Predicting the impact of rare variants on RNA splicing in CAGI6.
Predicting the impact of rare variants on RNA splicing in CAGI6.
复制标题
预测 CAGI6 中罕见变异对 RNA 剪接的影响。
DOI：
10.1007/s00439-023-02624-3
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发表时间：
2024
期刊：
影响因子：
5.3
通讯作者：
Mount,St
中科院分区：
文献类型：
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作者：
Lord,Jenny;Oquendo,CarolinaJaramillo;Wai,HtooA;Douglas,AndrewGL;Bunyan,DavidJ;Wang,Yaqiong;Hu,Zhiqiang;Zeng,Zishuo;Danis,Daniel;Katsonis,Panagiotis;Williams,Amanda;Lichtarge,Olivier;Chang,Yuchen;Bagnall,RichardD;Mount,St
破坏剪接的变异体是临床上尚未确定的罕见疾病的常见原因。需要准确和有效的方法来预测变体对剪接的影响，以解释外显子组和基因组测序鉴定的越来越多的未知意义的变体（VUS）。在这里，我们提出了CAGI6剪接VUS挑战的结果，该挑战邀请了对56种变体的剪接影响的预测，这些变体在临床上和功能上经过验证，以确定剪接影响。在56个功能验证的变体上比较了12种预测方法的性能，沿着与SpliceAI和CADD。通过两种不同的方法实现的最大准确度为82%，一种是通过次要等位基因频率加权SpliceAI评分，另一种是应用最近发表的剪接预测管道（SPiP）。SPiP在灵敏度方面表现最佳，而结合多种预测工具和数据库信息的集成方法在特异性方面超过了所有其他方法。几种挑战方法的性能等于或超过了SpliceAI，预测方法的最终选择可能取决于实验或临床目的。至少50%的方法错误地预测了四分之一的变体，这突出了进一步改进剪接预测方法以成功应用于临床的需要。
Variants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variant’s impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here, we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. The performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. Several challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application.