This track shows regions of the genome within 200bp of transcribed regions and DNA sequences targetable by CRISPR RNA guides using the Cas9 enzyme from S. pyogenes (PAM: NGG). CRISPR target sites were annotated with predicted specificity (off-target effects) and predicted efficiency (on-target cleavage) by various algorithms through the tool crispor.org
.The track "CRISPR Regions" shows the regions of the genome where target sites were analyzed, i.e. within 200bp of transcribed regions as annotated by Ensembl (=Gencode Comprehensive) transcript models.
The track "CRISPR Targets" shows the target sites in these regions. They 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 is cleavage at other locations of the genome (off-target effects). Efficiency is the frequency of cleavage at the target site (on-target efficiency).
Shades of grey 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:
low predicted cleavage: Doench/Fusi 2016 Efficiency percentile <= 30 | |
medium predicted cleavage: Doench/Fusi 2016 Efficiency percentile > 30 and < 60 | |
high predicted cleavage: Doench/Fusi 2016 Efficiency > 60 |
Mouse-over a target site to show predicted specificity and efficiency scores:
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 on this page are sorted by the CFD score (Doench et al. 2016). The higher the CFD score, the more likely is off-target cleavage at a site. The large majority of predicted off-targets with CFD scores < 0.02 were false-positives.
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.
Similarily, 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 is it 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 are evaluated on their own training data here. Especially for the Moreno-Mateos score, the results are too optimistic, due to over-fitting. 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.
Exons as predicted by Ensembl Gene models were used, extended by 200 basepairs on each side, searched 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 crispor.org. 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.
The raw data can be explored interactively with the Table Browser. For automated analysis, the genome annotation is stored in a bigBed file that can be downloaded from our download server. The files for this track are called crispr.bb and crisprDetails.tab. Individual regions or the whole genome annotation can be obtained using our tool bigBedToBed which can be compiled from the source code or downloaded as a precompiled 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 http://hgdownload.cse.ucsc.edu/gbdb/hg19/crispr/crispr.bb -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 Maximilian; Schönig Kai; Eckert Hélène; Eschstruth Alexis; Mianné Joffrey; Renaud Jean-Baptiste; Schneider-Maunoury Sylvie; Shkumatava Alena; Teboul Lydia; Kent Jim; Joly Jean-Stephane; Concordet Jean-Paul. Evaluation of off-target and on-target scoring algorithms and integration into the guide RNA selection tool CRISPOR.. Genome biology. 2016 171:148. PMID: 27380939; PMC: PMC4934014
Bae Sangsu; Kweon Jiyeon; Kim Heon Seok; Kim Jin-Soo. Microhomology-based choice of Cas9 nuclease target sites.. Nature methods. 2014 117:705-6. PMID: 24972169;
Doench John G; Fusi Nicolo; Sullender Meagan; Hegde Mudra; Vaimberg Emma W; Donovan Katherine F; Smith Ian; Tothova Zuzana; Wilen Craig; Orchard Robert; Virgin Herbert W; Listgarten Jennifer; Root David E. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9.. Nature biotechnology. 2016 342:184-91. PMID: 26780180; PMC: PMC4744125
Moreno-Mateos Miguel A; Vejnar Charles E; Beaudoin Jean-Denis; Fernandez Juan P; Mis Emily K; Khokha Mustafa K; Giraldez Antonio J. CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo.. Nature methods. 2016 1210:982-8. PMID: 26322839; PMC: PMC4589495
Hsu Patrick D; Scott David A; Weinstein Joshua A; Ran F Ann; Konermann Silvana; Agarwala Vineeta; Li Yinqing; Fine Eli J; Wu Xuebing; Shalem Ophir; Cradick Thomas J; Marraffini Luciano A; Bao Gang; Zhang Feng. DNA targeting specificity of RNA-guided Cas9 nucleases.. Nature biotechnology. 2014 319:827-32. PMID: 23873081; PMC: PMC3969858