A Machine Learning Approach to Identify the Importance of Novel Features for CRISPR/Cas9 Activity Prediction.

ABSTRACT: The reprogrammable CRISPR/Cas9 genome editing tool's growing popularity is hindered by unwanted off-target effects. Efforts have been directed toward designing efficient guide RNAs as well as identifying potential off-target threats, yet factors that determine efficiency and off-target activity remain obscure. Based on sequence features, previous machine learning models performed poorly on new datasets, thus there is a need for the incorporation of novel features. The binding energy estimation of the gRNA-DNA hybrid as well as the Cas9-gRNA-DNA hybrid allowed generating better performing machine learning models for the prediction of Cas9 activity. The analysis of feature contribution towards the model output on a limited dataset indicated that energy features played a determining role alon

SUBMITTER: Vora DS 

PROVIDER: S-EPMC9405635 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature