Splam: a deep-learning-based splice site predictor that improves spliced alignments.

Journal: Genome biology
PMID: 39285451

Abstract

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.

Authors

  • Kuan-Hao Chao
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA. kh.chao@cs.jhu.edu.
  • Alan Mao
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
  • Steven L Salzberg
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA.
  • Mihaela Pertea
    Department of Computer Science, Johns Hopkins University, Baltimore, MD, 21218, USA. mpertea@jhu.edu.

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