Schema for CRISPR Targets - CRISPR/Cas9 -NGG Targets, whole genome
  Database: mm39    Primary Table: crisprAllTargets Data last updated: 2020-08-13
Big Bed File Download: /gbdb/mm39/crisprAll/
Item Count: 276,331,386
The data is stored in the binary BigBed format.

Format description: crispr targets
chromchr1Reference sequence chromosome or scaffold
chromStart130102843Start position in chromosome
chromEnd130102866End position in chromosome
nameName or ID of item, ideally both human readable and unique
score72Score (0-1000)
strand-+ or - for strand
thickStart130102846Start of where display should be thick (start codon)
thickEnd130102866End of where display should be thick (stop codon)
reserved0,200,0Doench 2016 / Fusi et al. Score
_crisprScanColor255,100,100Moreno-Mateos Score
_specColor0,255,0MIT Specificity Score
pamGGGProtospacer Adjacent Motif (PAM)
scoreDesc72MIT Guide Specificity Score
fusi65Efficiency: Doench et al. 2016 Score (raw)
fusiPerc92Efficiency: Doench et al. 2016 Score (percentile)
crisprScan24Efficiency: Moreno-Mateos T7 Score (raw)
crisprScanPerc12Efficiency: Moreno-Mateos T7 Score (percentile)
doench80Efficiency: Doench et al 2014 Score
oof68Bae et al. Out-of-Frame Score
_mouseOverMIT Spec. Score: 72, Doench 2016: 92%, Moreno-Mateos: 12%Label for Mouse-over
_offset81079578683Offset into tab-sep file for details page

Sample Rows
chr113010284313010286672-1301028461301028660,200,0255,100,1000,255,0ATAAAACTAAGAATACGGAAGGG72659224128068MIT Spec. Score: 72, Doench 2016: 92%, Moreno-Mateos: 12%81079578683
chr113010284413010286768-1301028471301028670,200,0255,100,100128,128,0AATAAAACTAAGAATACGGAAGG68556622101469MIT Spec. Score: 68, Doench 2016: 66%, Moreno-Mateos: 10%63743546505
chr113010284813010287137-13010285113010287180,80,8080,80,80255,0,0GTAAAATAAAACTAAGAATACGG37453540394363MIT Spec. Score: 37, Doench 2016: 35%, Moreno-Mateos: 39%261811081887
chr113010285613010287927+13010285613010287680,80,8080,80,80255,0,0TTAGTTTTATTTTACAGAACAGG27392123111765MIT Spec. Score: 27, Doench 2016: 21%, Moreno-Mateos: 11%375407445104
chr113010287313010289665+1301028731301028930,200,0255,255,0128,128,0AACAGGAAATTCATGTTCAGAGG65628641411663MIT Spec. Score: 65, Doench 2016: 86%, Moreno-Mateos: 41%158324190361
chr113010294213010296569+1301029421301029620,200,0255,100,100128,128,0TATCTCCAACTACAGATGACTGG6958751861874MIT Spec. Score: 69, Doench 2016: 75%, Moreno-Mateos: 6%53923644579
chr113010294713010297076-1301029501301029700,200,00,200,00,255,0TGGCACCAGTCATCTGTAGTTGG76546352651456MIT Spec. Score: 76, Doench 2016: 63%, Moreno-Mateos: 65%143971467036
chr113010296713010299051-1301029701301029900,200,0255,100,100128,128,0CAGTGGAAAGAAACTGGGCTTGG51628631221468MIT Spec. Score: 51, Doench 2016: 86%, Moreno-Mateos: 22%245004475569
chr113010297213010299552-1301029751301029950,200,0255,255,0128,128,0AGGCACAGTGGAAAGAAACTGGG52638945503775MIT Spec. Score: 52, Doench 2016: 89%, Moreno-Mateos: 50%246834807905
chr113010297313010299643-13010297613010299680,80,8080,80,80255,0,0GAGGCACAGTGGAAAGAAACTGG4334134346570MIT Spec. Score: 43, Doench 2016: 13%, Moreno-Mateos: 46%225135050728

CRISPR Targets (crisprAllTargets) Track Description


This track shows the DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG) over the entire mouse (mm39) genome. CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool CRISPOR. Sp-Cas9 usually cuts double-stranded DNA three or four base pairs 5' of the PAM site.

Display Conventions and Configuration

The track "CRISPR Targets" shows all potential -NGG target sites across the genome. The target sequence of the guide is shown with a thick (exon) bar. The PAM motif match (NGG) is shown with a thinner bar. Guides are colored to reflect both predicted specificity and efficiency. Specificity reflects the "uniqueness" of a 20mer sequence in the genome; the less unique a sequence is, the more likely it is to cleave other locations of the genome (off-target effects). Efficiency is the frequency of cleavage at the target site (on-target efficiency).

Shades of gray stand for sites that are hard to target specifically, as the 20mer is not very unique in the genome:

impossible to target: target site has at least one identical copy in the genome and was not scored
hard to target: many similar sequences in the genome that alignment stopped, repeat?
hard to target: target site was aligned but results in a low specificity score <= 50 (see below)

Colors highlight targets that are specific in the genome (MIT specificity > 50) but have different predicted efficiencies:

unable to calculate Doench/Fusi 2016 efficiency score
low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30
medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 55
high predicted cleavage: Doench/Fusi 2016 Efficiency > 55

Mouse-over a target site to show predicted specificity and efficiency scores:

  1. The MIT Specificity score summarizes all off-targets into a single number from 0-100. The higher the number, the fewer off-target effects are expected. We recommend guides with an MIT specificity > 50.
  2. The efficiency score tries to predict if a guide leads to rather strong or weak cleavage. According to (Haeussler et al. 2016), the Doench 2016 Efficiency score should be used to select the guide with the highest cleavage efficiency when expressing guides from RNA PolIII Promoters such as U6. Scores are given as percentiles, e.g. "70%" means that 70% of mammalian guides have a score equal or lower than this guide. The raw score number is also shown in parentheses after the percentile.
  3. The Moreno-Mateos 2015 Efficiency score should be used instead of the Doench 2016 score when transcribing the guide in vitro with a T7 promoter, e.g. for injections in mouse, zebrafish or Xenopus embryos. The Moreno-Mateos score is given in percentiles and the raw value in parentheses, see the note above.

Click onto features to show all scores and predicted off-targets with up to four mismatches. The Out-of-Frame score by Bae et al. 2014 is correlated with the probability that mutations induced by the guide RNA will disrupt the open reading frame. The authors recommend out-of-frame scores > 66 to create knock-outs with a single guide efficiently.

Off-target sites are sorted by the CFD (Cutting Frequency Determination) score (Doench et al. 2016). The higher the CFD score, the more likely there is off-target cleavage at that site. Off-targets with a CFD score < 0.023 are not shown on this page, but are available when following the link to the external CRISPOR tool. When compared against experimentally validated off-targets by Haeussler et al. 2016, the large majority of predicted off-targets with CFD scores < 0.023 were false-positives. For storage and performance reasons, on the level of individual off-targets, only CFD scores are available.


Relationship between predictions and experimental data

Like most algorithms, the MIT specificity score is not always a perfect predictor of off-target effects. Despite low scores, many tested guides caused few and/or weak off-target cleavage when tested with whole-genome assays (Figure 2 from Haeussler et al. 2016), as shown below, and the published data contains few data points with high specificity scores. Overall though, the assays showed that the higher the specificity score, the lower the off-target effects.

Similarly, efficiency scoring is not very accurate: guides with low scores can be efficient and vice versa. As a general rule, however, the higher the score, the less likely that a guide is very inefficient. The following histograms illustrate, for each type of score, how the share of inefficient guides drops with increasing efficiency scores:

When reading this plot, keep in mind that both scores were evaluated on their own training data. Especially for the Moreno-Mateos score, the results are too optimistic, due to overfitting. When evaluated on independent datasets, the correlation of the prediction with other assays was around 25% lower, see Haeussler et al. 2016. At the time of writing, there is no independent dataset available yet to determine the Moreno-Mateos accuracy for each score percentile range.

Track methods

The entire mouse (mm39) genome was scanned for the -NGG motif. Flanking 20mer guide sequences were aligned to the genome with BWA and scored with MIT Specificity scores using the command-line version of Non-unique guide sequences were skipped. Flanking sequences were extracted from the genome and input for Crispor efficiency scoring, available from the Crispor downloads page, which includes the Doench 2016, Moreno-Mateos 2015 and Bae 2014 algorithms, among others.

Note that the Doench 2016 scores were updated by the Broad institute in 2017 ("Azimuth" update). As a result, earlier versions of the track show the old Doench 2016 scores and this version of the track shows new Doench 2016 scores. Old and new scores are almost identical, they are correlated to 0.99 and for more than 80% of the guides the difference is below 0.02. However, for very few guides, the difference can be bigger. In case of doubt, we recommend the new scores. can display both scores and many more with the "Show all scores" link.

Data Access

Positional data can be explored interactively with the Table Browser or the Data Integrator. For small programmatic positional queries, the track can be accessed using our REST API. For genome-wide data or automated analysis, CRISPR genome annotations can be downloaded from our download server as a bigBedFile.

The files for this track are called, which lists positions and scores, and, which has information about off-target matches. Individual regions or whole genome annotations can be obtained using our tool bigBedToBed, which can be compiled from the source code or downloaded as a pre-compiled binary for your system. Instructions for downloading source code and binaries can be found here. The tool can also be used to obtain only features within a given range, e.g.

bigBedToBed -chrom=chr21 -start=0 -end=1000000 stdout


Track created by Maximilian Haeussler, with helpful input from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU).


Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud JB, Schneider-Maunoury S, Shkumatava A, Teboul L, Kent J et al. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR. Genome Biol. 2016 Jul 5;17(1):148. PMID: 27380939; PMC: PMC4934014

Bae S, Kweon J, Kim HS, Kim JS. Microhomology-based choice of Cas9 nuclease target sites. Nat Methods. 2014 Jul;11(7):705-6. PMID: 24972169

Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, Orchard R et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nat Biotechnol. 2016 Feb;34(2):184-91. PMID: 26780180; PMC: PMC4744125

Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O et al. DNA targeting specificity of RNA-guided Cas9 nucleases. Nat Biotechnol. 2013 Sep;31(9):827-32. PMID: 23873081; PMC: PMC3969858

Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods. 2015 Oct;12(10):982-8. PMID: 26322839; PMC: PMC4589495