PDF(Pubmed)
Abstract：
Predicting the impact of coding and noncoding variants on splicing is challenging, particularly in non-canonical splice sites, leading to missed diagnoses in patients. Existing splice prediction tools are complementary but knowing which to use for each splicing context remains difficult. Here, we describe Introme, which uses machine learning to integrate predictions from several splice detection tools, additional splicing rules, and gene architecture features to comprehensively evaluate the likelihood of a variant impacting splicing. Through extensive benchmarking across 21,000 splice-altering variants, Introme outperformed all tools (auPRC: 0.98) for the detection of clinically significant splice variants. Introme is available at https://github.com/CCICB/introme .
摘要：
预测编码和非编码变体对剪接的影响是具有挑战性的，特别是在非规范剪接位点，导致患者漏诊。现有的拼接预测工具是互补的,但是知道哪个用于每个拼接上下文仍然是困难的。这里,我们描述介绍，它使用机器学习来整合来自几个拼接检测工具的预测，附加拼接规则，和基因结构特征，以全面评估变体影响剪接的可能性。通过对21,000个剪接改变变体的广泛基准测试，Introme优于所有工具（auPRC：0.98）用于检测临床上有意义的剪接变体。Introme可在https://github.com/CCICB/introme获得。
