---
title: "Normalizing CUT&RUN Data Using E. coli Spike-in DNA"
slug: "normalizing-to-e-coli-spike-in-dna"
description: "Learn how to normalize CUT&RUN sequencing data using E. coli spike-in DNA for accurate sample comparison and quantitative analysis."
updated: 2025-12-17T15:00:35Z
published: 2025-12-17T15:00:35Z
canonical: "support.epicypher.com/normalizing-to-e-coli-spike-in-dna"
---

> ## 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.

# Normalizing to E. coli Spike-in DNA

Here we describe how to normalize CUT&RUN sequencing data using *E. coli* Spike-in DNA ([EpiCypher 18-1401](https://www.epicypher.com/product/cutana-e-coli-spike-in-dna/)):

1. Align sequencing reads to reference genome (e.g. human, mouse), and filter out muti-mapping reads, reads assigned to ENCODE DAC exclusion list regions, and duplicate reads (as desired) to determine the total number of uniquely aligned reads. Perform for each reaction.
2. In a separate alignment, align sequencing reads to the *E. coli* K12, MG1655 reference genome: [https://support.illumina.com/sequencing/sequencing_software/igenome.html](https://support.illumina.com/sequencing/sequencing_software/igenome.html). Filter out reads that do NOT align uniquely. Note that this alignment is separate from the reference genome in Step 1.
3. For pairwise comparisons, quantify *E. coli* Spike-in DNA reads for each CUT&RUN reaction and normalize to the total number of uniquely aligned reads.

Example: CUT&RUN was used to map H3K4me3 in treated and untreated cells.
  1. Treatment spike-in = 100,000 *E. coli* reads in 5,000,000 total reads = 2%
  2. Untreated spike-in = 30,000 *E. coli* reads in 3,000,000 total reads = 1%
4. Calculate normalization factor (see [1]) such that after normalization the *E. coli* spike-in signal is set to be equal across all reactions.

Example from above, comparing H3K4me3 in treated vs. untreated cells:
  1. Treatment normalization factor = 1 / 2% spike-in bandwidth = 0.5
  2. Untreated normalization factor = 1 / 1% spike-in bandwidth = 1.0
5. Use single scalar normalization ratio with the -- scaleFactor option enabled in deepTools bamCoverage tool to generate normalized bigwig files for visualization in IGV ([https://deeptools.readthedocs.io/en/develop/content/tools/bamCoverage.html](https://deeptools.readthedocs.io/en/develop/content/tools/bamCoverage.html)).

Continuing with the Example from above:
  1. Treatment sample --scaleFactor = 0.5
  2. Untreated sample --scaleFactor = 1.0

The effect of normalization on a dataset is inversely proportional to the *E. coli* Spike-in bandwidth. In other words, reactions with the highest bandwidth will receive the largest reduction in signal after normalization. For further information on sequencing normalization using exogenous spike-in controls, see [1,2].

  

**References**

1. Tay et al. [Hdac3 is an epigenetic inhibitor of the cytotoxicity program in CD8 T cells](https://pubmed.ncbi.nlm.nih.gov/32374402/). J Exp Med 217 (2020).
2. Orlando et al. [Quantitative ChIP-Seq normalization reveals global modulation of the epigenome.](https://pubmed.ncbi.nlm.nih.gov/25437568/) Cell Rep 9, 1163-1170 (2014).