242cadb9e7b21f08168f2a2e347d60069372a9c4 dschmelt Mon Jul 1 16:09:35 2019 -0700 Correcting typos in crisprAll.html page #23514 diff --git src/hg/makeDb/trackDb/crisprAll.html src/hg/makeDb/trackDb/crisprAll.html index f342f79..9a26a15 100644 --- src/hg/makeDb/trackDb/crisprAll.html +++ src/hg/makeDb/trackDb/crisprAll.html @@ -61,31 +61,31 @@ score</a> 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.</li> </ol> </p> <p>Click onto features to show all scores and predicted off-targets with up to four mismatches. The Out-of-Frame score by <a href="#References">Bae et al. 2014</a> 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.<p> <p>Off-target sites are sorted by the CFD score (<a href="https://www.nature.com/articles/nbt.3437" target="_blank">Doench et al. 2016</a>). 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 availble when +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 <a href="#References">Haeussler et al. 2016</a>, 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.</p> <h2>Methods</h2> <h3>Relationship between predictions and experimental data</h3> <p> 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 <a href="#References">Haeussler @@ -102,31 +102,31 @@ inefficient guides drops with increasing efficiency scores: </p> <img src="../images/crisprFig_effScores.png"> <p>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 <a href="#References">Haeussler et al. 2016</a>. At the time of writing, there is no independent dataset available yet to determine the Moreno-Mateos accuracy for each score percentile range.</p> <h3>Track methods</h3> <p> -The entire $organism ($db) genome was for the -NGG motif. Flanking 20mer +The entire $organism ($db) 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 crispor.org. Non-unique guide sequences were skipped. Flanking sequences were extracted from the genome and input for Crispor efficiency scoring, available from the <a href="http://crispor.tefor.net/downloads/">Crispor downloads page</a>, 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. Crispor.org can display both scores and many more with the "Show all scores" link.</p>