Fig. 2: Identification of features of highly efficient pegRNAs.
From: High-throughput evaluation of genetic variants with prime editing sensor libraries
a, Spearman correlations between various features of pegRNA design and correct editing percentage, assessed for all pegRNA-sensor pairs with sufficient reads. Each dot represents a separate replicate/timepoint. For Doench 2016 score, see ref. 46. b, Relationship between PEGG score and average correct editing percentage at each timepoint and condition is increasing monotonically. c, Representative example of the correlation between PEGG score and editing efficiency for day 25 replicate 1 (D25-REP1) (Untreated). d, Visualization of the protospacer bias in editing efficiency. The number of pegRNAs generated per protospacer at each TP53 exon on the plus (+) or minus (−) strand (top) and the average editing efficiency at each of these protospacers at day 34 (D34) of the untreated condition (bottom). e, Average editing efficiency for SNV-generating pegRNAs in the library as a function of distance to the nick generated by PEmax and PBS length. The location of the ‘NGG’ PAM sequence is highlighted in blue. Protospacer disrupting (locations +1 to +3) and PAM-disrupting variants (locations +5 and +6) tend to be more efficient. f, Feature importance of 20 random forest models trained separately to predict pegRNA efficiency. Each dot represents a different model. Data are presented as mean values with a 95% confidence interval. Source data and code to reproduce this figure can be found at https://github.com/samgould2/p53-prime-editing-sensor/blob/main/figure2.ipynb. NUT, Nutlin-3-treated.
