Accounting for small variations in the tracrRNA sequence improves sgRNA activity predictions for CRISPR screening

Peter C. DeWeirdt, Abby V. McGee, Fengyi Zheng, Ifunanya Nwolah, Mudra Hegde and John G. Doench ()
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Peter C. DeWeirdt: Broad Institute of MIT and Harvard
Abby V. McGee: Broad Institute of MIT and Harvard
Fengyi Zheng: Broad Institute of MIT and Harvard
Ifunanya Nwolah: Broad Institute of MIT and Harvard
Mudra Hegde: Broad Institute of MIT and Harvard
John G. Doench: Broad Institute of MIT and Harvard

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract CRISPR technology is a powerful tool for studying genome function. To aid in picking sgRNAs that have maximal efficacy against a target of interest from many possible options, several groups have developed models that predict sgRNA on-target activity. Although multiple tracrRNA variants are commonly used for screening, no existing models account for this feature when nominating sgRNAs. Here we develop an on-target model, Rule Set 3, that makes optimal predictions for multiple tracrRNA variants. We validate Rule Set 3 on a new dataset of sgRNAs tiling essential and non-essential genes, demonstrating substantial improvement over prior prediction models. By analyzing the differences in sgRNA activity between tracrRNA variants, we show that Pol III transcription termination is a strong determinant of sgRNA activity. We expect these results to improve the performance of CRISPR screening and inform future research on tracrRNA engineering and sgRNA modeling.

Date: 2022
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DOI: 10.1038/s41467-022-33024-2

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