 Research
 Open Access
Whole genome analysis of CRISPR Cas9 sgRNA offtarget homologies via an efficient computational algorithm
 Hong Zhou^{1}Email author,
 Michael Zhou^{2},
 Daisy Li^{2},
 Joseph Manthey^{1},
 Ekaterina Lioutikova^{1},
 Hong Wang^{3} and
 Xiao Zeng^{4}
 Published: 17 November 2017
Abstract
Background
The beauty and power of the genome editing mechanism, CRISPR Cas9 endonuclease system, lies in the fact that it is RNAprogrammable such that Cas9 can be guided to any genomic loci complementary to a 20nt RNA, single guide RNA (sgRNA), to cleave double stranded DNA, allowing the introduction of wanted mutations. Unfortunately, it has been reported repeatedly that the sgRNA can also guide Cas9 to offtarget sites where the DNA sequence is homologous to sgRNA.
Results
Using human genome and Streptococcus pyogenes Cas9 (SpCas9) as an example, this article mathematically analyzed the probabilities of offtarget homologies of sgRNAs and discovered that for large genome size such as human genome, potential offtarget homologies are inevitable for sgRNA selection. A highly efficient computationl algorithm was developed for whole genome sgRNA design and offtarget homology searches. By means of a dynamically constructed sequenceindexed database and a simplified sequence alignment method, this algorithm achieves very high efficiency while guaranteeing the identification of all existing potential offtarget homologies. Via this algorithm, 1,876,775 sgRNAs were designed for the 19,153 human mRNA genes and only two sgRNAs were found to be free of offtarget homology.
Conclusions
By means of the novel and efficient sgRNA homology search algorithm introduced in this article, genome wide sgRNA design and offtarget analysis were conducted and the results confirmed the mathematical analysis that for a sgRNA sequence, it is almost impossible to escape potential offtarget homologies. Future innovations on the CRISPR Cas9 gene editing technology need to focus on how to eliminate the Cas9 offtarget activity.
Keywords
 sgRNA
 Offtarget homology
 Crispr
 Cas9
 Computational algorithm
 Genome wide
Background
Derived from the microbial clustered, regularly interspaced, short palindromic repeats (CRISPR) system, the Cas9 endonuclease has become an effective and reliable tool for genome editing in eukaryotes [1–6]. The magnificence of the working mechanism of Cas9 is that it can be guided by a 20base sgRNA, immediately upstream the short DNA motif of Cas9, the so called protospacer adjacent motif (PAM), to almost any genome loci where the DNA sequence is complementary to the sgRNA [1–4]. The PAM sequence is absolutely required for Cas9 to function and depends on the species of Cas9. For SpCas9, the most used Cas9 species, the PAM sequence is NGG, where N can be either A, C, G, or T. The very first step in making use of the sgRNACas9 system for genome editing is to locate a primary PAM within the target region. Immediately upstream the PAM, the 20 bases of DNA sequence is the guide RNA sequence. Though they can be on either the sense or antisense strand, the PAM and sgRNA sequences must be on the same DNA strand.
Certain rules regarding the design of active sgRNAs have been proposed [6, 7]. As the gene editing mechanism of sgRNACas9 is to generate indels via DNA repairing mechanisms, it is not difficult to understand that for mRNA genes, the target site should better be inside the gene coding sequence and be near the start codon. Another design rule is the GC content. It was found that higher sgRNA GC content could result in higher Cas9 activities [8]. In addition, the design of sgRNA should avoid certain sequences, for example, polyT [7].
One of the most important design rules is to avoid potential Cas9 offtarget activity. Unfortunately, a significant number of experiments discovered undesired offtarget cleavages by Cas9 at offtarget genome sites where the DNA sequences are homologous to the 20base sgRNA, though with one or more mismatches [7–16]. Considering the large size of some genomes, for example human, mouse and rat genomes, avoiding offtarget Cas9 activities immediately becomes the most critical challenge in the application of the sgRNACas9 technology. Systematic research has revealed sequence features governing sgRNA offtarget interaction. However, the possible offtarget Cas9 cleavages remain a defect and a challenge in sgRNACas9 applications.
