Included in crispr theme

Altmetric score 20.15 (top 5.1%)

Editor:
Type:

Open peer-review

Review content is open, signing review is optional.

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


Created on 5th October 2016

Jennifer Listgarten; Michael Weinstein; Melih Elibol; Luong Hoang; John Doench; Nicolo Fusi;


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).

Show more

Review Summary

This paper has 0 completed reviews and 0 reviews in progress.

# Status Date



Name:
Email: