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Precision CRISPR–Cas9-mediated genome engineering remains challenging, particularly gene integration and editing in non-dividing cells. We present Pythia, a deep learning solution that forecasts optimal repair templates and enables predictable and accurate genome editing in diverse cellular contexts, both in vivo and in vitro.
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References
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van Overbeek, M. et al. DNA repair profiling reveals nonrandom outcomes at Cas9-mediated breaks. Mol. Cell 63, 633–646 (2016). This study demonstrates that DNA repair outcomes following Cas9 cleavage are non-random and largely determined by the target sequence.
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Anzalone, A. V. et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature 576, 149–157 (2019). This study introduces prime editing, which uses a Cas9–reverse transcriptase and pegRNA-encoded template to install precise edits without double-strand breaks or separate donor DNA.
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Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016). This study introduces cytosine base editing using a Cas9 nickase–cytidine deaminase fusion that converts C>T (G>A) without double-stranded breaks or donor DNA.
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This is a summary of: Naert, T. et al. Precise, predictable genome integrations by deep-learning-assisted design of microhomology-based templates. Nat. Biotechnol. https://doi.org/10.1038/s41587-025-02771-0 (2025).
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Pythia provides deep learning-driven precision in CRISPR–Cas9 genome engineering.
Nat Biotechnol (2025). https://doi.org/10.1038/s41587-025-02818-2
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DOI: https://doi.org/10.1038/s41587-025-02818-2