{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE337nnn/GSE337966/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Genomics"],"species":["Homo sapiens"],"gds_type":["Genome binding/occupancy profiling by high throughput sequencing"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE337966"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"Benchmarking CUT&RUN analysis procedure using motif enrichment","description":"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.","dates":{"publication":"2026/07/18"},"accession":"GSE337966","cross_references":{"GSM":["GSM9863665","GSM9863666","GSM9863667","GSM9863663","GSM9863664"],"GPL":["18573"],"GSE":["337966"],"taxon":["Homo sapiens"]}}