< crisprAllTargets | This track shows the DNA sequences targetable by CRISPR RNA guides using < crisprAllTargets | the Cas9 enzyme from S. pyogenes (PAM: NGG) over the entire < crisprAllTargets | dog (canFam5) genome. CRISPR target sites were annotated with < crisprAllTargets | predicted specificity (off-target effects) and predicted efficiency < crisprAllTargets | (on-target cleavage) by various < crisprAllTargets | algorithms through the tool CRISPOR. Sp-Cas9 usually cuts double-stranded DNA three or < crisprAllTargets | four base pairs 5' of the PAM site. < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | The track "CRISPR Targets" shows all potential -NGG target sites across the genome. < crisprAllTargets | The target sequence of the guide is shown with a thick (exon) bar. The PAM < crisprAllTargets | motif match (NGG) is shown with a thinner bar. Guides < crisprAllTargets | are colored to reflect both predicted specificity and efficiency. Specificity < crisprAllTargets | reflects the "uniqueness" of a 20mer sequence in the genome; the less unique a < crisprAllTargets | sequence is, the more likely it is to cleave other locations of the genome < crisprAllTargets | (off-target effects). Efficiency is the frequency of cleavage at the target < crisprAllTargets | site (on-target efficiency).
< crisprAllTargets | < crisprAllTargets |Shades of gray stand for sites that are hard to target specifically, as the < crisprAllTargets | 20mer is not very unique in the genome:
< crisprAllTargets || 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:
< crisprAllTargets || 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 |
< crisprAllTargets | Mouse-over a target site to show predicted specificity and efficiency scores:
< crisprAllTargets |
Click onto features to show all scores and predicted off-targets with up to < crisprAllTargets | four mismatches. The Out-of-Frame score by Bae et al. 2014 < crisprAllTargets | is correlated with < crisprAllTargets | the probability that mutations induced by the guide RNA will disrupt the open < crisprAllTargets | reading frame. The authors recommend out-of-frame scores > 66 to create < crisprAllTargets | knock-outs with a single guide efficiently.
< crisprAllTargets | < crisprAllTargets |
Off-target sites are sorted by the CFD (Cutting Frequency Determination) < crisprAllTargets | score (Doench et al. 2016). < crisprAllTargets | The higher the CFD score, the more likely there is off-target cleavage at that site. < crisprAllTargets | Off-targets with a CFD score < 0.023 are not shown on this page, but are available when < crisprAllTargets | following the link to the external CRISPOR tool. < crisprAllTargets | When compared against experimentally validated off-targets by < crisprAllTargets | Haeussler et al. 2016, the large majority of predicted < crisprAllTargets | off-targets with CFD scores < 0.023 were false-positives. For storage and performance < crisprAllTargets | reasons, on the level of individual off-targets, only CFD scores are available.
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Like most algorithms, the MIT specificity score is not always a perfect < crisprAllTargets | predictor of off-target effects. Despite low scores, many tested guides < crisprAllTargets | caused few and/or weak off-target cleavage when tested with whole-genome assays < crisprAllTargets | (Figure 2 from Haeussler < crisprAllTargets | et al. 2016), as shown below, and the published data contains few data points < crisprAllTargets | with high specificity scores. Overall though, the assays showed that the higher < crisprAllTargets | the specificity score, the lower the off-target effects.
< crisprAllTargets | < crisprAllTargets |
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< crisprAllTargets | Similarly, efficiency scoring is not very accurate: guides with low < crisprAllTargets | scores can be efficient and vice versa. As a general rule, however, the higher < crisprAllTargets | the score, the less likely that a guide is very inefficient. The < crisprAllTargets | following histograms illustrate, for each type of score, how the share of < crisprAllTargets | inefficient guides drops with increasing efficiency scores: < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |
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< crisprAllTargets | When reading this plot, keep in mind that both scores were evaluated on < crisprAllTargets | their own training data. Especially for the Moreno-Mateos score, the < crisprAllTargets | results are too optimistic, due to overfitting. When evaluated on independent < crisprAllTargets | datasets, the correlation of the prediction with other assays was around 25% < crisprAllTargets | lower, see Haeussler et al. 2016. At the time of < crisprAllTargets | writing, there is no independent dataset available yet to determine the < crisprAllTargets | Moreno-Mateos accuracy for each score percentile range.
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | The entire dog (canFam5) genome was scanned for the -NGG motif. Flanking 20mer < crisprAllTargets | guide sequences were < crisprAllTargets | aligned to the genome with BWA and scored with MIT Specificity scores using the < crisprAllTargets | command-line version of crispor.org. Non-unique guide sequences were skipped. < crisprAllTargets | Flanking sequences were extracted from the genome and input for Crispor < crisprAllTargets | efficiency scoring, available from the Crispor downloads page, which < crisprAllTargets | includes the Doench 2016, Moreno-Mateos 2015 and Bae < crisprAllTargets | 2014 algorithms, among others.
