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Benchmarking CUT&RUN analysis procedure using motif enrichment

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE337966 | GEO | 2026/07/18

REPOSITORIES: GEO

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Benchmarking Peak Calling Methods for CUT&RUN

Project description:Cleavage Under Targets and Release Using Nuclease (CUT&RUN) has rapidly gained prominence as an effective approach for mapping protein-DNA interactions, especially histone modifications, offering substantial improvements over conventional chromatin immunoprecipitation sequencing (ChIP-seq). However, the effectiveness of this technique is contingent upon accurate peak identification, necessitating the use of optimal peak calling methods tailored to the unique characteristics of CUT&RUN data. Here, we benchmark four prominent peak calling tools - MACS2, SEACR, GoPeaks, and LanceOtron - evaluating their performance in identifying peaks from CUT&RUN datasets. Our analysis utilizes in-house data of three histone marks (H3K4me3, H3K27ac, and H3K27me3) from mouse brain tissue, as well as samples from the 4DNucleome database. We systematically assess these tools based on parameters such as the number of peaks called, peak length distribution, signal enrichment, and reproducibility across biological replicates. Our findings reveal substantial variability in peak calling efficacy, with each method demonstrating distinct strengths in sensitivity, precision, and applicability depending on the histone mark in question. These insights provide a comprehensive evaluation that will assist in selecting the most suitable peak caller for high-confidence identification of regions of interest in CUT&RUN experiments, ultimately enhancing the study of chromatin dynamics and transcriptional regulation.

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