Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs


Off-target effects of the CRISPR–Cas9 system can lead to suboptimal gene-editing outcomes and are a bottleneck in its development. Here, we introduce two interdependent machine-learning models for the prediction of off-target effects of CRISPR–Cas9. The approach, which we named Elevation, scores individual guide–target pairs, and also aggregates them into a single, overall summary guide score. We demonstrate that Elevation consistently outperforms competing approaches on both tasks. We also introduce an evaluation method that balances errors between active and inactive guides, thereby encapsulating a range of practical use cases. Because of the large-scale and computational demands of the prediction of off-target activities, we have developed a fast cloud-based service ( for end-to-end guide-RNA design. The service makes use of pre-computed on-target and off-target activity prediction for every genic region in the human genome.

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Fig. 1: Schematic of Elevation off-target predictive modelling.
Fig. 2: gRNA–target pair scoring.
Fig. 3: First-layer gRNA–target scoring feature importances.
Fig. 4: Validation of the Elevation gRNA–target scoring model.
Fig. 5: Joint scoring and aggregation on viability screens.
Fig. 6: Aggregator feature importances.


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We thank A. Annavajhala for Azure cloud support, C. Kadie for use and support of his HPC cluster code, J. Jernigan, O. Losinets and the HPC team for cluster support, M. Hegde for help with the data, J. Lopez and M. Aryee for assistance with GUIDE-seq data analysis, M. Haeussler for help accessing the data from his paper, and J.-P. Concordet for feedback on the manuscript. M.W. is supported by a UCLA Collaboratory Fellowship. This work used computational and storage services associated with the Hoffman2 Shared Cluster provided by the UCLA Institute for Digital Research and Education’s Research Technology Group, and also an Azure-for-Research grant to UCLA. We acknowledge the ENCODE Consortium, the UW ENCODE group for generating these data, and UCSC for processing these data and making them available for download.

Author information

J.L. and N.F. designed, implemented and evaluated the machine learning and statistical methods (Elevation-score and Elevation-aggregate). M.W. designed and implemented the Elevation-search infrastructure, also known as dsNickFury. J.G.D. provided biological expertise. B.P.K., J.K.J., J.L., N.F. and J.G.D. selected validation gRNAs. B.P.K., A.A.S. and J.K.J. assayed the validation gRNAs for off-target activity. J.L., N.F., M.W. and J.G.D. designed the web interface. L.H. and K.G. created the front-end webpage for the cloud service. M.E. and J.C. helped run the experiments and populated the cloud server. J.L., M.W., J.G.D., N.F., B.P.K. and J.K.J. wrote the paper.

Correspondence to Jennifer Listgarten or Michael Weinstein or John G. Doench or Nicolo Fusi.

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J.L., L.H., M.E., J.C. and N.F. performed research related to this manuscript while employed by Microsoft. J.K.J. has financial interests in Beam Therapeutics, Editas Medicine, Monitor Biotechnologies, Pairwise Plants, Poseida Therapeutics and Transposagen Biopharmaceuticals. J.K.J.’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies.

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GUIDE-seq details for validation dataset 2.

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Listgarten, J., Weinstein, M., Kleinstiver, B.P. et al. Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs. Nat Biomed Eng 2, 38–47 (2018).

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