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Frontiers in Biology

ISSN 1674-7984

ISSN 1674-7992(Online)

CN 11-5892/Q

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Front. Biol.    2015, Vol. 10 Issue (4) : 289-296    https://doi.org/10.1007/s11515-015-1366-y
MINI-REVIEW
Overview of guide RNA design tools for CRISPR-Cas9 genome editing technology
Lihua Julie Zhu()
Department of Molecular, Cell and Cancer Biology, Program in Bioinformatics and Integrated Biology, Program in Molecular Medicine, University of Massachusetts Medical School,Worcester, MA 01605, USA
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Abstract

CRISPR-Cas (Clustered, Regularly Interspaced, Short Palindromic Repeats – CRISPR-associated (Cas)) RNA guided endonuclease has emerged as the most effective and widely used genome editing technology, which has become the most exciting and rapidly advancing research field. Efficient genome editing by the CRISPR-Cas9 system has been demonstrated in many species, and several laboratories have established CRISPR-Cas9 as a screening tool for systematic genetic analysis, similar to shRNA screening. At least three companies have been founded to leverage this technology for therapeutic uses. To facilitate the implementation of this technology, many software tools have been developed to identify guide RNAs that effectively target a desired genomic region. Here, I provide an overview of the technology, focusing on guide RNA design principles, available software tools and their strengths and weaknesses.
Keywords CRISPR-Cas9      genome editing      gRNA design      off-target analysis      gRNA efficacy     
Corresponding Author(s): Lihua Julie Zhu   
Just Accepted Date: 10 July 2015   Online First Date: 05 August 2015    Issue Date: 14 August 2015
 Cite this article:   
Lihua Julie Zhu. Overview of guide RNA design tools for CRISPR-Cas9 genome editing technology[J]. Front. Biol., 2015, 10(4): 289-296.
 URL:  
https://academic.hep.com.cn/fib/EN/10.1007/s11515-015-1366-y
https://academic.hep.com.cn/fib/EN/Y2015/V10/I4/289
Fig.1  Two components of the CRISPR-Cas9 system from bacteria S. pyogenes along with their recognition sites.
Functions/Limits sgRNA designer1 Zhang laboratory2 eCRISP3 Kong laboratory4 CRISPR MultiTargeter5 CRISPRseek6
Off-target analysis No Yes with PDMM penalty scoring Yes without scoring Yes without scoring Yes without scoring Yes with PDMM penalty scoring
gRNA efficacy prediction Yes No No No No Yes
Batch search No Yes Yes No No Yes
Paired configuration No Nickase No No No Nickase, dCas9-Fok1 and Cas9-DBD fusion (dev)
Cas9 type SpCas9 only SpCas9 only SpCas9 only SpCas9 only SpCas9 only Flexible
Alternative scoring matrix for efficacy and off-targets No No No No No Yes
Target species Human and mouse A specified set A specified set A specified set Any
Restriction enzyme sites No No No No No Yes
Compare set of sequences No No No No Yes Yes
Predict secondary structure No No No Yes No Yes
Tab.1  Overview of several representative gRNA design tools, that are available at 1http://www.bioconductor.org/packages/release/bioc/html/CRISPRseek.html, 2http://crispr.mit.edu, 3http://www.e-crisp.org/E-CRISP/, 4http://cas9.cbi.pku.edu.cn, 5http://www.multicrispr.net, and 6http://www.broadinstitute.org/rnai/public/analysis-tools/sgrna-design
Mismatch position Penalty weight
1 0
2 0
3 0.014
4 0
5 0
6 0.395
7 0.317
8 0
9 0.389
10 0.079
11 0.445
12 0.508
13 0.613
14 0.851
15 0.732
16 0.828
17 0.615
18 0.804
19 0.685
20 0.583
Tab.2  Penalty weights to capture the position-dependent mismatch effect of gRNA on target cleavage, where 0 means no mismatch effect and 1 indicates the biggest effect on cleavage, and position 1 is the most distal from PAM sequence (Hsu et al., 2013).
Fig.2  Identify INDELs by restriction enzyme digestion (Courtesy of Dr. Huang Yang at Dr. Michael Green’s laboratory University of Massachusetts)
Fig.3  The offTargetAnalysis workflow. Major steps in gRNA design are gRNA finding and filtering, on-target and off-target searching, scoring, filtering and annotation, flanking sequence fetching and report generation. The gRNA analysis is colored in blue and off-target analysis is colored in gray. Required inputs for gRNA and off-target analysis are colored in dark green, optional input is colored in dark salmon, and output is colored in mint green. Several report files are generated including gRNAs in different format, i.e., fasta format, GenBank format, bed format to be visualized in UCSC genome browser, a tab delimited file containing gRNAs overlap with restriction sites, a tab delimited file containing gRNAs in paired configuration, a tab delimited file containing detailed off-target analysis results, a tab delimited file containing a summary of the gRNAs.
Fig.4  An example of gRNA bed output, visualized in UCSC genome browser, one row per gRNA plotted in gray scale to reflect the gRNA efficacy. The darker the color, the higher efficacy the gRNA has. The thicker portion of the rectangle is the cas9 cleavage site with arrow indicating the gRNA orientation either in positive or negative strand.
Fig.5  The compare2Sequences workflow for design gRNAs that target one but not the other sequences, or that target all sequences equally well. The gRNA analysis is colored in blue and off-target analysis is colored in gray. Required inputs for gRNA and off-target analysis are colored in dark green and output is colored in mint green. Several report files are generated including gRNAs in different format for each input sequence, i.e., fasta format, GenBank format, bed format to be visualized in UCSC genome browser, a tab delimited file containing gRNAs overlap with restriction sites, a tab delimited file containing gRNAs in paired configuration. In addition, it outputs a tab-delimited file containing gRNAs with their predicted efficacy, cleavage score for both sequences and score difference. For applications to target both sequences equally well, you would need to select the gRNAs with minimum absolute score difference. For applications to target one of the sequences but not the other, you would want to select gRNAs with the largest score difference.
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