Review of CRISPR/Cas9 sgRNA Design Tools

Abstract

The adaptive immunity system in bacteria and archaea, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-associate (CRISPR/Cas), has been adapted as a powerful gene editing tool and got a broad application in genome research field due to its ease of use and cost-effectiveness. The performance of CRISPR/Cas relies on well-designed single-guide RNA (sgRNA), so a lot of bioinformatic tools have been developed to assist the design of highly active and specific sgRNA. These tools vary in design specifications, parameters, genomes and so on. To help researchers to choose their proper tools, we reviewed various sgRNA design tools, mainly focusing on their on-target efficiency prediction model and off-target detection algorithm.

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Interdisciplinary Sciences: Computational Life Sciences
https://doi.org/10.1007/s12539-018-0298-z
REVIEW
Review ofCRISPR/Cas9 sgRNA Design Tools
YingboCui1 · JiamingXu1· MinxiaCheng2· XiangkeLiao1· ShaoliangPeng1,2,3
Received: 6 March 2018 / Revised: 2 April 2018 / Accepted: 4 April 2018
© Springer-Verlag GmbH Germany, part of Springer Nature 2018
Abstract
The adaptive immunity system in bacteria and archaea, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-
associate (CRISPR/Cas), has been adapted as a powerful gene editing tool and got a broad application in genome research
field due to its ease of use and cost-effectiveness. The performance of CRISPR/Cas relies on well-designed single-guide
RNA (sgRNA), so a lot of bioinformatic tools have been developed to assist the design of highly active and specific sgRNA.
These tools vary in design specifications, parameters, genomes and so on. To help researchers to choose their proper tools,
we reviewed various sgRNA design tools, mainly focusing on their on-target efficiency prediction model and off-target
detection algorithm.
Keywords CRISPR· CRISPR/Cas9· SgRNA design· On-target efficiency· Off-target detection
1 Introduction
CRISPR/Cas is the adaptive immunity system in many
bacteria and most archaea [1–3], which provides immunity
against viruses and plasmids. In this system, the DNA of
invaded virus or plasmid will be cleaved into novel spacer
and stored in an array in DNA. When the same virus or plas-
mid invades again, the corresponding invading DNA will be
recognized and interfered [2].
In 2012, researchers ported the CRISPR/Cas system
to genome editing and explain the basic mechanism of
CRISPR/Cas9 [4, 5]. CRISPR/Cas9 consists of two parts:
sgRNA and Cas9 endonuclease. The two components form
a complex to cleave target DNA sites, as shown in Fig.1.
sgRNA is derived from a fusion of the tracrRNA (trans-
activating crRNA) and crRNA (CRISPR RNA) [2, 4].
From the engineering perspective, sgRNA consists of two
parts: constant part (purple in Fig.1), which forms a scaf-
fold by several stem-loop for Cas9 binding, and a 5′-end
20-nt altered part (pink in Fig.1) complementary to target
DNA sequence, which is programmable to target different
DNA sites [6]. Cas9 is nuclease derived from Streptococcus
pyogenes (S. pyogenes) [7]. The target site in DNA mainly
contains two parts: protospacer (black in Fig.1) comple-
mentary to the 5′-end 20-nt sequence in sgRNA and proto-
spacer adjacent motif (PAM, yellow in Fig.1) bound by
Cas9. PAM is usually short (for SpCas9 is 5′-NGG-3′) and
directly adjacent to protospacer. The Cas9 protein will not
cleave sequence without the presence of PAM.
If the protospacer pairs with the 5′-end 20-nt sequence,
and the Cas9 binds with PAM, a double-strand break (DSB)
will be made. After DSB, DNA repair machinery will start
and catalyze non-homologous end joining (NHEJ) or homol-
ogy-directed repair (HDR), as shown in Fig.1. For NHEJ,
the lost sequence may cannot be recovered and results in
sequence insertions or deletions (indels), which produce
gene loss of function, while for HDR, an introduced exog-
enous DNA template will fill the gap from DSB [8].
