Chapter 4
Computational Prediction of CRISPR/Cas9 Target Sites
Reveals Potential Off-Target Risks in Human and Mouse
Qingbo Wang and Kumiko Ui-Tei
Abstract
The clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated (Cas) system
is a prominent genome engineering technology. In the CRISPR/Cas system, the RNA-guided endonuclease Cas protein introduces a DNA double-stranded break at the genome position recognized by a guide
RNA (gRNA) based on complementary base-pairing of about 20-nucleotides in length. The 8- or 12-mergRNA sequence in the proximal region is especially important for target recognition, and the genes withsequence complementarity to such regions are often disrupted. To carry out target site-specific genome
editing, we released the CRISPRdirect (http://crispr.dbcls.jp/) website. This website allows us to selecttarget site-specific gRNA sequences by performing exhaustive searches against entire genomic sequences.
In this study, target site-specific gRNA sequences were designed for human, mouse, Drosophila melanogaster, and Caenorhabditis elegans. The calculation results revealed that at least five gRNA sequences, each ofthem having only one perfectly complementary site in the whole genome, could be designed for more than
95% of genes, regardless of the organism. Next, among those gRNAs, we selected gRNAs that did not haveany other complementary site to the unique 12-mer proximal sequences to avoid possible off-target effects.
This computational prediction revealed that target site-specific gRNAs are selectable for the majority ofgenes in D. melanogaster and C. elegans. However, for >50% of genes in humans and mice, there are notarget sites without possible off-target effects.
Key words CRISPR/Cas9, Target site, Off-target gene, CRISPR direct

1

Introduction
Genome engineering using the clustered regularly interspaced
short palindromic repeat/CRISPR-associated (CRISPR/Cas) system has been widely applied in recent years due to its simplicity andwide range of applicability [1–5]. The step-by-step mechanism of
the CRISPR/Cas system, which is derived from the adaptive
immune system of prokaryotes, is as follows [6–8]:(a) The Cas protein, an RNA-guided endonuclease, interacts witha single-guide RNA (sgRNA) to form a Cas-sgRNA complex.
The sgRNA is a short RNA artificially constructed by

Izuho Hatada (ed.), Genome Editing in Animals: Methods and Protocols, Methods in Molecular Biology, vol. 1630,
DOI 10.1007/978-1-4939-7128-2_4, © Springer Science+Business Media LLC 2017

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Qingbo Wang and Kumiko Ui-Teic­ onnecting CRISPR RNA (crRNA) with the trans-activating
crRNA (tracrRNA).
(b) The Cas protein in the Cas-sgRNA complex recognizes a specific sequence motif called proto-spacer adjacent motif (PAM),
5′-NGG in the case of Streptococcus pyogenes, downstream of
the target site. If the 20-mer sequence upstream of the PAM is
homologous to the guide RNA (gRNA) spacer sequence, thegRNA spacer region pairs with the complementary strand
upstream of the PAM (target sequence).
(c) After binding to the target sequence, the Cas protein in theCas-sgRNA complex cleaves both DNA strands a few bases
upstream of the PAM sequence to introduce a double-stranded
break (DSB).
(d) The DSB site is repaired by error-prone non-homologous end
joining (NHEJ) that often results in a small insertion or deletion. When there is a homologous template DNA, the site isrepaired by homology-directed repair (HDR) that results in
insertion of a specific DNA sequence.
Genome engineering using the CRISPR/Cas system allows
for the introduction of DSBs to induce NHEJ or HDR in theintended genomic regions by designing a target site-specific gRNA
sequence. Due to this, the CRISPR/Cas system is advantageouscompared to previous genome engineering techniques based onprotein engineering technology (such as transcription activator-­
like effector nucleases “TALENs” and zinc finger nucleases“ZFNs”), which require considerable efforts to design effective
proteins [9–11].
However, the stringency of target sequence recognition by theCas-sgRNA complex is not well understood. Previous studies haverevealed that site recognition and cleavage by the Cas protein canoccur even when there are gaps or mismatches between the gRNA
spacer and target sequences [12–14]. A number of different mismatch patterns have been reported for such nonspecific cleavage(“off-target effect”), but the 8 or 12 nucleotides upstream of thePAM (seed region) are especially important for target site recognition. In many cases, off-target mutations happen at sites where thegRNA seed region has no mismatches but the non-seed region
does [3, 15]. To knock out a specific region in the genome, suchoff-target effects should be avoided. Reducing such risk is especially important when we consider further application of the technology, including therapeutic applications.
In this study, we implemented a computational pipeline todesign site-specific gRNAs. Our pipeline enabled evaluation of the
off-target risk of each gRNA by calculating the number of seed-­matched off-target candidate sequences in the entire genome andallowed for the design of off-target risk-reduced gRNAs. Using theComputational Prediction of CRISPR/Cas9 Target Sites...