The large number of offtarget studies of the sgRNACas9 system has led to significant discoveries. Jinek et al. was the first to identify a seed sequence that is less tolerant to mismatches for sgRNACas9 activity [1]. The definition of the seed sequence is generally considered to be the 12 bases on the 3′ end of sgRNA sequence, immediately upstream PAM [1, 10–12]. Mali et al. found that sgRNACas9 system can tolerate one to three target mismatches, and two mismatches inside the seed sequence can eliminate offtarget activity [11]. Based on their data, Fu et al. concluded that offtarget activity can be observed with up to five mismatches when the concentrations of both sgRNA and Cas9 are relatively high [9]. Hsu et al. discovered that offtarget activity depends on the number and positions of the mismatches between sgRNA and target DNA sequence [10]. Lin et al. systematically studied the sgRNACas9 offtarget activities when there are indels between target DNA and sgRNA sequences [13]. Their results showed that sgRNAs with low GC content have less tolerance to mismatches. They also found, that a bulge in sgRNA or DNA preserves less Cas9 activity, a result later confirmed by Doench et al. [7].
Making the offtarget activity of sgRNACas9 system even more complicated, it has been observed that secondary PAM sequences, in addition to the NGG motifs, can render Cas9 activity [3, 7, 17, 18]. Though these secondary PAMs are far less effective compared to the NGG PAMs, they must be taken into consideration for offtarget searches [3, 7]. For SpCas9, the secondary PAMs include NAG, NCG, and NGA [3, 7].
The complexity of the Cas9sgRNA offtarget interaction and the large size of human genome led us to wonder the probability that a given sgRNA sequence has at least one offtarget homology. Theoretically, will it be possible to apply the Cas9sgRNA system without any potential offtarget homologies that may introduce unwanted genome editing? In this article, we analyze this question from a mathematical perspective, and then present a very efficient algorithm for sgRNA offtarget homology search. This algorithm can complete a whole genome sgRNA design and offtarget search in about 40 h under a default setting, an efficiency that cannot be achieved by other available sgRNA software. Via this algorithm, we searched the offtarget homologies for all sgRNAs designed for all human mRNA genes. The computational results confirmed our mathematical analysis.
Methods
The human genome was the sequence source used in this study. As SpCas9 is the most widely used CRISPRCas9 system, this study focuses on the mathematical and computational analysis of sgRNASpCas9 system. Human mRNA refseq sequence was downloaded from NCBI as the source for sgRNA sequence design. The offtarget site search for designed sgRNA sequences were conducted on human chromosome sequences hs_ref_GRCh38.p2 which were also downloaded from NCBI. Computational programs were implemented in Java and executed on a 2016 Dell Precision 7510 laptop computer with Intel(R) Core(TM) i76820HQ CPU @ 2.7 GHz and 64.00 GB RAM.
Mathematical analysis
 1.
All offtarget homologies must have a primary NGG PAM or a secondary PAM immediately downstream the sgRNA binding location.
 2.
All offtarget homologies can have up to four base mismatches within a given sgRNA sequence. If there are at least five base mismatches, the DNA sequence in study is not considered an offtarget homology. The reason for defining four instead of five base mismatches as the cutoff is because we have found only one active offtarget homology with five base mismatches in the literature, and the offtarget activity in that case could be eliminated by lowering both the Cas9 and sgRNA concentrations [9].
 3.
All offtarget homologies can have at most one bulge plus one base mismatch [8, 13]. This implies that a bulge penalty equals three base mismatches.
 4.
All offtarget homologies can have up to two base mismatches or one indel within the seed sequence of sgRNA.
 5.
No offtarget homology can have a DNA bulge that is of twobases, though an offtarget homology can have a RNA bulge of twobases but with no base mismatch at the same time. No offtarget homology can have a bulge of two bases inside the seed sequence.
Mathematical analysis of the sgRNA offtarget homologies
Total combination of 20 bp  1,099,511,627,776 

Mismatches in seed sequence  Number of combinations 
0  1 
1  36 
2  594 
Mismatches in nonseed sequence  Number of combinations 
0  1 
1  24 
2  252 
3  1512 
4  5670 
Total base mismatches  236,401 
DNA bulge with 0 base mismatch  64 
RNA bulge with 0 base mismatch  60 
DNA bulge with 1 base mismatch  2112 
RNA bulge with 1 base mismatch  1968 
RNA bulge of two bases  32 
Total combinations of homologies  240,637 
Offtarget homology probability of a 20base DNA sequence  0.00000021886 
Probability of potential PAM  0.2500 
Offtarget homology probability  0.00000005471 
The number of combinations of DNA sequences with different numbers of mismatches is computed by the expression.