Except for gene loss of function (CRISPRko) by making
a DSB, CRISPR/Cas9 system can also repress or boost the
expression of specific gene [9–11]. Gene repression (CRIS-
PRi) can be achieved by binding a catalytically dead Cas9
lacking cleaving activity to the transcription factor binding
sites [9], and gene activation (CRISPRa) can be realized by
Yingbo Cui and Jiaming Xu are equal contributors.
* Shaoliang Peng
pengshaoliang@nudt.edu.cn
1 College ofComputer, National University ofDefense
Technology, Changsha410073, China
2 College ofComputer Science andElectronic Engineering,
Hunan University, Changsha410082, China
3 National Supercomputing Center inChangsha,
Changsha410082, China

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the fusion of an inactive Cas9 to the transcriptional activa-
tion domain [10, 11].
Compared with previous gene editing technologies, zinc-
finger nucleases (ZFNs) [12–18] and transcription activa-
tor-like effector nucleases (TALENs) [19–22], which bind
to specific DNA sequence by protein-DNA recognition,
CRISPR/Cas9 system identifies specific site by the com-
plementarity between the 20-nt sequence at the 5′-end of
sgRNA and the protospacer in DNA, which is easier and
cheaper to implemented in engineering. Besides, some
studies show that CRISPR/Cas9 is more efficient than
ZFNs and TALENs in gene editing [23–25]. Due to these
advantages, it has been used in many species and mamma-
lian cells [26–34]. In addition, some researchers have used
the CRISPR/Cas9 system for genome-wide genetic screens
invitro and invivo [35–43].
Currently, the large amount of sgRNA design tools vary
in design specifications, parameters, genomes and so on [44,
45]. To help researchers choose the most suitable sgRNA
design tools for their experiments, we review current sgRNA
design tools, mainly focusing on the on-target efficiency pre-
diction models and off-target prediction algorithms.
2 Overview ofsgRNA Design Tools
The major work of sgRNA design is to find target sites in
genome, which is pretty easy to implement by scanning
PAM sequence (like 5′-NGG-3′ for SpCas9). However, sev-
eral challenges exist for designing a good sgRNA: efficacy
and specificity.
In theory, if the 5′-end 20-nt sequence in sgRNA is com-
plementary to the target DNA sequence, the sgRNA-Cas9
complex should bind to the site and make a cleavage, but
in practice, some studies suggest that the cutting efficiency
varies significantly among sgRNAs [5, 26, 27, 33, 35–37,
46–60]. Predictive models to identify target sites with high
efficiency are necessary.
Another challenge is off-target activity of sgRNA (speci-
ficity). The off-target is caused by both sgRNA and Cas9.
A few mismatches between the 5′-end 20-nt sequence in
sgRNA and target DNA sequence are tolerated [61]. Some
studies have shown that CRISPR/Cas9 non-specially cleave
DNA sites with several mismatches generating off-target
mutations with considerable frequencies [10, 47, 52, 62–73].
The optimal PAM recognized by SpCas9 is 5′-NGG-3′.
However, SpCas9 also binds to 5′-NAG-3′ or 5′-NGA-3′
with low frequencies [10, 57, 62, 74]. It is essential to find
out potential off-target sites and improve sgRNA specificity.
To design sgRNA with high efficacy and specificity, many
models and computational tools have been developed. Some
representative ones are summarized in Table1.
2.1 On‑target Efficiency Prediction
The Root laboratory proposed a rule to predict on-target
efficacy of sgRNAs [75]. They analyzed all possible target
sites for six mouse genes and three human genes, includ-
ing 1841 sgRNAs. In their model, they employed a sup-
port vector machine (SVM) model [90] to choose subsets
with the best generalization error from 586 features [75].