45

pipeline, the number of applicable gRNAs was gg (Subheading
3.4. also, see Note 1). The percentage of genes that had a certain
number of such gRNAs was also calculated and compared between
four different organisms (Subheadings 3.5 and 3.6).

2

Materials

2.1 Protein-Coding
Sequence Preparation

1. All human (hg19, GRCh37 Genome Reference Consortium
Human Reference 37), mouse (mm10, Genome Reference
Consortium Mouse Build 38), Drosophila melanogaster (D.
melanogaster) (dm3, BDGP Release 5), and Caenorhabditis
elegans (C. elegans) (ce10, Washington University School of
Medicine GSC and Sanger Institute WS220) mRNA sequences(protein-coding sequences) were downloaded from RefSeq,
through the UCSC Genome Browser (https://genome.ucsc.
edu/index.html) and stored as fasta files (Fig. 2, step 1).
2. The table describing the relationship between mRNA ID andgene name was also downloaded from the UCSC Table
Browser by changing the “output format” to “all fields fromselected table.” This table was used to map each mRNA to thecorresponding gene.

2.2 Software
for Genome-Wide
Specificity Check

The CRISPRdirect web server [16] (https://crispr.dbcls.jp) wasused to conduct genome-wide investigation of off-target sites.
This server investigates the entire genome of the organisms ofinterest for PAM-proximal 20-, 12-, or 8-mer-matched sequences,and lists all the possible gRNA sequences and the number ofmatched sequences.

2.3 Building
Environmentfor Iteration
and Visualization

Bash (GNU bash, version 3.2.51(1)-release) and Python 2.7.5
were used to construct a computational pipeline (Fig. 2) for iterative design of site-specific gRNAs against each gene. “Pandas”[17], an open-source tool for data analysis, was used to parse thedata into tab-separated value (tsv) files. The calculation results
were visualized using matplotlib [18], a python library for 2D
plotting. The entire process, described in the next chapter (fromgRNA search to graph visualization), was automated in a single
pipeline under the environment.

3

Methods

3.1 Directory
Structure Construction

Before beginning the calculation process, the directories for output file storage were structured as shown in Fig. 1 using bash
scripts.

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Qingbo Wang and Kumiko Ui-Tei

Fig. 1 The directory structure for storage of output files. The home directory contains src/ (where source codes
are stored), graph/ (where the visualized results of the calculation are stored), and the directory of each organism. The directories for each of the genes were positioned under the directory of each organism, and followed
by mRNA directories. The calculation summary for all the genes of a single organism (gene_property) is stored
under the directory of each organism. Each directory of a gene contains a list of all the target site-specificgRNAs for the gene (gRNA_list). The results of off-target candidate search using CRISPRdirect software(direct_result n) and the genomic sequence of each mRNA (seqn.fa) are stored under the directory of eachmRNA. Blue boxes indicate directories. Black boxes, tsv or fasta files. “~,” the lower directories

Fig. 2 Workflow of the target site-specific gRNA selection process with a minimum number of off-target hits,
and genes that have more than a certain number of selectable gRNAs per gene. STEP 1: Preparation of mRNA
sequences used. All mRNA sequences (protein-coding mRNA sequences) were downloaded through the UCSC
Genome Browser (https://genome.ucsc.edu/index.html). STEP 2: Genome-wide search for off-target candidates
using CRISPRdirect. Given the query sequence, CRISPRdirect investigates the entire genome for PAM-proximal
20-, 12-, or 8-mer-matched sequences. STEP 3: Target site-specific gRNA selection based on STEP2 results. (a)The schematic structure of 20-mer on-target region, 12-mer off-target region, and PAM region used for the evaluation of gRNA specificity. (b) The mRNA regions where gRNAs were designed in this study. STEP 4: Selection ofgenes containing a sufficient number of target site-specific gRNAs based on the STEP 3 results. (a) A gene containing more than five gRNAs, for example. (b) The selection step of genes under different conditions

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Qingbo Wang and Kumiko Ui-Tei

The workflow of the calculation pipeline is shown in Fig. 2,
and the details of each step are shown below.
3.2 Genome-WideSearch for Off-Target
Candidates

Each mRNA sequence, stored in a fasta format, was posted to oursoftware, CRISPRdirect [16], using the API provided by the software (Fig. 2, step 2). The list of possible gRNA candidates withthe specificity check results were then stored in the local directory
as a tsv file.

