Extended Data Fig. 3: Additional validation of the Easy-Prime PE2 model.
From: Predicting prime editing efficiency and product purity by deep learning
a, Edit type count distribution in the original Easy-Prime test dataset. b, Evaluation of Easy-Prime PE2 model by testing this XGBoost model on the original Easy-Prime test dataset5, filtered against 1 bp edits at position 5 of the RTT to eliminate the bias towards this edit type. n = 585. c–g, Evaluation of Easy-Prime PE2 by testing the model on datasets generated in this study. c, Library 1 in HEK293T, n = 92,423. d, Library 2 (editing with PE2 and pegRNAs without tevopreQ1) in HEK293T, n = 915. e, Library 2 (editing with PE2 and pegRNAs without tevopreQ1) in K562, n = 876. f,g, Endogenous loci from Fig. 4a, b in HEK293T (f) and K562 (g), n = 45. h, Intended editing efficiency rank of the best-predicted pegRNA for each pathogenic locus in library 1 (PRIDICT and Easy-Prime). Pathogenic loci with multiple pegRNAs on rank 1 (identical efficiency) and loci with less than three pegRNAs were excluded from this analysis. Predictions from PRIDICT were taken from five different cross-validations to ensure none of the predictions are included in the training set. n = 12,189. i, Intended editing efficiency rank of the best-predicted pegRNA for each endogenous locus (PRIDICT and Easy-Prime). n = 15.
