---
title: "Analyzing CUT&RUN and CUT&Tag sequencing data"
slug: "analyzing-cut-and-tag"
updated: 2024-09-04T20:14:28Z
published: 2024-09-04T20:14:28Z
canonical: "support.epicypher.com/analyzing-cut-and-tag"
---

> ## Documentation Index
> Fetch the complete documentation index at: https://support.epicypher.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Analyzing CUT&RUN and CUT&Tag sequencing data

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

- 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]).

  

**References**

1. Langmead & Salzberg. [Fast gapped-read alignment with Bowtie 2](https://pubmed.ncbi.nlm.nih.gov/22388286/). Nat Methods 9, 357-359 (2012).
2. Robinson et al. [Integrative Genomics Viewer](https://pubmed.ncbi.nlm.nih.gov/21221095/). Nat Biotechnol 29, 24–26 (2011).
3. Ramírez et al. [deepTools2: a next generation web server for deep-sequencing data analysis.](https://pubmed.ncbi.nlm.nih.gov/27079975/) 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](https://pubmed.ncbi.nlm.nih.gov/24743991/). 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](https://pubmed.ncbi.nlm.nih.gov/19505939/). Bioinformatics 25, 1952-1958 (2009).
6. Evans et al. [Ybx1 fine-tunes PRC2 activities to control embryonic brain development](https://pubmed.ncbi.nlm.nih.gov/32792512/). Nat Commun 11, 4060 (2020).
7. Laczik M et al. [Iterative Fragmentation Improves the Detection of ChIP-seq Peaks for Inactive Histone Marks](https://pubmed.ncbi.nlm.nih.gov/27812282/). Bioinform Biol Insights 10, 209-224 (2016).
8. Meers et al. [Peak calling by Sparse Enrichment Analysis for CUT&RUN chromatin profiling](https://pubmed.ncbi.nlm.nih.gov/31300027/). 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](https://pubmed.ncbi.nlm.nih.gov/34244724/). Bioinformatics 38, 252-254 (2021).
10. Schep AN et al. [chromVAR: inferring transcription-factor-associated accessibility from single-cell epigenomic data](https://pubmed.ncbi.nlm.nih.gov/28825706/). Nat Methods 14, 975-978 (2017).
11. Love MI et al. [Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2](https://pubmed.ncbi.nlm.nih.gov/25516281/). Genome Biol 15, 550 (2014).