Introduction: 5' splice site GT>GC or +2T>C variants have been frequently reported to cause human genetic disease and are routinely scored as pathogenic splicing mutations. However, we have recently demonstrated that such variants in human disease genes may not invariably be pathogenic. Moreover, we found that no splicing prediction tools appear to be capable of reliably distinguishing those +2T>C variants that generate wild-type transcripts from those that do not. Methodology Herein, we evaluated the performance of a novel deep learning-based tool, SpliceAI, in the context of three datasets of +2T>C variants, all of which had been characterized functionally in terms of their impact on pre-mRNA splicing. The first two datasets refer to our recently described “in vivo” dataset of 45 known disease-causing +2T>C variants and the “in vitro” dataset of 103 +2T>C substitutions subjected to full-length gene splicing assay. The third dataset comprised 12 BRCA1 +2T>C variants that were recently analyzed by saturation genome editing. Results Comparison of the SpliceAI-predicted and experimentally obtained functional impact assessments of these variants (and smaller datasets of +2T>A and +2T>G variants) revealed that although SpliceAI performed rather better than other prediction tools, it was still far from perfect. A key issue was that the impact of those +2T>C (and +2T>A) variants that generated wild-type transcripts represents a quantitative change that can vary from barely detectable to an almost full expression of wild-type transcripts, with wild-type transcripts often co-existing with aberrantly spliced transcripts. Conclusion Our findings highlight the challenges that we still face in attempting to accurately identify splice-altering variants.

中文翻译：

---

5' 剪接位点 GT>GC 变体的实验获得的功能影响评估与预测的显着不同

简介：5' 剪接位点 GT>GC 或 +2T>C 变异经常被报道会导致人类遗传疾病，并且通常被视为致病性剪接突变。然而，我们最近已经证明，人类疾病基因中的这种变异可能并不总是致病。此外，我们发现没有剪接预测工具似乎能够可靠地区分产生野生型转录本的 +2T>C 变体和不产生野生型转录本的那些。方法在此，我们在三个 +2T>C 变体数据集的背景下评估了一种新的基于深度学习的工具 SpliceAI 的性能，所有这些数据集都在其对前 mRNA 剪接的影响方面进行了功能表征。前两个数据集是指我们最近描述的 45 种已知引起疾病的“体内”数据集 +2T> C 变体和 103 个 +2T>C 替换的“体外”数据集进行全长基因剪接测定。第三个数据集包含最近通过饱和基因组编辑分析的 12 个 BRCA1 +2T>C 变体。结果 SpliceAI 预测和实验获得的对这些变体（以及 +2T>A 和 +2T>G 变体的较小数据集）的功能影响评估的比较表明，尽管 SpliceAI 的性能优于其他预测工具，但仍远非完美. 一个关键问题是，产生野生型转录本的那些 +2T>C（和 +2T>A）变体的影响代表了一种数量变化，从几乎无法检测到野生型转录本的几乎完全表达，野生型型转录本通常与异常剪接的转录本共存。