3.3 Target Site-­
Specific gRNA
Selection

The target site-specific gRNA candidates satisfying the following
conditions were selected:(a) Uniqueness of the target sequence among the entire genome:
No perfect match other than the target site (20-mer sequence+ PAM) was allowed (Fig. 2, step 3a).
(b) Limitation of the number of possible off-target sites with seed
(PAM-proximal 12-mer sequence) complementarity among
the entire genome: Only a limited number of seed-matchedsites was allowed (Fig. 2, step 3a).
(c) Absence of a “TTTT” stretch: No “TTTT” stretch (more than
three sequential Ts) was allowed in the gRNA sequence.
(d) The target site was positioned in the common exons among all
the transcription variants of a target gene (Fig. 2, step 3b).
Conditions (a) and (b) were applied to reduce the risk of off-­target cleavage, and (c) was applied to avoid the termination ofgRNA transcription by RNA polymerase III (the “TTTT” stretch
is a known RNA polymerase III termination site). Condition (d)was needed to disrupt the target gene expression regardless of thetranscription pattern (see Notes 2–4).
The gRNAs that did not satisfy any one of four conditions
shown above were removed using a filtering operation in Pandas,
and the gRNAs that simultaneously satisfied all four conditions
were defined as “target site-specific gRNA candidates.”

3.4 Calculating
the Number of Target
Site-Specific gRNA
Candidates per GeneGiven the list of target site-specific gRNA candidates selected in
Subheading 3.3, the number of selectable target site-specific gRNA
candidates for each gene was calculated in four organisms: human,
mouse, D. melanogaster, and C. elegans. The cumulative fraction
distribution of genes (y axis) as a function of the number of selectable gRNAs (x axis) was shown in two different cases: (1) the casewhere an unlimited number of seed-matched off-target candidate
sites was allowed (Fig. 3a), and (2) no seed-matched site otherthan the target site was allowed (Fig. 3b). In the former case, C.
elegans had relatively fewer gRNAs compared to the other threeorganisms (Fig. 3a). However, the cumulative curves for C. elegans
and D. melanogaster were similar to each other but different fromhumans and mice when no seed-matched site other than the target
site was allowed (Fig. 3b).

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49

Fig. 3 Comparison of the number of target site-specific gRNA candidates per gene, mean CDS length, andgenome sizes of human (blue), mouse (green), D. melanogaster (red), and C. elegans (light blue). (a), (b)Cumulative distributions of the genes as a function of the number of gRNAs with a 20-mer (a) or 12-mer (b)completely matched unique site per gene. The x axis indicates the number of designable gRNAs per gene, andthe y axis is the cumulative fraction of genes. The blue and green vertical lines indicate the cumulative fractions of human and mouse genes reached to 1.0, respectively, in (b). The mean CDS length (c) and genome
sizes (d) of four different organisms used in this research. For CDS length, the mean length of transcription
variants was calculated for each gene, and then the mean CDS length was calculated for each species

The results indicated that at least two different parameters may
contribute to specificity. First, in our selection conditions, since thetarget region that we designed gRNAs for was restricted to thecoding sequence (CDS), the relatively short average CDS length(Fig. 3c) was likely the reason for the lower abundance of gRNAs
in C. elegans, especially in the case where an unlimited number ofseed-matched sites was allowed. Second, the genome sizes of thesefour organisms differ by double-digits, and range from 109 to 107
bp (Fig. 3d). Since the off-target search was performed on a

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Qingbo Wang and Kumiko Ui-Tei

genome-wide scale, the off-target hits may increase based on
genome size. The sharp decrease in abundance of target site-­
specific gRNAs in human and mouse genomes could be attributed
to their large genome sizes. Thus, it may be difficult to select alarge amount of target site-specific gRNAs without off-target candidates in organisms with large genomes.
3.5 Selectionof Genes with Morethan Five Target
Site-Specific gRNAs
for Each Gene

Even when a target site-specific gRNA with a perfectly matched target sequence is selected, there is no guarantee that the target sitewould be cleaved by the gRNA [19, 20] since the conditions that
determine gRNA sequence functionality are not well known (seeNote 5). One of the possible procedures to efficiently induce DSB
is to design multiple gRNAs that target different sites of the samegene. For example, in recent large-scale knock-out experiments, fivegRNAs were designed for each gene, and numerous gene sets corresponding to fundamental biological processes in mammalian cells
were identified [21, 22]. In an analogous way, we used the genes forwhich more than five target site-specific gRNAs were selectable, anddefined them as “5 ≤ gRNA” genes (Fig. 2, step 4a). The percentage of such genes was calculated based on the results shown in
Fig. 3a and b. Since gRNA abundance is highly dependent on thenumber of off-target hits that we allowed, we gradually changed the
maximum number of seed-matched off-­target hits per gRNA andcounted the selectable gRNA abundance for each condition. The
results revealed that at least five gRNA sequences with only one perfectly complementary site in the whole genome could be designed
for more than 95% of genes for all four organisms when neglecting
possible off-target risks with 12-mer matched sequence (Fig. 4a).
However, the percentage of such genes decreased with the decreased
number of possible off-target sites when 12-mer matched sequenceswere allowed, especially for human and mouse genomes. The results
suggest that when considering seed-matched off-target risks, knockout screening experiments using the CRISPR/Cas system would be
relatively more feasible for D. melanogaster and C. elegans, compared to humans and mice.

