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

ABSTRACT: The CRISPR-Cas9 system provides unprecedented genome editing capabilities. However, off-target effects lead to sub-optimal usage and additionally are a bottleneck in the development of therapeutic uses. Herein, we introduce the first machine learning-based approach to off-target prediction, yielding a state-of-the-art model for CRISPR-Cas9 that outperforms all other guide design services. Our approach, Elevation, consists of two interdependent machine learning models-one for scoring individual guide-target pairs, and another which aggregates these guide-target scores into a single, overall summary guide score. Through systematic investigation, we demonstrate that Elevation performs substantially better than competing approaches on both tasks. Additionally, we are the first to systematicall

SUBMITTER: Listgarten J 

PROVIDER: S-EPMC6037314 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature