Switching from ChIP-seq to CUT&Tag feels like an obvious win: you drop sample requirements from 10 million cells to 1,000 and get clean browser tracks with minimal background reads.
The risk emerges when you process those tracks with standard ChIP-seq peak callers like MACS2.
In classical ChIP-seq, physical sonication shears chromatin across the entire genome, generating a matched Input control. The peak caller calculates enrichment against this local baseline, controlling for copy number and sequenceability.
CUT&Tag works differently. Protein A-Tn5 cleaves directly at antibody-bound epitopes in situ (Kaya-Okur et al., 2019). Because off-target chromatin remains uncut, background read counts stay near zero.
When algorithms rely on global Poisson models without a local baseline, any small cluster of reads looks statistically significant. Hyper-accessible chromatin regions—where Tn5 integrates readily even in non-specific IgG controls—get scored as high-confidence binding events.
If you use those false peaks to nominate upstream regulators in rare transitional cell states, validation screens end up targeting transposase accessibility artifacts instead of true fate-driving enhancers.
Tools built for sparse tagmentation, like SEACR paired with matched IgG controls, evaluate enrichment against empirical non-specific cutting thresholds.
How does your pipeline control for Tn5 accessibility bias when calling peaks in low-input CUT&Tag datasets?
Because CUT&Tag lacks a non-specific sonication background, standard Poisson peak callers misidentify hyper-accessible Tn5 integration hotspots as false-positive transcription factor binding events. Low-input tagmentation requires specialized algorithms like SEACR with matched IgG baselines.