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一组 327 项关于 BRCA1 和 BRCA2 变异的计算机模拟/体外研究得出的分子诊断中拼接分析指南。

Guidelines for splicing analysis in molecular diagnosis derived from a set of 327 combined in silico/in vitro studies on BRCA1 and BRCA2 variants.

机构信息

Service de Génétique, Institut Curie et Université Paris Descartes, Sorbonne Paris Cité, Paris, France.

出版信息

Hum Mutat. 2012 Aug;33(8):1228-38. doi: 10.1002/humu.22101. Epub 2012 May 11.

Abstract

Assessing the impact of variants of unknown significance (VUS) on splicing is a key issue in molecular diagnosis. This impact can be predicted by in silico tools, but proper evaluation and user guidelines are lacking. To fill this gap, we embarked upon the largest BRCA1 and BRCA2 splice study to date by testing 272 VUSs (327 analyses) within the BRCA splice network of Unicancer. All these VUSs were analyzed by using six tools (splice site prediction by neural network, splice site finder (SSF), MaxEntScan (MES), ESE finder, relative enhancer and silencer classification by unanimous enrichment, and human splicing finder) and the predictions obtained were compared with transcript analysis results. Combining MES and SSF gave 96% sensitivity and 83% specificity for VUSs occurring in the vicinity of consensus splice sites, that is, the surrounding 11 and 14 bases for the 5' and 3' sites, respectively. This study was also an opportunity to define guidelines for transcript analysis along with a tentative classification of splice variants. The guidelines drawn from this large series should be useful for the whole community, particularly in the context of growing sequencing capacities that require robust pipelines for variant interpretation.

摘要

评估意义不明的变异(VUS)对剪接的影响是分子诊断中的一个关键问题。这种影响可以通过计算机工具进行预测,但缺乏适当的评估和用户指南。为了填补这一空白,我们通过在 Unicancer 的 BRCA 剪接网络中测试 272 个 VUS(327 次分析),开展了迄今为止最大的 BRCA1 和 BRCA2 剪接研究。所有这些 VUS 都使用 6 种工具(神经网络预测剪接位点、剪接位点查找器(SSF)、最大熵扫描(MES)、ESE 查找器、通过一致富集的相对增强子和沉默子分类以及人类剪接发现器)进行分析,并将获得的预测结果与转录分析结果进行比较。MES 和 SSF 结合使用,在靠近共识剪接位点的 VUS 中,即 5'和 3'位点周围的 11 和 14 个碱基处,具有 96%的敏感性和 83%的特异性。这项研究还为转录分析制定了指南,并对剪接变体进行了暂定分类。从这个大型系列中得出的指南应该对整个社区都有用,特别是在测序能力不断增长的情况下,需要强大的变异解释管道。

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