\( \left(\genfrac{}{}{0pt}{}{m}{n}\right)\times {3}^n \) ,
where m = the length of the DNA sequence in consideration, n = number of mismatches. Thus, for the seed sequence of 12 bases, there are 1, 36 and 594 combinations respectively for zero, one and two base mismatches.
As the total base mismatches cannot exceed four, the available base mismatches for the remaining nonseed regions would be zero, one, two, three and four, and can only have a maximum of three or two base mismatches if the seed sequence has one or two base mismatches. So, the total combinations of homologies with up to four base mismatches is computed as:
The computation of the number of combinations of indels deserves a detailed explanation. There are two cases, DNA bulge, i.e. there is an additional base in the DNA sequence, and RNA bulges, i.e., when there are one or two less bases in the DNA sequence. For both DNA bulge and RNA bulge, there are two subcases, i.e. a bulge with zero or one base mismatch. However, for RNA bulge of two bases (there are two bases less inside the aligned DNA sequence), the number of base mismatches must be zero. In addition, if the bulge is inside the seed sequence, then no base mismatch is allowed to be inside the seed sequence.
We start with the DNA bulge with zero mismatches, which means that the 20base RNA sequence is in fact aligned with a 21base DNA sequence and all the 20 bases of sgRNA must have an exact match to a base in the DNA sequence. In a 20 vs 20 exact alignment, there are a maximum of 20 positions in the DNA sequence to insert one additional base, and this additional base can be either one of A, C, G, T. There are two additional restrictions when considering a DNA bulge: a DNA bulge can be considered only when there are at least five base mismatches between the sgRNA and DNA sequences (20 bases vs 20 bases) and the introduction of the bulge can trade off more than the number of base mismatches that a bulge penalty equals. Thus, when introducing a bulge inside the DNA sequence, the DNA fragment left of the bulge must be at least four bases such that there are enough base mismatches to be traded off by the bulge. Therefore, there are 16 × 4 = 64 combinations. Via the same logic, the RNA bulge with no base mismatches will have 15 × 4 = 60 combinations.
When there is an indel and a mismatch, the computation becomes a bit more complicated. For DNA bulge, the bulge can be anywhere but the mismatch can only be inside the nonseed region if the bulge is already inside the seed sequence. Thus, the maximum combinations of the indel plus a base mismatch would be
However, for RNA bulge case, the expression would be
The last condition in consideration is the RNA bulge of two bases. Since a twobases RNA bulge can only be inside the nonseed region, there are only two different ways to form such a RNA bulge because the introduction of such a bulge must trade off at least five base mismatches. The combinations would be
Based on data in Table 1, the probability for a 23base single DNA region to be an offtarget homology for a given sgRNA sequence is 0.00000005471. Considering the fact that there are six billion 23base single DNA sequences, The probability for a sgRNA to have no potential offtarget homology is 2.67 × 10^{−143}, and the expected number of offtarget sites is 328.
Based on the above mathematical analysis, it seems that for a given SpCas9 sgRNA sequence, potential offtarget homologies in the human genome are unavoidable.
Computational algorithm
We implemented a sgRNA design and offtarget search algorithm in Java. The sgRNA design is based on the rules outlined in [6, 7] with the following exceptions: 1) sgRNA are designed only inside the first half CDS sequence; 2) all sgRNAs do not contain a run of four T or four A.
As the offtarget search must be conducted through all the human chromosome sequences, the offtarget search of sgRNA can be very time expensive. The high efficiency of our offtarget search process comes from two critical algorithmic innovations which are explained below in detail.
The first innovation is that an indexed database based on the seed sequence variations is dynamically constructed before any homology search work starts. Based on assumption 4, for a DNA region to be an offtarget homology of a given sgRNA, it must have a good alignment with the sgRNA seed sequence such that there should be at most two mismatches or one indel. Hence, the offtarget homology search starts with finding those DNA sequences that are variations of the sgRNA seed sequence. The seed sequence consists of 12 bases, so there are 4^{12} different 12base variations in total. If we assign 0, 1, 2, 3 to A, C, G, T respectively and convert DNA sequence to a base4 number system, then each 12base variation can then be represented as a unique integer using the expression \( \sum_{i=0}^{11}N\times {4}^i \), where N = 0, 1, 2, 3, representing A, C, G, T respectively.
