Extended Data Fig. 10: Evaluation of DeepBE in predicting base editor activities at endogenous targets in three different cell lines.
From: Deep learning models to predict the editing efficiencies and outcomes of diverse base editors
a-c, Correlations between DeepBE-predicted base editing outcome frequencies and measured outcome frequencies at endogenous targets in HEK293T (a), HCT116 (b), and U20S (c) cells. The number of targets (n) = 240, 312, and 174 (a), 219, 293, and 158 (b), and 219, 330, and 181 (c) for SpCas9-NRCH-YE1-BE4max, SpCas9-NRCH-ABE8e(V106W), and SpCas9-NRCH-CGBE1, respectively. d,e, Comparison of measured base editing outcome frequencies at targets with different chromatin accessibilities (d) and different functional regions (that is, coding versus non-coding) (e). The boxes represent the 25th, 50th, and 75th percentiles; the whiskers show the 10th and 90th percentiles. The Wilcoxon rank-sum test; two-sided. The number of targets (n) = 114 (Non-DNase I hypersensitive sites (DHS)) and 33 (DHS; HEK293T), 102 and 40 (HCT116), and 98 and 28 (U2OS), 267 and 44 (HEK293T), 246 and 47 (HCT116), and 269 and 61 (U2OS), and 144 and 30 (HEK293T), 71 and 23 (HCT116), and 151 (Non-DHS) and 30 (HDS; U2OS) (d) and 79 (coding) and 68 (non-coding; HEK293T), 80 and 62 (HCT116), and 71 and 55 (U2OS), 150 and 161 (HEK293T), 149 and 144 (HCT116), and 143 and 187 (U2OS), and 78 and 96 (HEK293T), 79 and 40 (HCT116), and 89 (coding) and 92 (non-coding; U2OS) (e) for NRCH-YE1-BE4max, NRCH-ABE8e(V106W), and NRCH-CGBE1, respectively.
