Large dataset enables prediction of repair after CRISPR–Cas9 editing in primary T cells

Understanding of repair outcomes after Cas9-induced DNA cleavage is still limited, especially in primary human cells. We sequence repair outcomes at 1,656 on-target genomic sites in primary human T cells and use these data to train a machine learning model, which we have called CRISPR Repair Outcome (SPROUT). SPROUT accurately predicts the length, probability and sequence of nucleotide insertions and deletions, and will facilitate design of SpCas9 guide RNAs in therapeutically important primary human cells. A machine learning model based on data from primary human T cells accurately predicts repair outcomes after CRISPR–Cas9 editing.