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Genomics Evidence

Retrieve specific, versioned evidence records on gene functions, mutations, and sequencing data.Know what the science actually supports before you trust the answer.

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     Quick Analysis Plan



    GUIDE-seq remains the most validated cell-based gold standard for CRISPR off-target discovery, but its sensitivity is bounded: it requires DSB formation and dsODN integration, depends on cell-line chromatin context, and newer in vitro methods (CIRCLE-seq) and nickase-based edits substantially reduce or escape its detection window (). Battle-testing shows concordance for known sites but systematic blind spots for single-strand nicking and low-frequency events.


     Long Analysis Plan



    What GUIDE-seq Directly Detects — and What It Structurally Cannot

    GUIDE-seq tags double-strand breaks via dsODN integration, so its sensitivity is bounded by DSB formation, dsODN delivery efficiency, and sequencing depth; the original authors themselves flagged shallow sequencing and two-cell-line (U2OS, HEK293) reliance as sensitivity constraints (). A decisive stress test comes from spacer-nick Cas9n editing in HSPCs: off-target dsODN integrations were not detected at all in Cas9n-treated cells, and on-target GUIDE-seq reads dropped ~260-fold versus nuclease Cas9 (). This is not proof of zero off-targets — single-strand nicks do not integrate dsODN — so GUIDE-seq under-reports nickase damage by design.

    When GUIDE-seq-identified sites are then quantified by amplicon sequencing in edited cells, reported frequencies can be dramatic: in β-thalassemia base editing, the IVS2-1 guide produced off-target edits at S-OT2 (90.7% ± 2.3) and three E-OT sites above 50%, while the CD39 guide stayed mostly below 10% at its detected sites (). This spread shows why GUIDE-seq discovery must be paired with orthogonal quantitative validation — discovery sensitivity does not equal measured edit frequency.

    Comparative Sensitivity: In Vitro Beats In Cellulo

    CIRCLE-seq, a purely in vitro circularized-DNA assay, found 156 off-target sites missed by Digenome-seq with ~180,000-fold enrichment over background, illustrating that cell-based GUIDE-seq systematically misses chromatin-inaccessible or low-activity sites (). The trade-off: CIRCLE-seq false positives require cell-based confirmation — GUIDE-seq and CIRCLE-seq are complementary, not interchangeable.

    Battle-Testing Verdict and Blind Spots

    • Validated strength: GUIDE-seq correctly recovers known off-targets across nuclease, base-editor, and prime-editor systems (EMX1: 10 sites; FANCF: 3 sites in the SunTag-PE evaluation) ().
    • Structural blind spot 1: nicks and SSBs are invisible by design (Cas9n data above).
    • Structural blind spot 2: chromatin and cell-type dependence — K562 GUIDE-seq results may not transfer to HSPCs; the thalassemia study partially mitigates this with WES/RNA-seq orthogonal checks ().
    • Analysis-chain bias: off-target calling depends on pipeline assumptions (bulge handling, multi-mapper assignment); newer Snakemake pipelines address these but rely partly on in-silico validation ().

    What would change this conclusion: a head-to-head study showing GUIDE-seq recovering nick-induced or low-frequency off-targets that CIRCLE-seq/amplicon validation miss would overturn the DSB-dependence blind spot; none exists in the supplied evidence. Confidence: high on reported values (all from supplied records), moderate on the generalization that GUIDE-seq under-reports genome-wide absolute off-target burden.



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    Updated: November 01, 2026



     Top Data Sources ExportMCP



     Analysis Wizard



    Computing a sensitivity matrix comparing GUIDE-seq-detected off-target frequencies across the reported CD39, IVS2-1, spacer-nick, and SunTag-PE datasets to quantify discovery-versus-quantification gaps.



     Hypothesis Graveyard



    GUIDE-seq detects essentially all off-targets because it is called a gold standard: falsified by the spacer-nick study where zero off-target dsODN integrations were detected despite known nickase activity elsewhere, and by CIRCLE-seq's 156 previously missed sites.


    Off-target frequency measured by GUIDE-seq read counts equals editing frequency: falsified by the β-thalassemia data where GUIDE-seq-discovered sites spanned 2.8%–90.7% measured edit frequency, requiring orthogonal amplicon quantification.

     Science Art


    Best Evidence: GUIDE-seq validation sensitivity known off-targets Science Art

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