Step-by-Step Guide for Analyzing CRISPR Editing Results with ICE

CRISPR-based genome engineering has revolutionized gene editing by making experimental workflows significantly easier, faster, and more efficient. Analyzing the data is a critical step in the CRISPR workflow, but until recently, researchers had limited options for accessible, reliable analysis tools.

EditCo’s ICE (Inference of CRISPR Edits) tool simplifies the analysis of CRISPR knockouts and knock-ins like never before. Originally developed to meet EditCo’s internal CRISPR analysis needs, ICE was built to fill the gap in available software. We validated its performance across thousands of CRISPR edits, demonstrating superior accuracy, speed, and robustness compared to other tools.

Advantages of Analyzing CRISPR Edits with ICE

Determine Editing Efficiency and Types of Edits with Low-Cost Sanger Sequencing

ICE uses Sanger sequencing data to produce quantitative, NGS-quality analysis of CRISPR edits—offering up to a ~100-fold cost reduction compared to NGS-based amplicon sequencing.

To use the tool, simply upload your Sanger sequencing files (individually or in bulk), enter your guide RNA (gRNA) sequence, and select the nuclease used in your CRISPR experiment. ICE will calculate overall editing efficiency and identify the profiles and relative abundances of all edit types present in the sample.

Analyze Complex CRISPR Edits

Traditional Sanger-based analysis tools often fall short when it comes to complex CRISPR edits—such as those involving multiple gRNAs or non-SpCas9 nucleases. EditCo’s ICE addresses this limitation by supporting multi-gRNA analysis and a curated list of nucleases, including SpCas9, hfCas12Max, Cas12a, and MAD7. The tool also generates clear visualizations of the edit types detected.

How to Analyze CRISPR Results with ICE

Once CRISPR components are delivered into cells, verifying successful genome editing is crucial. Many researchers turn to cost-effective Sanger sequencing to do so. Preparing DNA samples includes primer design, genomic DNA extraction, and PCR amplification. For detailed guidance, refer to our Genotyping Protocol for sample prep and Sanger sequencing tips.

After sequencing, simply upload your files to ICE along with the gRNA target sequence and selected nuclease. ICE will analyze the data, determine what percentage of the target sequence contains insertions or deletions (indels), and characterize the sequence and abundance of each indel.

Overview of ICE Analysis

Once the analysis is complete, a new screen will display visualizations of the results and a list of analyzed samples. If analysis runs smoothly, you’ll see a green checkmark next to the sample name. A yellow checkmark indicates a minor issue, while a red exclamation mark signals a failed or incomplete analysis. Hover over the yellow or red icons to view specific error messages. For help resolving issues, refer to EditCo’s ICE Knockout and Knock-In Analysis Protocols and their respective troubleshooting sections.

Successfully analyzed samples will include the following data points:

• Sample – The unique name or label you assigned to the sample.

• Guide Target – The user-defined nucleotide sequence of the gRNA’s targeting region, excluding the PAM.

• PAM Sequence – The protospacer adjacent motif used by the selected nuclease. ICE currently supports SpCas9, hfCas12Max, Cas12a, and MAD7.

• Indel Percentage – The proportion of sequences containing insertions or deletions, calculated by comparing edited vs. control traces.

• Model Fit (R²) Score – A Pearson correlation coefficient indicating how well the ICE model fits your sequencing data. Higher scores suggest greater confidence in the results.

• Knockout Score – The percentage of sequences predicted to result in a functional gene knockout (frameshift or indel ≥21 bp).

• Knock-in Score – The percentage of sequences containing the precise knock-in edit.

You can sort the summary table by any parameter or use “Control+F” (Windows) or “Command+F” (Mac) to search specific entries. For deeper insights, click on a sample name or its corresponding graph bar to open a detailed view. This view includes four tabs—Traces, Discord & Indel, Contributions, and Alignment—each offering a unique look at the sample’s editing profile.

To save your data, click the Download Analysis Data button in the lower right corner to export a ZIP file with your complete analysis.

Using ICE for Analysis of CRISPR Knockouts

Analyzing knockout data with EditCo’s ICE tool is fast and user-friendly. Upload your Sanger sequencing files (edited and control), input your gRNA sequence, choose the nuclease used, and ICE takes care of the rest—no parameter tuning or advanced setup required.

ICE can analyze edits created by one or multiple gRNAs and supports several nucleases, including SpCas9, hfCas12Max, Cas12a, and MAD7. For flexibility, ICE offers two modes: “sample-by-sample” for smaller sets (up to five experiments), and “batch” mode for analyzing hundreds of samples in parallel.

ICE provides several useful metrics, including:

• Knockout Score – Estimates the proportion of cells likely to have a functional gene knockout.

• Model Fit (R²) – Reflects how well the sequence data fits the predicted model for indel distribution.

Detailed views help visualize sequencing quality, edit contributions, and trace alignments. While ICE offers a strong prediction of editing outcomes, we recommend follow-up validation—such as western blotting or flow cytometry—to confirm knockout at the protein level.

Using the ICE Tool for CRISPR Knock-in Analysis

The process for analyzing knock-in data with the ICE tool is similar to the process for knockout analysis. Upload your Sanger sequencing files for edited and control samples, gRNA sequence, and donor sequence (up to 300 bp), select your nuclease from the dropdown menu, and complete your ICE analysis.

The Knock-in Score (KI Score), a measure of the proportion of sequences with the desired knock-in edit, is the key indicator of knock-in edit success. Similar to knockout analysis, the results are displayed across multiple tabs that allow visualization of contributions, indels, and traces. After completing your KI ICE analysis assessment, it is best practice to perform functional assays specific to your KI edit to validate that the insertion was successful.

CRISPR Analysis Made Easy

ICE generates NGS-quality CRISPR editing analysis from Sanger sequencing data. Furthermore, ICE can analyze more types of editing experiments than other Sanger sequencing-based software tools and is also faster and easier to use. 

Read more details about the ICE tool in this article published in The CRISPR Journal.

We invite you to try ICE today and let us know how it works for you!

Have more questions about ICE or your CRISPR experiment?