---
title: "Analysis and expected results for SNAP-CUTANA™ Spike-ins"
slug: "analyzing-snap-cutana-spike-in-controls-and-results-cut-and-tag"
updated: 2024-10-01T17:13:22Z
published: 2024-10-01T17:13:22Z
canonical: "support.epicypher.com/analyzing-snap-cutana-spike-in-controls-and-results-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.

# Analysis and expected results for SNAP-CUTANA™ Spike-ins

## **SNAP-CUTANA™ data analysis protocol**

This protocol can also be used to analyze SNAP-CUTANA Spike-in data from CUT&RUN and CUT&Tag reactions. CUT&RUN data are shown as an example.

1. Download R1 & R2 paired-end sequencing files (fastq.gz) for control reactions. Double-click the fastq.gz files to create **fastq files** and save in a **new folder**.
2. On the SNAP-CUTANA Spike-in product page (e.g. [K-MetStat Panel](https://www.epicypher.com/products/nucleosomes/snap-cutana-k-metstat-panel)), under Documents and Resources, download the Shell Script (**.sh**) and K-MetStat Panel Analysis (**.xlsx**) files. Save to the **folder** from Step 1.
3. Open the .sh file in TextEdit or any **text editing** program. Do **NOT** open in Word or a PDF program. Scroll past the barcode sequences to find the **analysis script**.
4. The script is a **loop** that **counts the number of reads** aligned to each **PTM-specific DNA barcode** in a reaction. Each PTM in the SNAP-CUTANA Panel is represented by two unique barcodes, A & B. For the script, you need to create **one loop per control reaction**. To customize:
  1. Copy lines **between** # template loop begin ## and # template loop end ##.
  2. Paste the loop under the last **done**. Paste one copy per control reaction.
  3. In the first loop replace sample1_R1.fastq and sample1_R2.fastq with R1 & R2 fastq file names for **one** control reaction. Repeat for each loop. Press save.
5. In **Terminal**, set the directory to your **folder**: Type **cd** and press space. Drag the folder from your files into Terminal to copy the location. Press return.
6. **Run your script in Terminal**: Type **sh** and press space. Drag your .sh file from your files into Terminal to copy the file location. Press return. Terminal generates barcode read counts from R1 & R2 reads, one loop/reaction at a time.
7. Open the **Panel Analysis Excel .xlsx file**. Fill in reaction names and set the **on-target PTM in Column B**. The first reaction is set to IgG (negative control); for other reactions, select a target (i.e. H3K4me3) from the drop-down menu.
8. Copy R1 barcode read counts from the first loop in Terminal. In Excel, paste into the yellow cells for that reaction in **Column C**. Copy & paste the R2 read counts from the same loop to yellow cells in **Column D**. Repeat for each loop/reaction.
9. The Excel file automatically analyzes spike-in data for **each reaction** by:
  1. Calculating total read counts for each DNA barcode (R1 + R2) in **Column E**.
  2. Calculating total barcode read counts for each PTM (A + B) in **Column F**.
  3. Expressing total read counts for each PTM as a percentage of on-target PTM read counts (**Columns G & J**), providing a readout of on- vs. off-target PTM recovery and antibody specificity.
10. **Column J** auto-populates the **Output Table** (**Figure 2**). Reactions are separated by row and PTM data are sorted into **columns**. A color gradient is used to visualize the recovery of each PTM normalized to on-target PTM, from blue (100%) to orange (less than 20%).
11. For each reaction, calculate the percent of unique sequencing reads that have been assigned to spike-ins. In Excel, type the t**otal number of unique reads** in the yellow cell **Uniq align reads** (in Column B). The **% total barcode reads** is calculated in the cell immediately below and is added to the Output Table.

![2024.09.05 SNAP K-MetHeatmap pass_fail@2x](https://cdn.document360.io/1fc69f91-cf1e-45ab-bc51-4063eb80e37c/Images/Documentation/2024.09.05%20SNAP%20K-MetHeatmap%20pass_fail@2x.png)

**Figure 1.** K-MetStat Spike-ins validate workflows and flag poor samples in CUTANA experiments. Spike-in data for H3K4me3 and H3K27me3 positive control reactions is shown for three independently prepared mouse B cell samples (10,000 cells each) in CUT&RUN. Samples 1 & 2 show expected results, while Sample 3 was flagged for recovery of off-target PTMs and low signal-to-noise. Representative data from one IgG reaction is shown as a negative control.

  

## **Expected results from SNAP-CUTANA Spike-in control reactions**

- The IgG negative control shows low background and no preference among PTMs (**Figure 1**, Top row).
- Positive controls (e.g., H3K4me3, H3K27me3, or H3K36me3) has strong enrichment for its target nucleosome spike-ins, less than 20% off-target PTM recovery, and high signal-to-noise (**Figure 1**, Samples 1 and 2).
- Spike-in barcode reads comprise ~1% (0.5-5%) of total sequencing reads. This may vary based on target abundance and sequencing depth. The main goal is to have thousands of reads, which will allow adequate sampling of the K-MetStat Panel for reliable data analysis.
- If control reactions generate expected spike-in data (**Figure 1**, Samples 1 and 2), you can be confident in the technical aspects of your workflow.
- More than 20% off-target PTM recovery in the positive control and/or high background in IgG control indicate experimental problems (**Figure 1**, Sample 3). See [this article](/v1/docs/using-snap-cutana-spike-ins-to-troubleshoot-workflows) for guidance for using SNAP-CUTANA Spike-in controls to troubleshoot workflows.