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跳跃中出了什么问题?剪接位点变异的预测与验证

What's Wrong in a Jump? Prediction and Validation of Splice Site Variants.

作者信息

Riolo Giulia, Cantara Silvia, Ricci Claudia

机构信息

Department of Medical, Surgical and Neurological Sciences, University of Siena, 53100 Siena, Italy.

出版信息

Methods Protoc. 2021 Sep 5;4(3):62. doi: 10.3390/mps4030062.

Abstract

Alternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) involving consensus splicing regulatory sequences in a specific gene may result in the production of aberrant and not properly working proteins. In this review, we introduce the key steps of splicing mechanism and describe all different types of genomic variants affecting this process (splicing variants in acceptor/donor sites or branch point or polypyrimidine tract, exonic, and deep intronic changes). Then, we provide an updated approach to improve splice variants detection. First, we review the main computational tools, including the recent Machine Learning-based algorithms, for the prediction of splice site variants, in order to characterize how a genomic variant interferes with splicing process. Next, we report the experimental methods to validate the predictive analyses are defined, distinguishing between methods testing RNA (transcriptomics analysis) or proteins (proteomics experiments). For both prediction and validation steps, benefits and weaknesses of each tool/procedure are accurately reported, as well as suggestions on which approaches are more suitable in diagnostic rather than in clinical research.

摘要

可变剪接(Alternative splicing,AS)是增强基因表达以驱动生物体发育的关键过程。有趣的是,超过95%的人类基因会发生可变剪接,从同一转录本产生多种蛋白质异构体。特定基因中涉及共有剪接调控序列的任何改变(例如核苷酸替换、插入和缺失)都可能导致产生异常且无法正常发挥功能的蛋白质。在本综述中,我们介绍了剪接机制的关键步骤,并描述了影响这一过程的所有不同类型的基因组变异(受体/供体位点、分支点或多嘧啶序列中的剪接变异、外显子变异和内含子深处的变化)。然后,我们提供了一种改进剪接变异检测的最新方法。首先,我们综述了主要的计算工具,包括最近基于机器学习的算法,用于预测剪接位点变异,以表征基因组变异如何干扰剪接过程。接下来,我们报告了用于验证预测分析的实验方法,区分了检测RNA的方法(转录组学分析)和检测蛋白质的方法(蛋白质组学实验)。对于预测和验证步骤,我们准确报告了每种工具/程序 的优点和缺点,以及关于哪些方法更适合诊断而非临床研究的建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7e9/8482176/069ffcd4e42c/mps-04-00062-g001.jpg

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