计算机科学
清脆的
背景(考古学)
推论
基因组编辑
源代码
多样性(控制论)
桑格测序
跟踪(心理语言学)
开源
计算生物学
生物
人工智能
程序设计语言
DNA测序
软件
基因
DNA
古生物学
哲学
生物化学
遗传学
语言学
作者
David S. Conant,Tim Hsiau,Nicholas A. Rossi,Jennifer Oki,Travis J. Maures,Kelsey Waite,Joyce Yang,Sahil Joshi,Reed Kelso,Kevin Holden,Brittany L. Enzmann,Rich Stoner
出处
期刊:The CRISPR journal
[Mary Ann Liebert, Inc.]
日期:2022-02-01
卷期号:5 (1): 123-130
被引量:830
标识
DOI:10.1089/crispr.2021.0113
摘要
Efficient and precise genome editing requires a fast, quantitative, and inexpensive assay to assess genotype following editing. 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, and then determines which are supported by the data via regression. The ICE algorithm is robust and reproducible, and it can be used to analyze CRISPR experiments within days after transfection. We also confirm that ICE produces accurate estimates of editing outcomes across a variety of benchmarks, and within the context of other existing Sanger analysis tools. The ICE tool is free to use and open source, and offers several improvements over current analysis tools, such as batch analysis and support for a variety of editing conditions. It is available online at ice.synthego.com, and the source code is available at github.com/synthego-open/ice.
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