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Rules and tools to predict the splicing effects of exonic and intronic mutations
http://hdl.handle.net/2237/00027899
6092f51f-22da-42de-8c78-dac07bb1c121
名前 / ファイル | ライセンス | アクション | |
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2018-04-16 | |||||
タイトル | ||||||
タイトル | Rules and tools to predict the splicing effects of exonic and intronic mutations | |||||
著者 |
Ohno, Kinji
× Ohno, Kinji× Takeda, Jun-ichi× Masuda, Akio |
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権利 | ||||||
権利情報 | "This is the peer reviewed version of the following article: [Ohno, K. , Takeda, J. and Masuda, A. (2018), Rules and tools to predict the splicing effects of exonic and intronic mutations. WIREs RNA, 9: e1451. doi:10.1002/wrna.1451], which has been published in final form at [http://doi.org/10.1002/wrna.1451]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving." | |||||
抄録 | ||||||
内容記述 | 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. | |||||
内容記述タイプ | Abstract | |||||
内容記述 | ||||||
内容記述 | ファイル公開:2019-02-01 | |||||
内容記述タイプ | Other | |||||
出版者 | ||||||
出版者 | Wiley | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプresource | http://purl.org/coar/resource_type/c_6501 | |||||
タイプ | journal article | |||||
DOI | ||||||
関連識別子 | ||||||
識別子タイプ | DOI | |||||
関連識別子 | https://doi.org/10.1002/wrna.1451 | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 17577004 | |||||
書誌情報 |
Wiley Interdisciplinary Reviews: RNA 巻 9, 号 1, p. e1451, 発行日 2018-02 |
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著者版フラグ | ||||||
値 | author |