Since the package was implemented in Java whose int data type can only hold integers ranging from −2^{31} to 2^{31}1 and the human genome has about three billion base pairs, i.e. six billion bases, we decided to divide the 24 chromosomes into two groups with roughly equal number of nucleotides. For each group, a twodimensional array G_{ij} is constructed as follows: i = the integer value of each 12base sequence, the row G[i] stores all the positions of the 12base sequence (equivalent to integer i) in the group of chromosomes. A positive G[i][j] indicates that the position is on the sense strand while a negative G[i][j] means that the 12base sequence is found on the antisense strand. Given the integer G[i][j], a conversion system matches it to a specific chromosome, a specific NT record, and a specific position inside the NT sequence. An important tip in constructing the twodimensional array G_{ij} is that G_{ij} only stores the location information of those 12base sequences followed by a primary PAM or a secondary PAM.
Given a 20base sgRNA sequence, based on its 12base seed sequence, all variations of its 12base seed sequence are generated according to Assumption 4, which are interpreted as: 1) a variation can have at most two mismatches with this seed sequence; 2) a variation can have at most one indel when aligned against the seed sequence. The homology search algorithm then finds all the exact positions inside each NT record for all the different variations very quickly and then uses a dynamic programming algorithm to determine if there is an offtarget homology at each position.
The above algorithm computes the number of base mismatches only, which are the values in Table 2. For L1, L2, R1 and R2, as there is a specific bulge for each case, the total number of mismatches should add the specific bulge penalty. In our default setting, a bulge penalty equals three base mismatches (counted as two if inside the seed sequence), a RNA bulge extension penalty equals one base mismatch, and a DNA bulge extension penalty equals two base mismatches. Thus in Table 2, when L1 is computed, though it is shown that L[13] = L[14] = L[15] = L[16] = 1, they are in fact = 1 + DNA bulge penalty = 4. The result shows that by shifting the 5′ fragment (up to either the 13th, 14th, 15th, or 16th base) one base to the left, we can achieve an alignment with only one base mismatch and one DNA bulge.

Since the seed sequence has more stringent requirements on the number of mismatches, the number of base mismatches and indels within the seed sequence should be counted and stored to determine whether or not a specific alignment should be considered as an offtarget homology. In the example shown in Table 2, though the case of L1 can achieve a good alignment with only one base mismatch and one DNA bulge, d is eventually not considered a homology to r because both the DNA bulge and the base mismatch are inside the seed sequence,

There are a total of five cases that are computed in this algorithm: H, L1, R1, L2, R2. If in one case d is found to be a homology to r, there is no need to go on to the next case.

For cases L1, R1, L2, and R2, a shortcut can be applied. If (m + bulge penalty) become larger than the number of base mismatches allowed, there is no need to continue computing for that case because it is guaranteed that the alignment represented by this case is not a homology.
Results and discussion
We first simulated a human genome of size three billion base pairs in which A, C, G, T are randomly distributed. With this simulated genome, we examined the offtarget homologies for 1,000,000 sgRNAs randomly designed from the simulated genome and the 1,876,775 sgRNAs designed for the 19,153 human mRNA genes based on the above design rules. The offtarget homology search identified 326 homologies per sgRNA in average for the group of 1,000,000 sgRNAs and 325 homologies per sgRNA in average for the group of 1,876,775 sgRNAs. Both results are fairly close to the mathematically expected 328 homologies. In fact, the mathematically expected values should be slightly larger than the computational experimental values because of two reasons. The first reason is that the mathematically calculated number of combinations for the case with one indel plus one base mismatch is the possible maximum number. The real number should be slightly smaller. The second reason can be explained by using the sequence alignment (DNA) ACCCCT/acccct (RNA) as an example. Removing any C will generate the same RNA bulge ACCCT/acccct, i.e. the computational experiment will detect one RNA bulge while the mathematical model would count four times. Overall, in agreement with our mathematical model, no sgRNA was found to be free of homologies with the simulated genome.