The selected features by SVM were trained with a logis-
tic regression classifier [91] to generate a sgRNA on-target
Fig. 1 CRISPR/Cas9 genome
editing system

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Table 1 Representative tools for sgRNA design
Tool Genome specific Nuclease Input On-target predic-
tion
Off-target scoring Websites References
CRISPR.mit Yes (17) SpCas9 DNA sequence No Hsu etal., 2013
[62]
http://crisp r.mit.
edu/
[62]
sgRNA designer No SpCas9
SaCas9
DNA sequence
Transcript ID
Gene ID
Gene Symbol
Rule Set 2 [50] CFD [50]https ://porta
ls.broad insti
tute.org/gpp/
publi c/analy
sis-tools /sgrna
-desig n
[50, 75]
E-CRISP Yes (55) Cas9
Custom PAM
Gene ID
Gene Symbol
DNA sequence
Xu etal., 2015
[51]
Doench etal.,
2014 [75]
E-score [76]
S-score [76]http://www.e-
crisp .org/E-
CRISP /
[76]
CHOPCHOP Yes (111) 7 PAMs
Custom PAM
RefSeq
Gene ID
Genomic region
Rule Set 2 [50]
Xu etal., 2015
[51]
Chari etal., 2015
[56]
Moreno-Mateos
etal., 2015 [58]
Doench etal.,
2014 [75]
G20
Cong etal., 2013
[26]
Hsu etal., 2013
[62]
https ://chopc hop.
rc.fas.harva
rd.edu/
[77, 78]
CRISPRseek No No Software package Doench etal.,
2014 [75]
CFD [50]
Hsu etal., 2013
[62]
http://www.bioco
nduct or.org/
packa ges/relea
se/bioc/html/
CRISP Rseek
.html
(Local R Pack-
age)
[79]
Cas-OFFinder Yes (36) 16 PAMs crRNA sequences No Cas-OFFinder
[80]
http://www.rgeno
me.net/cas-offin
der/
[80]
CRISPRdirect Yes (16) Custom PAM DNA sequence
Genomic region
Accession num-
bers
No kmer + PAM [81]http://crisp r.dbcls
.jp/
[81]
CCTop Yes (65) 11 PAMs DNA sequence CRISPRater [55] Stemmer etal.,
2017 [80]
https ://crisp r.cos.
uni-heide lberg
.de/index .html
[68]
CRISPRscan Yes (13) SpCas9
LbCpf1
AsCpf1
DNA sequence
Gene ID
Gene symbol
Moreno-Mateos
etal., 2015 [58]
Cong etal., 2013
[26]
CFD [50]
Hsu etal., 2013
[62]
http://www.crisp
rscan .org/
[58]
PROTOSPACER No SpCas9 Gene ID
DNA Sequence
Genomic Region
Doench etal.,
2014 [75]
Hsu etal., 2013
[62]
http://www.proto
space r.com/
[82]
sgRNA Scorer
1.0
No SpCas9
StCas9
DNA Sequence Chari etal., 2015
[56]
No https ://crisp r.med.
harva rd.edu/
sgRNA Score
rV1/
[56]
WU-CRISPR Yes (2) SpCas9 DNA Sequence
Gene ID
Gene Symbol
Wong etal., 2015
[49]
Wong etal., 2015
[49]
http://crisp r.wustl
.edu
[49]
SSC No SpCas9 DNA Sequence Xu etal., 2015
[51]
No http://cistr ome.
org/SSC/
[51]

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efficacy prediction model. The efficiency score from this
model is in the range of 0–1, where the higher values mean
more effective. They implemented the prediction model in
their online tool, sgRNA Designer [75]. Several tools use
this model to predict sgRNA on-target efficiency, such as
E-CRISP [76], CHOPCHOP [77, 78], PROTOSPACER
[82], CLD [85], CRISPOR [89], and CRISPETa [92]. This
tool supports medium-level throughput in query and one
remarkable feature is that the model in this tool is based on
the experimental data, so it is suitable for simultaneously
generate several reliable guides. CRISPRpred, also resorts
to learning algorithm to improve efficiency in predicting
on-target activity and performs better than SVM model in
Doench etal. [75, 93].
In 2016, the Root laboratory improved their model and
proposed the Rule Set 2 [50]. Comparing with their previous
model, the Rule Set 2 incorporated several new features,
such as counts of position-independent nucleotide, location
of target site in the corresponding gene, and melting tem-
perature. Besides, the authors compared the performance
of several classification-based models in machine learning
and referred to the gradient-bossed regression trees in their
reinforced model. GuideScan [67], CHOPCHOP [77, 78],
CRISPOR [89], and Guide Picker [94] use the Rule Set 2 to
evaluate activity of sgRNA.
