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<p>CRISPR-derived base editors (BE) enable precise single nucleotide substitution without introducing double-stranded DNA breaks. Apart from the base editing enzymes, efficient base editing strongly depends on both the CRISPR guide RNA (gRNA) efficiency and the edited position. Here, we show that the accuracy of BE gRNA design can be significantly improved by generating more data and by introducing deep neural networks trained on multiple different datasets simultaneously. Generating ~20,000 gRNAs for A•T to G•C and C•G to T•A conversions, we present such deep learning models, which also allow users to do dataset-aware predictions. The methods are available online and as stand-alone software.</p>

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Deep learning models simultaneously trained on multiple datasets improve base-editing activity prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月7日

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  • Ying Sun,
  • Kunli Qu,
  • Giulia I. Corsi,
  • Christian Anthon,
  • Xiaoguang Pan,
  • Xi Xiang,
  • Lars Juhl Jensen,
  • Lin Lin,
  • Yonglun Luo,
  • Jan Gorodkin

摘要

CRISPR-derived base editors (BE) enable precise single nucleotide substitution without introducing double-stranded DNA breaks. Apart from the base editing enzymes, efficient base editing strongly depends on both the CRISPR guide RNA (gRNA) efficiency and the edited position. Here, we show that the accuracy of BE gRNA design can be significantly improved by generating more data and by introducing deep neural networks trained on multiple different datasets simultaneously. Generating ~20,000 gRNAs for A•T to G•C and C•G to T•A conversions, we present such deep learning models, which also allow users to do dataset-aware predictions. The methods are available online and as stand-alone software.