Inference of CRISPR Edits from Sanger Trace Data.

Area of Science:

• Molecular Biology
• Genetics
• Bioinformatics

Background:

• Efficient genome editing necessitates rapid, quantitative, and cost-effective methods for genotype assessment post-editing.
• Current analytical tools for CRISPR-edited samples often lack speed, scalability, or comprehensive analysis capabilities.

Purpose of the Study:

• To introduce ICE (Inference of CRISPR Edits), a novel algorithm for analyzing CRISPR genome editing outcomes.
• To provide a robust, quantitative, and accessible tool for assessing CRISPR editing efficiency and precision using Sanger sequencing data.

Main Methods:

• Development of the ICE algorithm, which predicts potential editing outcomes based on guide RNA sequences.
• Utilizes regression analysis to determine which predicted outcomes are supported by Sanger sequencing data.
• Implementation of batch analysis and support for diverse editing conditions.

Main Results:

• ICE demonstrates robust and reproducible analysis of CRISPR edits from Sanger sequencing data.
• The tool provides accurate estimates of editing outcomes across various benchmarks and in comparison to existing Sanger analysis tools.
• ICE enables rapid analysis of CRISPR experiments, often within days of transfection.

Conclusions:

• ICE offers significant improvements over current Sanger analysis tools for CRISPR editing.
• The free and open-source nature of ICE, along with its user-friendly online platform and batch processing capabilities, enhances accessibility for researchers.
• ICE facilitates faster and more precise evaluation of genome editing experiments, accelerating research in the field.