Background: CRISPR is widely used to silence genes by inducing mutations expected to nullify their expression. While numerous computational tools have been developed to design single-guide RNAs (sgRNAs) with high cutting efficiency and minimal off-target effects, only a few tools focus specifically on predicting gene knockouts following CRISPR. These tools consider factors like conservation, amino acid composition, and frameshift likelihood. However, they neglect the impact of CRISPR on gene expression, which can dramatically affect the success of CRISPR-induced gene silencing attempts. Furthermore, information regarding gene expression can be useful even when the objective is not to silence a gene. Therefore, a tool that considers gene expression when predicting CRISPR outcomes is lacking.
Results: We developed EXPosition, the first computational tool that combines models predicting gene knockouts after CRISPR with models that forecast gene expression, offering more accurate predictions of gene knockout outcomes. EXPosition leverages deep-learning models to predict key steps in gene expression: transcription, splicing, and translation initiation. We showed our tool performs better at predicting gene knockout than existing tools across 6 datasets, 4 cell types and ~207k sgRNAs. We also validated our gene expression models using the ClinVar dataset by showing enrichment of pathogenic mutations in high-scoring mutations according to our models.
Conclusions: We believe EXPosition will enhance both the efficiency and accuracy of genome editing projects, by directly predicting CRISPR's effect on various aspects of gene expression. EXPosition is available at http://www.cs.tau.ac.il/~tamirtul/EXPosition . The source code is available at https://github.com/shaicoh3n/EXPosition .
© 2024. The Author(s).