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Department of Quantitative Biomedicine

The Krauthammer lab and collaborators publish PRIDICT: An attention-based bidirectional recurrent neural network to predict prime editing efficiency and product purity

Predicting prime editing efficiency and product purity by deep learning

Prime editing allows precise modifications of genomic DNA without introducing DNA double-strand breaks. However, high editing efficiency requires experimental optimization of the prime editing guide RNA (pegRNA). In this work, the Krauthammer lab and collaborators conducted a high-throughput screen to analyze prime editing outcomes of >90K pegRNAs on a set of >13K human pathogenic mutations. This dataset yielded sequence context features that influence prime editing and was subsequently used to train PRIDICT; an attention-based bidirectional recurrent neural network. PRIDICT reliably predicts editing rates for all small-sized genetic changes. Validation of PRIDICT, both on endogenous editing sites as well as on an external dataset, showed that pegRNAs with high versus low PRIDICT scores showed increased prime editing efficiencies in different cell types in vitro (12-fold) and in hepatocytes in vivo (tenfold), highlighting the value of PRIDICT for basic and translational research applications. PRIDICT is freely accessible at

See Mathis, Allam et al., Nature Biotechnology

The code is available on GitHub:

This work is featured on UZH News: Artificial Intelligence Improves Efficiency of Genome Editing