3.6 Selection of “N ≤gRNA” Genes

For the human genome, which had the lowest percentage, theminimum number of target site-specific gRNA candidates waschanged for each selected gene (i.e., we selected “N ≤ gRNA”
genes, where N is not restricted to 5, ranging from 1 to 10.) to
examine the changes in percentage of selectable genes. The results(Fig. 4b) clearly show that there is a trade-off relationship between
the minimum gRNA-abundance threshold N (y axis) and the maximum number of off-target candidate sites allowed per gRNA (xaxis): If we strictly select gRNAs that have fewer 12-mer matched
off-target candidate sites, we can only select a few gRNAs per gene.
In other words, the N parameter must be relatively small to maintain a high percentage of selectable genes.

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51

Fig. 4 The percentage of “N ≤ gRNA” genes. (a) The percentage of genes with more than five target site-­
specific gRNAs in different organisms (y axis), with different numbers of 12-mer matched sequences allowed(x axis). The colors of the bars correspond to specific organisms: human (blue), mouse (green), D. melanogaster (red), and C. elegans (light blue). Note that the “∞” column of (a) corresponds to the intersection of x = 5
and each curve in Fig. 3a, and the “1” column of (a) to that in Fig. 3b. (b) Heat map of the percentage of “N ≤gRNA” genes (N ranging from 1 to 10) in the human genome, with different numbers of possible off-target sites
with 12-mer matched sequences allowed. Y axis, N for “N ≤ gRNA” gene. X axis, the number of 12-mermatched sequences allowed. The color gradient indicates the percentage of genes in each condition. (c)Comparison of the percentage of genes with more than five target site-specific gRNAs, with and without considering the splice variants of each gene. The dense blue bar indicates the result from selecting gRNAs targeting common regions among splice variants, and the light blue bar is the result of selecting gRNA not limited tothe common region

Overall, our study provided a computational pipeline to design
target gene-specific gRNAs, and we quantitatively compared thenumber of selectable candidate gRNAs per gene among four different organisms under differing selection conditions. This pipeline may be applied to data sets of different organisms that were
not analyzed in this research.

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Qingbo Wang and Kumiko Ui-Tei

Notes
1. The time needed to complete the calculation depended mainlyon the speed of web communication, rather than the algorithm
itself. When the human genome (hg19, 41,845 mRNAs, and19,132 genes) was used, the whol0e process required 48 hours
to complete (Subheading 3.3, Fig. 2, step 2).
2. The gRNAs that target exon-exon junctions, the regions without continuous genomic DNA sequences, are eliminated by
the CRISPRdirect algorithm (Subheading 3.2), and therefore
were not included in this study.
3. In this research, the off-target candidate sites were estimatedbased on the complementarity to 12-mer, rather than 8-mer,seed sequences to increase prediction accuracy as shown in
Subheading 3.3, step (b) (Fig. 2, step 3a). Although seedmatch is important for target recognition, different patterns ofoff-target hits are reported [13]. For example, cell type, chromatin state, or SNP presence are also important factors [23–25]. Revealing more in-depth mechanisms of target site
recognition to reduce off-target effects could also bemeaningful.
4. If it is not necessary to target the common regions of all transcription variants of a gene, it is possible to eliminate condition(d) in Subheading 3.3 (Fig. 2, step 3b). The alteration can beeasily achieved by making a minor change in the Python script.
Figure 4c shows the comparison of the percentages in thehuman genome with and without condition (d).
5. As described in Subheading 3.5, the major reason we designed
multiple gRNAs for a single gene is because the editing efficiency of each gRNA in the CRISPR/Cas system is not known.
Therefore, we did not exclude gRNAs that are likely to show noor weak genome engineering function in this research. Additional
research may improve our understanding of both the efficiency
and off-target risks of the CRISPR/Cas system, leading us toselect truly target-specific and highly efficient gRNAs.

Acknowledgment
We thank Dr. Yuki Naito for valuable discussion and technicaladvice. The English in this document has been checked by at least
two professional editors, both native speakers of English. This
work was supported by the grants from the Ministry of Education,
Culture, Sports, Science and Technology of Japan to K.U.-T.

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