Houdayer Claude
Faculty of Pharmacy, Institut Curie, Paris Descartes University, Paris, France.
Methods Mol Biol. 2011;760:269-81. doi: 10.1007/978-1-61779-176-5_17.
It appears that all types of genomic nucleotide variations can be deleterious by affecting normal pre-mRNA splicing via disruption/creation of splice site consensus sequences. As it is neither pertinent nor realistic to perform functional testing for all of these variants, it is important to identify those that could lead to a splice defect in order to restrict experimental transcript analyses to the most appropriate cases. In silico tools designed to provide this type of prediction are available. In this chapter, we present in silico splice tools integrated in the Alamut (Interactive Biosoftware) application and detail their use in routine diagnostic applications. At this time, in silico predictions are useful for variants that decrease the strength of wild-type splice sites or create a cryptic splice site. Importantly, in silico predictions are not sufficient to classify variants as neutral or deleterious: they should be used as part of the decision-making process to detect potential candidates for splicing anomalies, prompting molecular geneticists to carry out transcript analyses in a limited and pertinent number of cases which could be managed in routine settings.
似乎所有类型的基因组核苷酸变异都可能通过破坏/创建剪接位点共有序列来影响正常的前体mRNA剪接，从而具有有害性。由于对所有这些变异进行功能测试既不相关也不现实，因此识别那些可能导致剪接缺陷的变异很重要，以便将实验转录本分析限制在最合适的病例中。有旨在提供此类预测的计算机工具。在本章中，我们介绍了集成在Alamut（交互式生物软件）应用程序中的计算机剪接工具，并详细说明了它们在常规诊断应用中的使用。目前，计算机预测对于降低野生型剪接位点强度或创建隐蔽剪接位点的变异很有用。重要的是，计算机预测不足以将变异分类为中性或有害：它们应用作决策过程的一部分，以检测剪接异常的潜在候选者，促使分子遗传学家在有限且相关的病例中进行转录本分析，这些病例可以在常规环境中处理。
