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Abstract CRISPR interference (CRISPRi) is the leading technique to silence gene expression in bacteria; however, design rules remain poorly defined. We develop a best-in-class prediction algorithm for guide silencing efficiency by systematically investigating factors influencing guide depletion in genome-wide essentiality screens, with the surprising discovery that gene-specific features substantially impact prediction. We develop a mixed-effect random forest regression model that provides better estimates of guide efficiency. We further apply methods from explainable AI to extract interpretable design rules from the model. This study provides a blueprint for predictive models for CRISPR technologies where only indirect measurements of guide activity are available.

CRISPR technologies comprising a Cas nuclease and a guide RNA (gRNA) can utilize multiple gRNAs to enact multi-site editing or regulation in the same cell. Nature devised a highly compact means of encoding gRNAs in the form of CRISPR arrays composed of conserved repeats separated by targeting spacers. However, the capacity to acquire new spacers keeps the arrays longer than necessary for CRISPR technologies. Here, we show that CRISPR arrays utilized by the Cas9 nuclease can be shortened without compromising and sometimes even enhancing targeting activity. Using multiplexed gene repression in E. coli, we found that each region could be systematically shortened to varying degrees before severely compromising targeting activity. Surprisingly, shortening some spacers yielded enhanced targeting activity, which was linked to folding of the transcribed array prior to processing. Overall, shortened CRISPR-Cas9 arrays can facilitate multiplexed editing and gene regulation from a smaller DNA footprint across many bacterial applications of CRISPR technologies.