Predicting off-target effects for end-to-end CRISPR guide design

Abstract

To enable more effective guide design we have developed the first machine learning-based approach to assess CRISPR/Cas9 off-target effects. Our approach consistently and substantially outperformed the state-of the-art over multiple, independent data sets, yielding up to a 6-fold improvement in accuracy. Because of the large computational demands of the task, we also developed a cloud-based service for end-to-end guide design which incorporates our previously reported on-target model, Azimuth, as well as our new off-target model, Elevation (https://www.microsoft.com/en-us/research/project/crispr). . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was . http://dx.doi.org/10.1101/078253 doi: bioRxiv preprint first posted online Oct. 5, 2016;

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Cite this paper

@inproceedings{Listgarten2016PredictingOE, title={Predicting off-target effects for end-to-end CRISPR guide design}, author={Jennifer Listgarten and Michael H Weinstein and Melih Elibol and Luong Hoang and John G Doench and Nicol{\'o} Fusi}, year={2016} }