A major challenge for effective application of CRISPR systems is to accurately predict the single guide RNA (sgRNA) on-target knockout efficacy and off-target profile, which would facilitate the optimized design of sgRNAs with high sensitivity and specificity. Here we present DeepCRISPR, a comprehensive computational platform to unify sgRNA on-target and off-target site prediction into one framework with deep learning, surpassing available state-of-the-art in silico tools. In addition, DeepCRISPR fully automates the identification of sequence and epigenetic features that may affect sgRNA knockout efficacy in a data-driven manner. DeepCRISPR is available at http://www.deepcrispr.net/.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1459-4) contains supplementary material, which is available to authorized users.

Cpf1 nucleases were recently reported to be highly specific and programmable nucleases with efficiencies comparable to those of SpCas9. AsCpf1 and LbCpf1 require a single crRNA and recognize a 5′-TTTN-3′ protospacer adjacent motif (PAM) at the 5′ end of the protospacer for genome editing. For widespread application in precision site-specific human genome editing, the range of sequences that AsCpf1 and LbCpf1 can recognize is limited due to the size of this PAM. To address this limitation, we sought to identify a novel Cpf1 nuclease with simpler PAM requirements. Specifically, here we sought to test and engineer FnCpf1, one reported Cpf1 nuclease (FnCpf1) only requires 5′-TTN-3′ as a PAM but does not exhibit detectable levels of nuclease-induced indels at certain locus in human cells. Surprisingly, we found that FnCpf1 possesses DNA cleavage activity in human cells at multiple loci. We also comprehensively and quantitatively examined various FnCpf1 parameters in human cells, including spacer sequence, direct repeat sequence and the PAM sequence. Our study identifies FnCpf1 as a new member of the Cpf1 family for human genome editing with distinctive characteristics, which shows promise as a genome editing tool with the potential for both research and therapeutic applications.

deep generative models, de novo molecule generation | INTRODUCTIONDe novo design of new molecules and analysis of their structure and properties is an important issue in computational molecular science. In the last few years, new approaches based on artificial intelligence (AI), especially deep learning models, have *These authors contributed equally to this study.