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Genomics and Rare Diseases / CRISPR and Genetic Engineering · Journal article
Human Gene Therapy · July 26, 2026
Early or partial results. Treat as a signal, not a conclusion.
This systematic benchmarking of 14 CRISPR-Cas9 off-target prediction tools against 3,827 experimentally validated genomic sites demonstrates that all tools assign higher scores to true off-target sites but exhibit weak-to-moderate correlation with actual editing magnitude and moderate precision–recall trade-offs. The findings indicate that current in silico tools can prioritize off-target candidates but fail to comprehensively identify or quantitatively predict off-target activity, supporting the need for combined computational and experimental approaches in preclinical assessment.
Systematic benchmarking study using experimental validation dataset. 3,827 genomic sites from human cells tested with 26 distinct guide RNA/Cas9 combinations; curated dataset derived from CRISPRoffT database.. Intervention: In silico prediction tools (14 tools: standard approaches and machine learning-based models). Compared with: Experimental validation by deep sequencing; indel frequency measurement in human cells. n = 3,827.
All tools assigned higher scores to true off-target sites (≥0.1% indel frequency) compared with nontarget sites, although substantial overlap between classes was observed. Correlation between prediction scores and indel frequencies was weak to moderate, indicating limited ability to predict editing magnitude. Precision–recall performance was moderate across all tools, reflecting inherent trade-offs between sensitivity and specificity.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Preclinical researchers using CRISPR-Cas9 should recognize that current in silico off-target prediction tools, while useful for candidate prioritization, cannot alone comprehensively identify or quantify off-target activity; experimental validation remains essential for safety assessment in gene-editing workflows prior to clinical translation.
A systematic benchmarking study of computational tools against experimental data, showing moderate performance with substantial limitations; informs preclinical practice but does not establish a new clinical standard or definitive tool ranking.
As stated by the source record.
Quoted from the source exactly as published.
Preclinical researchers using CRISPR-Cas9 should recognize that current in silico off-target prediction tools, while useful for candidate prioritization, cannot alone comprehensively identify or quantify off-target activity; experimental validation remains essential for safety assessment in gene-editing workflows prior to clinical translation.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Accurate identification of CRISPR-Cas9 off-target sites is essential for the safety assessment of genome-editing-based therapies. While numerous in silico prediction tools have been developed, their comparative performance and practical utility in preclinical workflows remain incompletely defined. We performed a systematic benchmarking of 14 in silico CRISPR-Cas9 off-target prediction tools, including both standard approaches and machine learning-based models. The analysis was based on a curated dataset derived from the CRISPRoffT database, comprising 3,827 deep-sequenced genomic sites across 26 guide RNA/Cas9 combinations in human cells. Sites with indel frequencies ≥0.1% were operationally defined as true off-targets. We evaluated tool performance using score distributions, correlation with indel frequencies, precision–recall characteristics, recall among top-ranked candidate sites, and the effect of combining tools. All tools assigned higher scores to true off-target sites compared with nontarget sites, although substantial overlap between classes was observed. Correlation between prediction scores and indel frequencies was weak to moderate, indicating limited ability to predict editing magnitude. Precision–recall performance was moderate across all tools, reflecting inherent trade-offs between sensitivity and specificity. Recall increased with the number of predicted sites considered, reaching approximately 77% among the top 500 and up to 83% among the top 1,250 sites, but leaving a substantial fraction of true off-targets undetected. Combining tools yielded only modest improvements. Current in silico tools enable prioritization of CRISPR-Cas9 off-target candidates but remain limited in their ability to comprehensively identify and quantitatively predict off-target activity. Our findings highlight the importance of considering both ranking performance and candidate site coverage and support the use of combined computational and experimental strategies for robust off-target assessment in preclinical gene editing workflows.
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