IntSplice 和基于剪接的变异分析 (SPANR) 是唯一可用于预测内含子 SNV 剪接效应的工具。本文介绍的规则和工具主要基于对有限数量基因的观察，没有任何规则或工具可以保证 100%的准确性。在得出任何与临床相关的结论之前，总是需要进行实验验证。然而，开发有效的预测异常剪接的工具将有助于我们理解人类疾病中的剪接病理机制。WIREs RNA 2018, 9:e1451. doi: 10.1002/wrna.1451 本文属于以下类别: RNA 处理 > 剪接调控/可变剪接 RNA 在疾病与发展中的作用 > RNA 在疾病中的作用 RNA 方法 > RNA 体外与计算分析
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