The Liu laboratory assessed the features affecting
sgRNA efficiency in CRISPR screen data [51]. They used
Elastic-Net [95] to construct the predictive model and
found some new features, mainly the preference for cyto-
sine for sgRNA. They applied their model in SSC [51],
CRISPR-FOCUS [53] and CRISPR-DO [60]. E-CRISP
[76], CHOPCHOP [77, 78], CLD [85], CRISPOR [89],
Table 1 (continued)
Tool Genome specific Nuclease Input On-target predic-
tion
Off-target scoring Websites References
CROP-IT Yes (2) SpCas9 Genomic Region No Singh etal., 2015
[69]
http://cheet
ah.bioch .virgi
nia.edu/AdliL
ab/CROP-IT/
homep age.html
[69]
CRISPR Multi-
Targeter
No SpCas9
Custom PAM
DNA Sequence
Gene Symbol
Gene ID/
Transcripts ID
RefSeq Sequence
Doench etal.
2014 [75]
GT-Scan [83]
Cas-OFFinder
[80]
http://www.multi
crisp r.net/
[84]
CLD No SpCas9 Gene ID
Genomic Region
Xu etal., 2015
[51]
Doench etal.
2014 [75]
Custom
Heigwer etal.,
2016 [68]
https ://githu
b.com/boutr
oslab /cld (Local
Perl Package)
[85]
CRISPOR Yes (258) 9 PAMs DNA Sequence
Genomic Region
Rule Set 2 [50]
Xu etal., 2015
[51]
Chari etal., 2015
[56]
Doench etal.,
2014 [75]
Housden etal.,
2015 [86]
Prox GC [87, 88]
CFD [50]
Hsu etal., 2013
[62]
http://crisp or.tefor
.net/
[89]
sgRNA Scorer
2.0
No 6 PAMs
Custom PAM
DNA Sequence Chari etal., 2015
[57]
No https ://crisp r.med.
harva rd.edu/
sgRNA Score
rV2/
[57]
GuideScan Yes (6) SpCas9
AsCf1
LbCpf1
Genomic Region
Gene Symbol
Rule Set 2 [50] CFD [50]http://www.guide
scan.com/
[67]
CASPER No Custom PAM DNA Sequence CASPER [52] CASPER [52]https ://githu
b.com/Trinh
Lab/CASPE R
(Local Python
Package)
[52]

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and pgRNAFinder [96] also adopt this model. Besides,
authors of SSC found the effect of sequence context on
sgRNA efficiency differs significantly between CRIS-
PRko and CRISPRa/i and further proposed a model to
predict efficiency of sgRNA in CRISPRa/i. Due to the use
of machine learning method, high efficiency of designed
candidate guides is remarkable [97].
As the same demand for targeting invivo, Moreno-
Mateos etal. discovered that guanine enrichment and ade-
nine depletion contribute to the stability, loading and activity
of sgRNA by an analysis of target sites of 128 genes, where
ten sgRNAs for each gene [58]. They used logistic regres-
sion model to select features associated with sgRNA activ-
ity, including features on mononucleotide and dinucleotide,
and they used a linear regression model for sgRNA activ-
ity prediction. The model is integrated in their online tool
CRISPRscan [58]. CHOPCHOP [77, 78] and CRISPOR [89]
also use this model to predict sgRNA activity. Nevertheless,
one factor should be noticed that this efficiency prediction
model is based on the data of zebrafish and applying this
model to other species will receive worse results compared
with other tools.
The Church laboratory developed an invivo methodol-
ogy to evaluate sgRNA activity across thousands of genes
[56]. The two libraries refer to target-site library and sgRNA
library, respectively. They built a SVM classifier model to
score the on-target activity of sgRNA sequences. They also
observed the preference for guanine in position 20, next to
the PAM in SpCas9 and St1Cas9 (Streptococcus thermophi-
lus). The model was implemented in their online tool sgRNA
Scorer. CHOPCHOP [77, 78] and CRISPOR [89] applied
this model for on-target efficiency prediction. Its improved
version sgRNA scorer 2.0 [57] established a more general-
ized model with a new combined SVM, which was proved
to be useful for predicting sgRNA activity for multiple Cas9
orthologs and Cpf1 protein. The SVM model implemented
in this tool highly improved on-target prediction power,
whereas the analysis procedure is slow and the results have
to be returned by email.
