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CRISPR/Cas systems offer powerful tools for genome editing, but their therapeutic application is hampered by the risk of unintended off-target mutations. Many molecular methods have been established to detect off-target editing, however, their sensitivity depends on first identifying potential sites using in silico methods. However, these in silico prediction methods are challenged by a trade-off between speed and sensitivity, and can fail to comprehensively detect all edited off-target sites. Here, we demonstrate that ignoring bulges has led to missing editing at off-target sites in previous studies and that continuing this practice can lead to inflated claims of fidelity. As a solution, we introduce the concept of symbolic alignments to efficiently identify off-targets without sacrificing sensitivity. We further present specialized data structures that enable rapid, alignment-free probabilistic ranking of guide RNAs based on their predicted off-target burden. Implemented in the tool CHOPOFF, these innovations accommodate mismatches, bulges (insertions/deletions), and incorporate genomic sequence variants for personalized off-target assessment. Benchmarking demonstrates that CHOPOFF significantly outperforms state-of-the-art tools in both prediction accuracy and computational speed.

Availability

CHOPOFF command line available at https://github.com/JokingHero/CHOPOFF.jl

CHOPOFF web server available at https://crisprtools.org/chopoff