PT  - JOURNAL ARTICLE
AU  - Jennifer Listgarten
AU  - Michael Weinstein
AU  - Melih Elibol
AU  - Luong Hoang
AU  - John Doench
AU  - Nicolo Fusi
TI  - Predicting off-target effects for end-to-end CRISPR guide design
AID  - 10.1101/078253
DP  - 2016 Jan 01
TA  - bioRxiv
PG  - 078253
4099  - http://biorxiv.org/content/early/2016/10/05/078253.short
4100  - http://biorxiv.org/content/early/2016/10/05/078253.full
AB  - 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).