CRISPR-VAE: An Interpretable and Efficiency-aware gRNA Sequence Generator
Abstract
Deep learning has shown significant potential in predicting gRNA efficiency, thereby optimizing engineered gRNAs and enhancing the application of CRISPR-Cas systems in genome editing. However, the black-box nature of these deep learning methods often lacks transparency, hindering our understanding of the factors that boost efficiency. Addressing this issue can significantly expand the use of CRISPR-Cas systems across various domains. We introduce CRISPR-VAE, a framework designed to interpret gRNA efficiency predictions, thereby elucidating the factors that enhance gRNA performance, specifically applied to CRISPR/Cas12a (formerly known as CRISPR/Cpf1). Our framework articulates these factors into position-specific k-mer rules. The methodology involves constructing an efficiency-aware gRNA sequence generator, trained on real-world data, to produce a large volume of synthetic sequences exhibiting desirable traits. These sequences form the basis for explaining gRNA predictions. Additionally, CRISPR-VAE functions as an independent sequence generator, providing users with fine-grained control over the sequences. This versatile framework integrates seamlessly with various CRISPR-Cas tools and datasets, demonstrating its efficacy. The complete code implementation of CRISPR-VAE can be found at github.com/AhmadObeid/CRISPR-VAE.