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Benchmarking CUT&RUN analysis procedure using motif enrichment

GSE337966 Homo sapiens Genome binding/occupancy profiling by high throughput sequencing 5 samples Submitted 2026/07/18 Platform GPL18573
Summary
We designed a benchmarking method to evaluate peak calling procedures for CUT&RUN data and the effects of varying preprocessing approaches, such as fragment length filtering and spike-in calibration. To support this work, we generated new CUT&RUN datasets with distinct experimental characteristics alongside publicly available datasets. In particular, we produced libraries with an average read length of approximately 133 bp—longer than typical CUT&RUN read lengths—which provide higher sequencing coverage and improved read mappability. These datasets provide complementary data for evaluating how experimental characteristics influence preprocessing strategies and peak-calling performance in CUT&RUN.
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Also filed as BioProject PRJNA1492002 and SRA study SRP716291. Searching any of these in the dataset finder brings you back here.

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