Analyzing CUT&RUN data

CUT&RUN analysis methods are similar to those used for ChIP-seq datasets, with a few key differences. Briefly:

  • Align raw reads to a reference genome using Bowtie2 [1]. The Integrative Genomics Viewer (IGV) [2] and/or deepTools2 [3] can be used to visualize enrichment (e.g. bigWig files graphed over a genome browser).
  • For peak calling, EpiCypher frequently uses MACS2 [4] and SICER [5], programs for ChIP-seq that work well for CUT&RUN [6]. SICER can be adjusted for analysis of sharp enrichment peaks (e.g. H3K4me3) vs. broad areas of enrichment (e.g. H3K27me3) [7]. Other options include SEACR [8], a peak caller designed for CUT&RUN data, and the CUT&RUNTools 2.0 pipeline, which is designed for CUT&RUN and CUT&Tag data, including analysis of single cells [9]. It is recommended to test several programs and select the one that faithfully represents the target of interest.
  • To determine signal over background, EpiCypher uses bedTools to calculate fractions of reads in peaks (FRiP) and compare FRiP scores from experimental samples vs. controls [10]. Other tools can be applied for differential analysis and heatmap generation (e.g. DESeq2 [11], deepTools2 [3]).


  1. Langmead & Salzberg. Fast gapped-read alignment with Bowtie 2. Nat Methods 9, 357-359 (2012).
  2. Robinson et al. Integrative Genomics Viewer. Nat Biotechnol 29, 24–26 (2011).
  3. Ramírez et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 8, 44 (2016).
  4. Liu T. Use model-based Analysis of ChIP-Seq (MACS) to analyze short reads generated by sequencing protein-DNA interactions in embryonic stem cells. Methods Mol Biol 1150, 81-95 (2014).
  5. Zang C et al. A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25, 1952-1958 (2009).
  6. Evans et al. Ybx1 fine-tunes PRC2 activities to control embryonic brain development. Nat Commun 11, 4060 (2020).
  7. Laczik M et al. Iterative Fragmentation Improves the Detection of ChIP-seq Peaks for Inactive Histone Marks. Bioinform Biol Insights 10, 209-224 (2016).
  8. Meers et al. Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling. Epigenetics Chromatin 12, 42 (2019).
  9. Yu F et al. CUT&RUNTools 2.0: A pipeline for single-cell and bulk-level CUT&RUN and CUT&Tag data analysis. Bioinformatics 38, 252-254 (2021).
  10. Schep AN et al. chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data. Nat Methods 14, 975-978 (2017).
  11. Love MI et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15, 550 (2014).

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