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Inference of CRISPR Edits from Sanger Trace Data

Tim Hsiau, Travis Maures, Kelsey Waite, Joyce Yang, Reed Kelso, Kevin Holden, Rich Stoner
doi: https://doi.org/10.1101/251082
Tim Hsiau
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  • For correspondence: tim.hsiau@synthego.com
Travis Maures
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Kelsey Waite
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Joyce Yang
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Reed Kelso
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Kevin Holden
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Rich Stoner
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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 batch analysis of CRISPR edits using Sanger data. ICE proposes potential editing outcomes for single guide, multiplex editing, base editing, and homology-directed repair experiments 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 NGS analysis (Amp-Seq). ICE is an improvement over current analysis tools in that it provides batch analysis, is free to use, and can detect a wider variety of edits. It provides investigators with a reliable editing tool that can significantly expedite CRISPR editing workflows. Our ICE tool is available online at ice.synthego.com and the source code is at github.com/synthego-open/ice

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted January 20, 2018.
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Inference of CRISPR Edits from Sanger Trace Data
Tim Hsiau, Travis Maures, Kelsey Waite, Joyce Yang, Reed Kelso, Kevin Holden, Rich Stoner
bioRxiv 251082; doi: https://doi.org/10.1101/251082
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Inference of CRISPR Edits from Sanger Trace Data
Tim Hsiau, Travis Maures, Kelsey Waite, Joyce Yang, Reed Kelso, Kevin Holden, Rich Stoner
bioRxiv 251082; doi: https://doi.org/10.1101/251082

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