WU-CRISPR [49] identified some novel structural and
sequence features from Doench’s dataset [75], and built a
sgRNA potency prediction model with SVM. They used the
Chari dataset [56] to evaluate the model and got the best per-
formance compared with three other tools [51, 56, 75]. This
tool is recommended for its highly efficient sgRNA design
using machine learning method and ease of use.
The Huang laboratory used Elastic-Net algorithm [95]
in their model to predict sgRNA efficiency [59]. They bor-
rowed knowledge from oligonucleotide design in microarray
and found that including position-dependent features can
help the design of effective sgRNA. The authors further
found that the frequency of T and TT affects the efficiency
of sgRNA significantly.
Although some studies found guanine enrichment can
improve the stability and potency of sgRNA [35, 51, 58,
75], Malina etal. discovered that too many PAMs (5′-NGG-
3′) within the target DNA sites inhibit the CRISPR/Cas9
activity in vivo [54]. Some other studies suggested the pref-
erence for guanines rather than adenines for the stability of
sgRNA [35, 75]. Taking these into consideration, CASPER
[52] integrate the CRISPRscan [58] score, PAM penalty
score [54] and a score indicating guanines preference [35,
75] into its predictive model. Their tool also expanded the
applications in multi-targeting analysis and multi-population
analysis.
Labuhn etal. utilized the surrogate fluorescent reporter
knock-out assays to assess and analyze a dataset of 430
sgRNAs [55]. They found nine additional features affecting
sgRNA activity and generated a linear model-based discrete
system using R package lars [98]. Their model enables the
exclusion of impotent sgRNA, implemented in their online
tool CRISPRater.
Some other tools include more than one on-target effi-
ciency prediction models and provide more choices for
users, such as E-CRISP [76], CHOPCHOP [77, 78], CLD
[85], and CRISPOR [89]. The details of the corresponding
tools are shown in Table1.
2.2 Off‑target Detection
The Zhang laboratory investigated sgRNA target specificity
by evaluating more than 700 sgRNA variants and predicated
off-target loci which is induced by SpCas9 with indel muta-
tion levels > 100 [62]. They found that mismatch tolerance
between SpCas9 complex and DNA is influenced by the
number, position and distribution of mismatches. Based on
this observation, they proposed a penalty matrix (Table2)
to describe the effect of mismatch position. The penalty is
between 0 and 1 where higher value means bigger effect
on cleavage. Referring to this penalty matrix correspond-
ing to each position, each sgRNA can be assigned a score
according to its potential off-target sites, as an evidence to
choose appropriate sgRNAs. The authors also implemented
a web-based software tool to facilitate sgRNA design and
validation (http://crisp r.mit.edu). This method is widely used
for sgRNA specificity score calculation, such as CRISPR-
scan [58], CRISPR-DO [60], CHOPCHOP [77, 78], and
CRISPOR [89]. However, this tool is only suitable for short
sequences (23–500nt) and the analysis is very slow.
Stemmer etal. noticed the limitation of previous sgRNA
off-target detecting tools and developed an online sgRNA
finding tool, CCTop [68]. This tool provides more cus-
tom options for users to choose. A graphical visualization
interface makes it easier to identify the appropriate sgRNA
among the candidates. Many researchers use third party
sequence alignment tools such as BWA [99] and Bowtie

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[100] for target site searching [66, 76, 77]. In CCTop, the
candidate sgRNAs are all selected by Bowtie [100] and
ranked by their off-target potential. The off-target score of
each sgRNA is defined as follows:
In this equation,
dist
means the distance between this
off-target site and the closest exon,
scoreoff _target
is calcu-
lated by the relative position of mismatches in protospacer.
total_off _targets
means the total number of candidate off-
target sites. The authors validated the predicted results with
experiments and proved its effectiveness. This tool provides
many custom options which are both favorable for primary
users and advanced users. Additionally, the analysis speed in
simple mode is fast, whereas advanced mode is slow.