The computational experiment with human genome identified that only two out of the 1,876,775 sgRNAs were validated to be free of offtarget homology. This confirms our mathematical analysis that theoretically, it is almost impossible for a sgRNA to have no potential offtarget homologies. A total of 1,415,606,013 offtarget homologies were found, indicating 754 offtarget homologies per sgRNA. This number is significantly larger than the mathematical expected value. We believe that the large discrepancy was resulted from the fact that human DNA sequence is not a random composition of A, C, G, T. There are a large number of repeated sequences in human genome [19]. As we once pointed out [20], some sgRNAs with repeated sequences have an unusually large number of offtarget homologies, which contributes to the large discrepancy.
It is worth to point out that of the 1,415,606,013 homologies, about 2.70% are with indels. Thus, even though the offtarget homologies are mostly base mismatches, indels are a significant portion of offtarget homologies and should be considered. Some sgRNA offtarget search algorithms, for example, CasFinder and CRISPOR, do not detect indels, and thus miss a significant number of offtarget homologies [21, 22].
The time cost to complete a whole genome sgRNA design and offtarget homology examination is mostly on the homology examination. The time cost is a linear function of the number of sgRNAs. Furthermore, based on our homology examination algorithm, it is easy to understand that the time cost is also a function of the offtarget homology definition. Under our default homology examination settings, the time cost to complete the whole genome design and offtarget examination for the 1,876,775 sgNRAs is about 40 h. It is roughly about 77 s for every 1000 sgRNAs.
Compared with CasFinder which is built upon Bowtie, our package is much more efficient. Under a similar homology examination setting (the seed sequences allows maximum two mismatches, the 20base sequence allows totally up to four base mismatches but no bulge, and the secondary PAM is only NAG), CasFinder took 624 h to complete the design and offtarget examination of its 927,104 sgRNAs while our algorithm took about 22 h to examining 1,876,775 sgRNAs [21]. Roughly speaking, our algorithm is about 57 times faster than CasFinder.
CasOFFinder employed a similar strategy as our algorithm except that they first computed the variations of the 20base guide sequence with up to certain number of mismatches [23]. With each varied sequence, they tended to find an exact match in the genome. We also compared our algorithm’s efficiency with theirs under the same conditions: up to five base mismatches, no indels, and only consider the NGG and NAG PAM. CasOffinder’s maximum speed via GPU is about 3.01 s per sgRNA sequence. However, when comparing the CPU efficiency, CasOffinder’s maximum speed is about 60.03 s per sgRNA sequence, while ours is about 3.15 s per sgRNA sequence.
Comparison between CRISPOR, CasOFFinder and the proposed algorithm on offtarget homology search for EMX1 sgRNA guide sequence
Number of offtarget homologies identified  

Number of base mismatches  0  1  2  3  4 
CRISPOR  0  0  6  38  296 
CasOFFinder  0  0  1  18  273 
Our Algorithm (with Secondary PAM)  0  0  6  87  1227 
Our Algorithm (without secondary PAM)  0  0  1  18  273 
Under exactly the same conditions, our algorithm found exactly the same offtarget homologies as CasOFFinder and CRISPOR did. The only difference is that, by default, our algorithm searched for offtarget homologies anchored with all the secondary PAMs including NAG, NCG and NGA. The webtool of CasOFFinder did not search for any secondary PAM, while CRISPOR considered only a few PAMs (NAG, AGA, GGA, TGA).
The large expected number of homologies for each sgRNA has been motivating scientists to search for different solutions. A double nicking approach was then introduced to enhance genome editing specificity [11, 25]. The double nicking method is based on the Cas9 nickase mutant that can only break one single strand of DNA. To obtain a double stranded cleavage, simultaneous nicking via two individual sgRNAs each targeting a different strand is necessary [25]. The offset, the distance between the 5′ ends of the two sgRNA sequences (sgRNA pair), must be between −4 and 20 for the paired nicking to work well, and if the offset of the paired sgRNAs is less than −34 or larger than 110 bases, the pairedsgRNACas9 system completely loses its efficacy [25]. Thus, a potential offtarget homology for paired sgRNA nicking must have two single offtarget homologies positioned in a way that their offset is between −34 and 110 bases inclusive. After 387,679 sgRNA pairs were designed for the 19,153 mRNA genes, 175,712 sgRNA pairs were found to be free of offtarget homologies, covering 14,665 mRNA genes. This confirms that the double nicking method is much more reliable than the original SpCas9sgRNA system in avoiding offtarget homologies, a finding reported before [16, 25].
