CRISPR-VAE: A Method for Explaining CRISPR/Cas12a Predictions, and an Efficiency-aware gRNA Sequence Generator

Ahmad Obeid, Hasan AlMarzouqi

Abstract: h3>Abstract</h3> <p>Deep learning has shown great promise in the prediction of the gRNA efficiency, which helps optimize the engineered gRNAs, and thus has greatly improved the usage of CRISPR-Cas systems in genome editing. However, the black box prediction of deep learning methods does not provide adequate explanation to the factors that increase efficiency; rectifying this issue promotes the usage of CRISPR-Cas systems in numerous domains. We put forward a framework for interpreting gRNA efficiency prediction, dubbed CRISPR-VAE, that improves understanding the factors that increase gRNA efficiency, and apply it to CRISPR/Cas12a (formally known as CRISPR/Cpf1). We further lay out a semantic articulation of such factors into position-wise k-mer rules. The paradigm consists of building an efficiency-aware gRNA sequence generator trained on available real data, and using it to generate a large amount of synthetic sequences with favorable traits, upon which the explanation of the gRNA prediction is based. CRISPR-VAE can further be used as a standalone sequence generator, where the user has low-level control ability. The framework can be readily integrated with different CRISPR-Cas tools and datasets, and its efficacy is confirmed. The complete implementation of the methods can be found at github.com/AhmadObeid/CRISPR-VAE.</p>

External IDs:doi:10.1101/2021.07.05.451176