Prediction of on-target and off-target activity of CRISPR-Cas13dguide RNAs using deep learning
Transcriptome engineering applications in living cells with RNA-targeting CRISPR effectors depend on accurate prediction of on-target activity and off-target avoidance. Here, we design and test ~200,000 RfxCas13d guide RNAs targeting essential genes in human cells with systematically-designed mismatches, insertions and deletions (indels). We find that mismatches and indels have a position- and context-dependent impact on Cas13d activity, and mismatches that result in G:U wobble pairings are better tolerated than other single-base mismatches. Using this large-scale dataset, we train a convolutional neural network that we term TIGER (Targeted Inhibition of Gene Expression via gRNA design) to predict efficacy from guide sequence and context. TIGER outperforms existing models at predicting on- and off-target activity on our dataset and published datasets. We show that TIGER scoring combined with specific mismatches yields the first general framework to modulate transcript expression, enabling use of RNA-targeting CRISPRs to precisely control gene dosage. Overall design: Evaluation of RfxCas13d guide RNAs for On-target and Off-target activity.