Abstract
Efficient precision genome editing requires a quick, quantitative, and inexpensive assay of editing outcomes. Here we present ICE (Inference of CRISPR Edits), which enables robust analysis of CRISPR edits using Sanger data. ICE proposes potential outcomes for editing with guide RNAs (gRNAs) and then determines which are supported by the data via regression. Additionally, we develop a score called ICE-D (Discordance) that can provide information on large or unexpected edits. We empirically confirm through over 1,800 edits that the ICE algorithm is robust, reproducible, and can analyze CRISPR experiments within days after transfection. We also confirm that ICE strongly correlates with next-generation sequencing of amplicons (Amp-Seq). The ICE tool is free to use and offers several improvements over current analysis tools. For instance, ICE can analyze individual experiments as well as multiple experiments simultaneously (batch analysis). ICE can also detect a wider variety of outcomes, including multi-guide edits (multiple gRNAs per target) and edits resulting from homology-directed repair (HDR), such as knock-ins and base edits. ICE is a reliable analysis tool that can significantly expedite CRISPR editing workflows. It is available online at ice.synthego.com, and the source code is at github.com/synthego-open/ice
Footnotes
This version of the "Inference of CRISPR Edits from Sanger Trace Data" manuscript has been updated with data demonstrating that lasso regression with an L1 parameter of 0.8 decreases the number of false-positive indels detected by ICE. New data illustrating a strong correlation between Amp-seq and ICE results using a lasso regression are presented in Figure 6. Some terminology was changed, two authors were added, and the corresponding author was changed.
