Expression and Codon Usage Enhance CRISPR Efficiency Prediction

The research addresses a key limitation in CRISPR technology: the challenge of designing guide RNAs (gRNAs) that are both highly efficient and specific to their target sites. Traditional predictive models primarily rely on sequence-based features, which often fall short in predicting CRISPR efficiency.

In contrast, the new study explores features derived from the genomic position of the target site, particularly focusing on nearby gene expression and codon usage. These features were found to perform comparably to, or better than, many state-of-the-art sequence-based features.

Using data from T cells, HEK293 cells, and U2OS cells, the researchers demonstrated that these CUB and expression features correlate strongly with CRISPR efficiency. When integrated into predictive models, these features led to significant improvements, boosting predictive accuracy by up to 39%.

The study concludes that incorporating such features, which are relatively easy to calculate and widely applicable, could greatly enhance our understanding and application of CRISPR technology in various biological contexts.

This research was conducted by Shaked Bergman and Tamir Tuller at Tel Aviv University, Israel, and it was published in Systems Biology and Applications on 3 September 2024.