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. Author manuscript; available in PMC: 2019 May 14.
Published in final edited form as: Nature. 2018 Nov 7;563(7733):646–651. doi: 10.1038/s41586-018-0686-x

Fig. 1 |. High-throughput assaying of Cas9-mediated DNA repair products supports the design of the inDelphi model.

Fig. 1 |

a, A high-throughput genome-integrated library for assaying Cas9 editing products. b, Categories of editing products at 1,996 Lib-A target sites in mouse embryonic stem cells (mESCs). c, Categories of editing products in 89 VO endogenous target sites in HEK293 cells. d, Mechanism of microhomology-mediated end-joining repair. e, inDelphi uses machine learning to predict the frequencies of editing products from target DNA sequence (selected outcomes depicted in table). Major editing outcomes include +1 to −60 indels.