BoostMEC: predicting CRISPR-Cas9 cleavage efficiency through boosting models.

ABSTRACT:

Background

In the CRISPR-Cas9 system, the efficiency of genetic modifications has been found to vary depending on the single guide RNA (sgRNA) used. A variety of sgRNA properties have been found to be predictive of CRISPR cleavage efficiency, including the position-specific sequence composition of sgRNAs, global sgRNA sequence properties, and thermodynamic features. While prevalent existing deep learning-based approaches provide competitive prediction accuracy, a more interpretable model is desirable to help understand how different features may contribute to CRISPR-Cas9 cleavage efficiency.

Results

We propose a gradient boosting approach, utilizing LightGBM to develop an integrated tool, BoostMEC (Boosting Model for Efficient CRISPR), for the prediction of wild-type CRISPR-

SUBMITTER: Zarate OA 

PROVIDER: S-EPMC9597963 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature