CRISPRon(v1.0): CRISPR-Cas9 guide efficiency prediction


For the stand-alone version of the CRISPRon software, you can download the training code, evaluation code and models. The software is released under the the GNU Affero General Public License version 3 or later.

Instructions for installing and running the software is included in the README file also available with the downloaded software.

Citing CRISPRon

If you are using the results of the CRISPRon in your publication, please cite:

Enhancing CRISPR-Cas9 gRNA efficiency prediction by data integration and deep learning

Xiang X, Corsi GI, Anthon C, Qu K, Pan X, Liang X, Han P, Dong Z, Liu L, Zhong J, Ma T, Wang J, Zhang X, Jiang H, Xu F, Liu X, Xu X, Wang J, Yang H, Bolund L, Church GM, Lin L, Gorodkin J*, Luo Y* Nature Communications 2021, 12(1)
[ Paper | Webserver | Software ]

CRISPRon is trained on data reported in the publication above and in a 2019 paper from Kim, please cite that as well.

SpCas9 activity prediction by DeepSpCas9, a deep learning-based model with high generalization performance

Kim HK, Kim Y, Lee S, Min S, Bae JY, Choi JW, Park J, Jung D, Yoon S, Kim HH* Sci Adv. 2019 Nov 6;5(11):eaax9249. eCollection 2019 Nov
[ PubMed | Paper ]

Finally, if you use the interaction energies reported by the CRISPRoff tool, please cite:

CRISPR-Cas9 off-targeting assessment with nucleic acid duplex energy parameters

Alkan F, Wenzel A, Anthon C, Havgaard JH, Gorodkin J* Genome Biol. 2018 Oct 26;19(1):177
[ PubMed | Paper | Webserver | Software ]


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