Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21211, USA.
Genome Biol. 2024 Sep 16;25(1):243. doi: 10.1186/s13059-024-03379-4.
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.
拼接信使 RNA 以去除内含子的过程在基因和基因变异的产生中起着核心作用。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 中剪接位点的新方法。与以前的模型不同，Splam 观察每个剪接位点侧翼的 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 还同时对供体和受体对进行训练，反映了剪接机制如何识别每个内含子的两端。与 SpliceAI 相比，Splam 的准确性始终更高，在预测人类剪接位点方面达到 96%的准确率。
