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We present Crackling, a new method for whole-genome identification of suitable CRISPR targets. The method maximises the efficiency of the guides by combining the results of multiple scoring approaches. On experimental data, the set of guides it selects are better than those produced by existing tools. The method also incorporates a new approach for faster off-target scoring, based on Inverted Signature Slice Lists (ISSL). This approach provides a gain of an order of magnitude in speed, while preserving the same level of accuracy.
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> The design of CRISPR-Cas9 guide RNAs is not trivial, and is a computationally demanding task. Design tools need to identify target sequences that will maximise the likelihood of obtaining the desired cut, whilst minimising off-target risk. There is a need for a tool that can meet both objectives while remaining practical to use on large genomes.
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> Here, we present Crackling, a new method that is more suitable for meeting these objectives. We test its performance on 12 genomes and on data from validation studies. Crackling maximises guide efficiency by combining multiple scoring approaches. On experimental data, the guides it selects are better than those selected by others. It also incorporates Inverted Signature Slice Lists (ISSL) for faster off-target scoring. ISSL provides a gain of an order of magnitude in speed compared to other popular tools, such as Cas-OFFinder, Crisflash and FlashFry, while preserving the same level of accuracy. Overall, this makes Crackling a faster and better method to design guide RNAs at scale.
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> Crackling is available at https://github.com/bmds-lab/Crackling under the Berkeley Software Distribution (BSD) 3-Clause license.
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## Dependencies
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- Python v3.6+
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## Installation
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## Installation & Usage
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1. Clone or [download](https://github.com/bmds-lab/Crackling/archive/master.zip) the source.
5. Compile the off-target indexing and scoring functions. An index of off-targets is required: to prepare this, readin the *Utilities* section (*Off-target Indexing*).
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```bash
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make
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```
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5. Create a Bowtie2 index
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The Bowtie2 manual can be found [here](http://bowtie-bio.sourceforge.net/bowtie2/manual.shtml).
We provided a pre-trained model, however, dependent on your environment (Python and package versions), you may need to retrain it, using the CLI command `trainModel`. All arguments to this command are optional, as the utility will compute the default values for you.
Ben Langmead and Steven L Salzberg. Fast gapped-read alignment with Bowtie 2. Nature Methods, 9(4):357, 2012.
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Ben Langmead and Steven L Salzberg. Fast gapped-read alignment with Bowtie2. Nature Methods, 9(4):357, 2012.
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Bradford, J., & Perrin, D. (2019). A benchmark of computational CRISPR-Cas9 guide design methods. PLoS computational biology, 15(8), e1007274.
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Bradford, J., & Perrin, D. (2019). Improving CRISPR guide design with consensus approaches. BMC genomics, 20(9), 931.
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Chari, R., Yeo, N. C., Chavez, A., & Church, G. M. (2017). sgRNA Scorer 2.0: a species-independent model to predict CRISPR/Cas9 activity. ACS synthetic biology, 6(5), 902-904.
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Lorenz, R., Bernhart, S. H., Zu Siederdissen, C. H., Tafer, H., Flamm, C., Stadler, P. F., & Hofacker, I. L. (2011). ViennaRNA Package 2.0. *Algorithms for molecular biology*, *6*(1), 1-14.
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Montague, T. G., Cruz, J. M., Gagnon, J. A., Church, G. M., & Valen, E. (2014). CHOPCHOP: a CRISPR/Cas9 and TALEN web tool for genome editing. Nucleic acids research, 42(W1), W401-W407.
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Sunagawa, G. A., Sumiyama, K., Ukai-Tadenuma, M., Perrin, D., Fujishima, H., Ukai, H., ... & Shimizu, Y. (2016). Mammalian reverse genetics without crossing reveals Nr3a as a short-sleeper gene. Cell reports, 14(3), 662-677.
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Sunagawa, G. A., Sumiyama, K., Ukai-Tadenuma, M., Perrin, D., Fujishima, H., Ukai, H., ... & Shimizu, Y. (2016). Mammalian reverse genetics without crossing reveals Nr3a as a short-sleeper gene. Cell reports, 14(3), 662-677.
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