Conclusions
A novel and efficient sgRNA homology search algorithm was introduced in this article. Via this algorithm, genome wide sgRNA design and offtarget analysis were conducted and the results confirmed the mathematical analysis that for a sgRNA sequence, it is almost impossible to escape potential offtarget homologies. Future innovations on the CRISPR Cas9 gene editing technology need to focus on how to eliminate the Cas9 offtarget activity.
Declarations
Acknowledgements
Not applicable.
Funding
The publication costs were funded by Hong Zhou’s institutional award from University of Saint Joseph.
Availability of data and materials
All data generated or analysed during this study are included in this published article.
About this supplement
This article has been published as part of BMC Genomics Volume 18 Supplement 9, 2017: Selected articles from the IEEE BIBM International Conference on Bioinformatics & Biomedicine (BIBM) 2016: genomics. The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/articles/supplements/volume18supplement9.
Authors’ contributions
All authors participated in the analysis of the data, interpretation of the results, and review of the paper. MZ, DL, and HZ performed the mathematical analysis, EL and JM reviewed the mathematical analysis. HZ and HW designed the algorithm, HZ implemented the algorithm, HZ and XZ generated the data. HZ, DL and MZ drafted the paper, JM and EL revised the paper. All authors read and approved the final version of the manuscript.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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References
 Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E. A programmable dualRNAguided DNA endonuclease in adaptive bacterial immunity. Science. 2012;337:816–21.View ArticlePubMedGoogle Scholar
 Mali P, Yang L, Esvelt KM, Aach J, Guell M, DiCarlo JE, Norville JE, Church GM. RNAguided human genome engineering via Cas9. Science. 2013;339:823–6.View ArticlePubMedPubMed CentralGoogle Scholar
 Hsu PD, Lander ES, Zheng F. Development and applications of CRISPRCas9 for genome engineering. Cell. 2014;157:1262–78.View ArticlePubMedPubMed CentralGoogle Scholar
 Wright AV, Nuñez JK, Doudna JA. Biology and applications of CRISPR systems: harnessing nature’s toolbox for genome engineering. Cell. 2016;164:29–44.View ArticlePubMedGoogle Scholar
 Travis J. Making the cut CRISPR genomeediting technology shows its power. Science. 2015;350:1456–7.View ArticlePubMedGoogle Scholar
 Doench JG, Hartenian E, Graham DB, Tothova Z, Hegde M, Smith I, Sullender M, Ebert BL, Xavier RJ, Root DE. Rational design of highly active sgRNAs for CRISPRCas9mediated gene inactivation. Nat Biotechnol. 2014;32:1262–7.View ArticlePubMedPubMed CentralGoogle Scholar
 Doench G, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R, Virgin HW, Listgarten J, Root DE. Optimized sgRNA design to maximize activity and minimize offtarget effects of CRISPRCas9. Nat Biotechnol. 2016;34:184–91.View ArticlePubMedPubMed CentralGoogle Scholar
 Cradick TJ, Fine EJ, Antico CJ, Bao G. CRISPR/Cas9 systems targeting βglobin and CCR5 genes have substantial offtarget activity. Nucleic Acids Res. 2013;41:9584–92.View ArticlePubMedPubMed CentralGoogle Scholar
 Fu Y, Foden JA, Khayter C, Maeder ML, Reyon D, Joung JK, Sander JD. Highfrequency offtarget mutagenesis induced by CRISPRCas nucleases in human cells. Nat Biotechnol. 2013;31:822–6.View ArticlePubMedPubMed CentralGoogle Scholar
 Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O, Cradick TJ, Marraffini LA, Bao G, Zhang F. DNA targeting specificity of RNAguided Cas9 nucleases. Nat Biotechnol. 2013;31:827–32.View ArticlePubMedPubMed CentralGoogle Scholar