The Root laboratory analyzed off-target effects on pooled-
screening data [50], with all possible sgRNAs targeting the
coding regions of human CD33 cell line, regardless of PAM.
The author profiled many mismatch conditions between
sgRNA and DNA, including alternative PAMs, mismatches,
deletions and insertions. Using the experimental data, they
proposed an algorithm to rank potential off-targets, which
is called the Cutting Frequency Determination (CFD) score.
They compared CFD score to off-target effect measurements
of Hsu–Zhang [62]. and CCTop [68]. Throughout various
(1)
Score
=

off _target
log10(dist)+scoreoff _ta rget
total_off _targets −total_off _target −b±

b2−4ac
2a
.
different examinations, they concluded that CFD score per-
forms better and could be able to avoid high-frequency off-
target effects. Some other tools apply this scoring algorithm
in their workflows, such as CRISPRScan [58], GuideScan
[67], and CRISPOR [89]. Here a recent study indicates that
CFD score performs better than CCTop [68], MIT-Zhang
[62] and CROP-IT [69] score when analyzing less than four
mismatches [89].
Mendoza etal. found that the previous off-target scor-
ing method cannot quickly accommodate across organisms,
so they proposed a novel algorithm to assess the off-target
activity, named CASPER [52]. The output of this algorithm
consists of three factors: SH, ST, and SS. SH is a score which
is calculated by the experimental data of mismatch posi-
tion and types derived from the Hsu–Zhang matrix [62]
(Table2). ST is calculated by the distance of mismatch
from PAM. SS is a stepped score, associated with the posi-
tion of mismatch in spacer. Overall, SH is derived from the
experimental data and ST, SS are derived from the properties
associated with CRISPR ribonucleoprotein complexes. Fur-
thermore, they adapted their scoring algorithm to as Cpf1
endonucleases and exhibited that CASPER can still effec-
tively assess its off-target activity even without sufficient
experimental data.
There are also many other tools devised their own algo-
rithms to evaluate off-target potential of sgRNA, such
as WU-CRISPR [49], CROP-IT [69], CT-Finder [73],
E-CRISPR [76], CGAT [101], sgRNAcas9 [102], and
CRISTA [103]. Overall, the off-target detection and evalu-
ation algorithms in these tools also have instructive value
in sgRNA design. Actually, after a lot of experiments,
researchers found out that using single off-target model can-
not always be effective and reliable in sgRNA design [89].
Based on this consideration, many tools provide advanced
options for researchers to choose the off-target detection
model or show several scores calculated by widely used
models in their output. CHOPCHOP [77, 78], an online
tool for sgRNA design, provides off-target scoring method
including two models devised by Hsu etal. [62] and Cong
etal. [26], separately. CRISPOR [89] offers scoring models
by Hsu etal. [62] and Doench etal., 2016 [50] (i.e., CFD).
CRISPR-RT [104] aims at crRNA design in CRISPR-C2c2/
Cas13a system and provides a variety of custom options for
users. It returns the candidate crRNA target sites by search-
ing the reference transcriptome with user-input RNA/cDNA
sequence. Users can rank all results by transcript-target num-
bers, gene-target numbers, GC content or more properties
of target sites.
Table 2 Experimentally
determined penalty weight
matrix of mismatch position
[62]
Mismatch rela-
tive 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

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3 Other Considerations ofsgRNA Design
Throughout various studies in CRISPR system, research-
ers have noticed that many other factors also influence the
gene editing performance of CRISPR. Here we summarize
some of these factors as follows:
Truncated sgRNAs with 17- to 19-nt spacer are more
sensitive to mismatches than others with 20-nt length,
which can effectively reduce off-target mutations [46]. In
addition, the recent study has showed that shorter spacers
such as 17 to 18 nt have higher specificity but perform less
on-target effective than 19- or 20-nt spacers [51]. On the
contrary, longer sgRNAs are less effective than sgRNAs
with 20-nt spacers [58].
Zetsche etal. found that Cpf1 (CRISPR from Prevo-
tella and Francisella 1) protein might be more convenient
in gene editing than Cas9 system, because it only need
mature crRNAs for targeting, while CRISPR/Cas9 system
requires both tracrRNA and crRNA. T-enriched PAM and
staggered DSB at distal end of spacer are notable features
in Cpf1, bringing new options in experimentation [105].