< crisprAllTargets |< crisprAllTargets | Note that the Doench 2016 scores were updated by < crisprAllTargets | the Broad institute in 2017 ("Azimuth" update). As a result, earlier versions of < crisprAllTargets | the track show the old Doench 2016 scores and this version of the track shows new < crisprAllTargets | Doench 2016 scores. Old and new scores are almost identical, they are < crisprAllTargets | correlated to 0.99 and for more than 80% of the guides the difference is below 0.02. < crisprAllTargets | However, for very few guides, the difference can be bigger. In case of doubt, we recommend < crisprAllTargets | the new scores. Crispor.org can display both < crisprAllTargets | scores and many more with the "Show all scores" link.
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Positional data can be explored interactively with the < crisprAllTargets | Table < crisprAllTargets | Browser or the Data Integrator. < crisprAllTargets | For small programmatic positional queries, the track can be accessed using our < crisprAllTargets | REST API. For genome-wide data or < crisprAllTargets | automated analysis, CRISPR genome annotations can be downloaded from < crisprAllTargets | our download server < crisprAllTargets | as a bigBedFile.
< crisprAllTargets |< crisprAllTargets | The files for this track are called crispr.bb, which lists positions and < crisprAllTargets | scores, and crisprDetails.tab, which has information about off-target matches. Individual < crisprAllTargets | regions or whole genome annotations can be obtained using our tool bigBedToBed, < crisprAllTargets | which can be compiled from the source code or downloaded as a pre-compiled < crisprAllTargets | binary for your system. Instructions for downloading source code and binaries can be found < crisprAllTargets | here. The tool < crisprAllTargets | can also be used to obtain only features within a given range, e.g.
< crisprAllTargets |< crisprAllTargets | bigBedToBed < crisprAllTargets | http://hgdownload.soe.ucsc.edu/gbdb/canFam5/crisprAllTargets/crispr.bb -chrom=chr21 < crisprAllTargets | -start=0 -end=1000000 stdout
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Track created by Maximilian Haeussler, with helpful input < crisprAllTargets | from Jean-Paul Concordet (MNHN Paris) and Alberto Stolfi (NYU). < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Haeussler M, Schönig K, Eckert H, Eschstruth A, Mianné J, Renaud JB, Schneider-Maunoury S, < crisprAllTargets | Shkumatava A, Teboul L, Kent J et al. < crisprAllTargets | Evaluation of off-target and on-target scoring algorithms and integration into the < crisprAllTargets | guide RNA selection tool CRISPOR. < crisprAllTargets | Genome Biol. 2016 Jul 5;17(1):148. < crisprAllTargets | PMID: 27380939; PMC: PMC4934014 < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Bae S, Kweon J, Kim HS, Kim JS. < crisprAllTargets | < crisprAllTargets | Microhomology-based choice of Cas9 nuclease target sites. < crisprAllTargets | Nat Methods. 2014 Jul;11(7):705-6. < crisprAllTargets | PMID: 24972169 < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Doench JG, Fusi N, Sullender M, Hegde M, Vaimberg EW, Donovan KF, Smith I, Tothova Z, Wilen C, < crisprAllTargets | Orchard R et al. < crisprAllTargets | < crisprAllTargets | Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. < crisprAllTargets | Nat Biotechnol. 2016 Feb;34(2):184-91. < crisprAllTargets | PMID: 26780180; PMC: PMC4744125 < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Hsu PD, Scott DA, Weinstein JA, Ran FA, Konermann S, Agarwala V, Li Y, Fine EJ, Wu X, Shalem O < crisprAllTargets | et al. < crisprAllTargets | < crisprAllTargets | DNA targeting specificity of RNA-guided Cas9 nucleases. < crisprAllTargets | Nat Biotechnol. 2013 Sep;31(9):827-32. < crisprAllTargets | PMID: 23873081; PMC: PMC3969858 < crisprAllTargets |
< crisprAllTargets | < crisprAllTargets |< crisprAllTargets | Moreno-Mateos MA, Vejnar CE, Beaudoin JD, Fernandez JP, Mis EK, Khokha MK, Giraldez AJ. < crisprAllTargets | < crisprAllTargets | CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. < crisprAllTargets | Nat Methods. 2015 Oct;12(10):982-8. < crisprAllTargets | PMID: 26322839; PMC: PMC4589495 < crisprAllTargets |
< crisprAllTargets | 3701,3706c3487,3490 < uniprot | Features in the "UniProt Modifications" (modified residues) track are drawn in < uniprot | light green. Disulfide bonds are shown in < uniprot | dark grey. Topological domains < uniprot | in maroon and zinc finger regions in < uniprot | olive green. < uniprot | --- > uniprot | In the "UniProt Modifications" track, lipoification sites are highlighted in > uniprot | dark blue, glycosylation sites in > uniprot | dark green, and phosphorylation in > uniprot | light green.