Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and-Cas12a guides in Yarrowia lipolytica

The successful use of CRISPR-based mutagenesis in non-conventional microorganisms requires high activity sgRNAs. Here, the authors present DeepGuide, a neural network-based architecture, that learns from genome-wide CRISPR activity profiles to accurately design Cas9 and Cas12a sgRNAs with high activity in the oleaginous yeast Yarrowia lipolytica. Genome-wide functional genetic screens have been successful in discovering genotype-phenotype relationships and in engineering new phenotypes. While broadly applied in mammalian cell lines and in E. coli, use in non-conventional microorganisms has been limited, in part, due to the inability to accurately design high activity CRISPR guides in such species. Here, we develop an experimental-computational approach to sgRNA design that is specific to an organism of choice, in this case the oleaginous yeast Yarrowia lipolytica. A negative selection screen in the absence of non-homologous end-joining, the dominant DNA repair mechanism, was used to generate single guide RNA (sgRNA) activity profiles for both SpCas9 and LbCas12a. This genome-wide data served as input to a deep learning algorithm, DeepGuide, that is able to accurately predict guide activity. DeepGuide uses unsupervised learning to obtain a compressed representation of the genome, followed by supervised learning to map sgRNA sequence, genomic context, and epigenetic features with guide activity. Experimental validation, both genome-wide and with a subset of selected genes, confirms DeepGuide's ability to accurately predict high activity sgRNAs. DeepGuide provides an organism specific predictor of CRISPR guide activity that with retraining could be applied to other fungal species, prokaryotes, and other non-conventional organisms.

Automated summary

In this paper, the authors propose a neural network-based architecture called DeepGuide, which learns from genome-wide CRISPR activity profiles to accurately design high activity sgRNAs. The experimental validation confirms the effectiveness of DeepGuide in predicting high activity sgRNAs in the oleaginous yeast Yarrowia lipolytica.