The process of splicing messenger RNA to remove introns plays a central role in creating genes and gene variants. We describe Splam, a novel method for predicting splice junctions in DNA using deep residual convolutional neural networks. Unlike previous models, Splam looks at a 400-base-pair window flanking each splice site, reflecting the biological splicing process that relies primarily on signals within this window. Splam also trains on donor and acceptor pairs together, mirroring how the splicing machinery recognizes both ends of each intron. Compared to SpliceAI, Splam is consistently more accurate, achieving 96% accuracy in predicting human splice junctions.

中文翻译：

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Splam：一种基于深度学习的剪接位点预测器，可改善剪接比对


剪接信使 RNA 以去除内含子的过程在产生基因和基因变异中起着核心作用。我们描述了 Splam，这是一种使用深度残差卷积神经网络预测 DNA 剪接连接的新方法。与以前的模型不同，Splam 着眼于每个剪接位点两侧的 400 个碱基对窗口，反映了主要依赖于该窗口内信号的生物剪接过程。Splam 还将供体和受体对一起训练，反映了剪接机制如何识别每个内含子的两端。与 SpliceAI 相比，Splam 始终更准确，在预测人类剪接连接方面的准确率达到 96%。