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Predicting RNA splicing from DNA sequence using Pangolin.

Tony ZengYang I Li
Published in: Genome biology (2022)
Recent progress in deep learning has greatly improved the prediction of RNA splicing from DNA sequence. Here, we present Pangolin, a deep learning model to predict splice site strength in multiple tissues. Pangolin outperforms state-of-the-art methods for predicting RNA splicing on a variety of prediction tasks. Pangolin improves prediction of the impact of genetic variants on RNA splicing, including common, rare, and lineage-specific genetic variation. In addition, Pangolin identifies loss-of-function mutations with high accuracy and recall, particularly for mutations that are not missense or nonsense, demonstrating remarkable potential for identifying pathogenic variants.
Keyphrases
  • deep learning
  • nucleic acid
  • circulating tumor
  • cell free
  • single molecule
  • gene expression
  • convolutional neural network
  • copy number
  • genome wide
  • intellectual disability
  • working memory
  • dna methylation