Rapid Whole-Genome Identification of High Quality CRISPR Guide RNAs with the Crackling Method

Bradford, Jacob; Chappell, Timothy; Perrin, Dimitri

doi:10.1089/crispr.2021.0102

Cited by 2 publications

(19 citation statements)

References 23 publications

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“…This is higher than the results obtained by Crackling, at the cost of a lower recall. On the complete dataset, Crackling was reported to outperform all the tools it was tested against (10), so these results are encouraging.…”

Section: Resultsmentioning

confidence: 89%

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Leveraging uncertainty quantification to optimise CRISPR guide RNA selection

Schmitz,

Bradford,

Salomone

et al. 2024

Preprint

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CRISPR-based genome editing relies on guide RNA sequences to target specific regions of interest. A large number of methods have been developed to predict how efficient different guides are at inducing indels. As more experimental data becomes available, methods based on machine learning have become more prominent. Here, we explore whether quantifying the uncertainty around these predictions can be used to design better guide selection strategies. We demonstrate how using a deep ensemble approach achieves better performance than utilising a single model. This approach can also provide uncertainty quantification. This allows to design, for the first time, strategies that consider uncertainty in guide RNA selection. These strategies achieve precision over 91% and can identify suitable guides for more than 93% of genes in the mouse genome. Our deep ensemble model is available at https://github.com/bmds-lab/CRISPR_DeepEnsemble.

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Section: Resultsmentioning

confidence: 89%

“…It would also be interesting to evaluate the impact of the size of the ensemble. Methods based on the consensus between multiple approaches can produce good results (10). Combining this consensus philosophy with the ability to estimate uncertainty could lead to new solutions with improved recall.…”

Section: Discussionmentioning

confidence: 99%

Leveraging uncertainty quantification to optimise CRISPR guide RNA selection

Schmitz,

Bradford,

Salomone

et al. 2024

Preprint

Self Cite

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“…In Crackling++, we can set a threshold τ on this off-target to decide whether to accept or reject a candidate gRNA. As this global off-target score depends on the sum of the local scores, it is possible to terminate the calculation as soon as the score is compared to be below τ , as discussed in (8). Here, we report results for τ = 75 (a conservative value which limits the risk of off-target modifications whilst still accepts several guides per gene, see e.g.…”

Section: Resultsmentioning

confidence: 99%

“…All the files required by CRISPR-SE were created as per the published instructions (13). All other tools are used as previously described (8). For our new mask-based implementation, we tested four configurations with different values for x and | C |.…”

Section: Methodsmentioning

confidence: 99%

“…Each tool was benchmarked by averaging the time taken to process 5 unique sets of 10,000 randomly selected guides on genomes of increasing size. The computer used for the benchmark was the same as the one used by (8), a Linux workstation with one 8-core Intel Core i7-5960X (3.0 GHz) CPU, 32 GB RAM, 32 GB allocated swap space, and a Samsung PM87 SSD.…”

Section: Methodsmentioning

confidence: 99%

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Fast and scalable off-target assessment for CRISPR guide RNAs using partial matches

Schmitz,

Bradford,

Salomone

et al. 2024

Preprint

Self Cite

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The design of CRISPR-Cas9 guide RNAs is not trivial. In particular, it is crucial to evaluate the risk of unintended, off-target modifications, but this is computationally expensive. To avoid a brute-force approach where each guide RNA is compared against every possible CRISPR target site in the genome, we previously introduced Crackling, a guide RNA design tool that relies on exact matches over 4bp subsequences to approximate a neighbourhood and accelerate off-target scoring by greatly reducing the search space. While this was faster than other existing tools, it still generates large neighbourhoods. Here, we aim to further reduce the search space by requiring more, now non-contiguous, exact matches. The new implementation, called Crackling++, is benchmarked against our initial approach and other off-target evaluation tools. We show that it provides the fastest way to assess candidate guide RNAs. By using memory-mapped files, it also scales to the largest genomes. Crackling++ is available at https://github.com/bmds-lab/CracklingPlusPlus under the Berkeley Software Distribution (BSD) 3-Clause license.

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Rapid Whole-Genome Identification of High Quality CRISPR Guide RNAs with the Crackling Method

Cited by 2 publications

References 23 publications

Leveraging uncertainty quantification to optimise CRISPR guide RNA selection

Leveraging uncertainty quantification to optimise CRISPR guide RNA selection

Fast and scalable off-target assessment for CRISPR guide RNAs using partial matches

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