Fonfara etal. have studied the Cas9 homologs and
identified specific PAM sequences and dual-RNA (i.e.,
tracrRNA-crRNA) in some Cas9 orthologous proteins (for
example, S. thermophilus, S. mutans, P. multocida and N.
meningitidis). They also verified the coevolution between
dual-RNA and Cas9 protein through experimentation.
Their study indicates that the Cas9 orthologs and associ-
ated PAM and dual-RNA can become candidate compo-
nents in CRISPR/Cas9 system [106].
Ran etal. devised a double nicking approach by a pair
of sgRNA-guided Cas9n (Cas9 D10A) nickases, which can
effectively reduce the off-target effects. In this gene editing
approach, two modified effectors will bind to two opposite
strands and surround 10 to 31 nt target sequence and then
make a DSB [107]. Some recent studies have applied this
technique in their research [108, 109].
Tsai etal. described a dimeric architecture in CRISPR
system called dimeric RNA-guided FokI nucleases (RFNs).
This kind of dimeric nickases implement cleavage by two
guide RNAs (gRNAs) binding to the opposite strands. This
dimeric architecture can promise any nucleotide at 5′ end of
gRNA and relative to paired nickases, which makes steps
forward in improving gRNA specificity [110].
CRISPR gene editing system can be divided into two
categories, CRISPRko and CRISPRa/i. The DNA target
sites vary for these two categories. In CRISPRko, target
sites are usually protein-coding region and Cas9 nucle-
ases produce loss-of-function gene knockouts by inducing
DSB. In CRISPRa/i, sgRNA guides catalytically inactivate
Cas9 (dCas9) to effector domains to activate or repress
gene transcription without DNA cleavage [50, 51].
Canonical sgRNA including 20-nt sequence and PAM
has limited target sites in vivo, because T7 or SP6 promot-
ers in CRISPR/Cas9 system restrict GG or GA in the 5′-end
of sgRNA sequence [58]. Experimental results showed
that there are two kinds of alternative truncated sequences
which can become efficient substitutes to replace the 20-nt
sequence, one is truncated sgRNA at 5′ end by less than
2-nt and the other is the 20-nt sgRNA with one mismatch
in 5′ end [58].
Some researchers investigated the epigenetics features,
and found that chromatin accessibility affects the binding of
dCas9 to DNA sites [111, 112]. Chari etal. also observed
this phenomenon [56]. Whereas Moreno-Mateos etal. did
not notice a strong effect of chromatin accessibility on
CRISPR/Cas9 activity [58].
Four consecutive T (i.e., TTTT) is a signal of termina-
tion for pol III promoter thus the presence of TTTT in target
DNA sequence sites cannot be adoptable. It is necessary to
avoid TTTT in target sites when using pol III promoter [81].
4 Conclusion
CRISPR/Cas9 technology has been used more and more
widely for genome editing. Its efficacy, specificity, easy to
use, cost-effectiveness and versatility, will boost this tech-
nique on more fronts. In this review, we examined various
aspects of sgRNA design tools, including activity prediction
models, and off-target detection algorithms. Almost all of
these models or algorithms depend on large-scale experi-
mental datasets and systematic analysis. Therefore, with
more CRISPR/Cas9 datasets available, more novel sgRNA
design tools will be developed to facilitate the research
community.
Acknowledgements We would like thank to Jingyu Amy Peng and
Chen-Hao Chen from Harvard T.H. Chan School of Public Health,
Shenglin Mei and Jian Ma from Tongji University for their discus-
sion about this work. This work was supported by National Key
R&D Program of China 2017YFB0202602, 2017YFC1311003,
2016YFC1302500, 2016YFB0200400, 2017YFB0202104; NSFC
Grants 61772543, U1435222, 61625202, 61272056; the Funds of State
Key Laboratory of Chemo/Biosensing and Chemometrics; the Funda-
mental Research Funds for the Central Universities; and Guangdong
Provincial Department of Science and Technology under grant No.
2016B090918122.
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