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Computational Tools and Resources Supporting CRISPR-Cas Experiments

Department of Genome Engineering, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
Author to whom correspondence should be addressed.
Cells 2020, 9(5), 1288;
Received: 6 May 2020 / Revised: 18 May 2020 / Accepted: 20 May 2020 / Published: 22 May 2020
(This article belongs to the Special Issue CRISPR Genome Editing)
The CRISPR-Cas system has become a cutting-edge technology that revolutionized genome engineering. The use of Cas9 nuclease is currently the method of choice in most tasks requiring a specific DNA modification. The rapid development in the field of CRISPR-Cas is reflected by the constantly expanding ecosystem of computational tools aimed at facilitating experimental design and result analysis. The first group of CRISPR-Cas-related tools that we review is dedicated to aid in guide RNA design by prediction of their efficiency and specificity. The second, relatively new group of tools exploits the observed biases in repair outcomes to predict the results of CRISPR-Cas edits. The third class of tools is developed to assist in the evaluation of the editing outcomes by analysis of the sequencing data. These utilities are accompanied by relevant repositories and databases. Here we present a comprehensive and updated overview of the currently available CRISPR-Cas-related tools, from the perspective of a user who needs a convenient and reliable means to facilitate genome editing experiments at every step, from the guide RNA design to analysis of editing outcomes. Moreover, we discuss the current limitations and challenges that the field must overcome for further improvement in the CRISPR-Cas endeavor. View Full-Text
Keywords: genome editing; CRISPR; database; off-target; sgRNA; microhomology; NHEJ; HDR; predictive algorithms; sgRNA design; NGS analysis genome editing; CRISPR; database; off-target; sgRNA; microhomology; NHEJ; HDR; predictive algorithms; sgRNA design; NGS analysis
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MDPI and ACS Style

Sledzinski, P.; Nowaczyk, M.; Olejniczak, M. Computational Tools and Resources Supporting CRISPR-Cas Experiments. Cells 2020, 9, 1288.

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