 Mali P, Aach J, Stranges PB, Esvelt KM, Moosburner M, Kosuri S, Yang L, Church GM. Cas9 transcriptional activators for target specificity screening and paired nickases for cooperative genome engineering. Nat Biotechnol. 2013;31:833–8.View ArticlePubMedPubMed CentralGoogle Scholar
 Pattanayak V, Lin S, Guilinger JP, Ma E, Doudna JA, Liu DR. Highthroughput profiling of offtarget DNA cleavage reveals RNAprogrammed Cas9 nuclease specificity. Nat Biotechnol. 2013;31:839–43.View ArticlePubMedPubMed CentralGoogle Scholar
 Lin Y, Cradick TJ, Brown MT, Deshmukh H, Ranjan P, Sarode N, Wile BM, Vertino PM, Stewart FJ, Bao G. CRISPR/Cas9 systems have offtarget activity with insertions or deletions between target DNA and guide RNA sequences. Nucleic Acids Res. 2014;42:7473–85.View ArticlePubMedPubMed CentralGoogle Scholar
 Wu X, Scott DA, Kriz AJ, Chiu AC, Hsu PD, Dadon DB, Cheng AW, Trevino AE, Konermann S, Chen S, Jaenisch R, Zhang F, Sharp PA. Genomewide binding of CRISPR endonuclease Cas9 in mammalian cells. Nat Biotechnol. 2014;32:670–6.View ArticlePubMedPubMed CentralGoogle Scholar
 Kuscu C, Arslan S, Singh R, Thorpe J, Adli M. Genomewide analysis reveals characteristics of offtarget sites bound by the Cas9 endonuclease. Nat Biotechnol. 2014;32:677–83.View ArticlePubMedGoogle Scholar
 Cho SW, Kim S, Kim Y, Kweon J, Kim HS, Bae S, Kim JS. Analysis of offtarget effects of CRISPR/Casderived RNAguided endonucleases and nickases. Genome Res. 2014;24:132–41.View ArticlePubMedPubMed CentralGoogle Scholar
 Friedland AE, Tzur YB, Esvelt KM, Colaiacovo MP, Church GM, Calarco JA. Heritable genome editing in C. Elegans via a CRISPRCas9 system. Nat Methods. 2013;10:741–3.View ArticlePubMedPubMed CentralGoogle Scholar
 Li JF, Norville JE, Aach J, McCormack M, Zhang D, Bush J, Church GM, Sheen J. Multiplex and homologous recombinationmediated genome editing in Arabidopsis and Nicotiana Benthamiana using guide RNA and Cas9. Nat Biotechnol. 2013;31:688–91.View ArticlePubMedPubMed CentralGoogle Scholar
 http://www.repeatmasker.org/, Accessed 23 Feb 2017.
 Zhou M, Li D, Huan X, Manthey J, Lioutikova E, Zhou H. Mathematical and computational analysis of CRISPR Cas9 sgRNA offtarget homologies. In: Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine: 1518 December 2016. China: Shenzhen. p. 449–54.Google Scholar
 Aach J, Mali P, Church GM: CasFinder: Flexible algorithm for identifying specific Cas9 targets in genomes. bioRxiv 2014, doi:https://doi.org/10.1101/005074.
 Haeussler M, Schonig K, Eckert H, Eschstruth A, Mianne J, Renaud J, SchneiderMaunoury S, Shkumatava A, Teboul L, Kent J, Joly J, Concordet J. Evaluation of offtarget and ontarget scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016;17:148.View ArticlePubMedPubMed CentralGoogle Scholar
 Bae S, Park J, Kim JS. CasOFFinder: a fast and versatile algorithm that searches potential offtarget sites of Cas9 RNAguided endonuclease. Bioinformatics. 2014;30:1743–5.View ArticleGoogle Scholar
 Tsai SQ, Zheng Z, Nguyen NT, Liebers M, Topkar VV, Thapar V, Wyvekens N, Khayter C, Iafrate AJ, Le LP, Aryee MJ, Joung JK. Guideseq enables genomewide profiling of offtarget cleavage by CRISPRCas nucleases. Nat Biotechnol. 2015;33:187–97.View ArticlePubMedGoogle Scholar
 Ran FA, Hsu PD, Lin CY, Gootenberg JS, Konermann S, Trevino AE, Scott DA, Inoue A, Matoba S, Zhang Y, Zhang F. Double nicking by RNAguided CRISPR Cas9 for enhanced genome editing specificity. Cell. 2013;154:1380–9.View ArticlePubMedPubMed CentralGoogle Scholar