EpiCRISPR: Improving CRISPR/Cas9 on- and off-target prediction by flanking sequences, epigenetic marks, and high-throughput datasets

CRISPR/Cas9 technology has revolutionized geneediting technologies. However, the endogenous gene-editing efficiency at the target site, unintended off-target sites (OTSs), and the DNA repair outcomes are affected by various factors, among them are the sequence of the site, its flanking sequences, and the epigenetic marks harboring it. Researchers have developed computational methods to predict endogenous efficiency, OTSs, and repair outcomes. However, previous studies have failed to successfully incorporate the factors that affect on- and offtarget activity, and repair outcomes, and the methods were trained and evaluated on small datasets. A recent dataset of endogenous on-target efficiencies and repair outcomes of nearly 1,600 measurements in T cells, and a recently preprocessed dataset of 78 OTS experiments in T cells provide a unique opportunity to develop a method to predict endogenous on-target efficiency, OTSs, and repair outcomes. Here, we developed Epi- CRISPROn and EpiCRISPROff to improve on-target efficiency and OTSs prediction, respectively, by combining various inputs: the site and its flanking sequences, multiple epigenetic marks, and a high-throughput-based prediction. In EpiCRISPROn and EpiCRISPROff, the additional non-sequence features improved prediction performance from an average Spearman correlation of 0.31 to 0.51 in 5-fold cross-validation and from an AUPRC of 0.436 to 0.441 on a held-out test set, respectively. Moreover, EpiCRISPROn and EpiCRISPROff were trained on one cell type and successfully generalized to other cell types. In contrast, the inclusion of flanking sequences and epigenetic features did not improve repair-outcome prediction. Furthermore, by interrogating EpiCRISPROn and EpiCRISPROff trained models we shed light on on-target and OTS editing preferences. We expect EpiCRISPROn and EpiCRISPROff to advance the field of gene editing by providing improved cell-type-aware prediction of endogenous CRISPR/Cas9 on- and off-target activity.