Download | DeepGE

DownloadEvery model listed below can be downloaded and run on your own machine, and the data behind the screens is offered the same way. A model archive holds the model exactly as this website runs it, together with a README describing how to run it, an environment file, and the agreement it is offered under; a data archive holds the tables its web server reads, with a README describing them.The website and the downloads are offered on different terms. Please read both.ModelsCRISPR–Cas Variant Editing

DeepSpCas9variants — Download Model
Deep learning-based CRISPR–Cas9 (SpCas9) and variant guide RNA design and efficiency prediction with optimized scaffold, published in Kim et al. Nature Biotechnology (2020).

DeepSpCas9 — Download Model
Deep learning-based CRISPR–Cas9 (SpCas9) guide RNA design and efficiency prediction tool with conventional scaffold, published in Kim et al. Science Advances (2019).

DeepCpf1 — Download Model
Deep learning-based CRISPR–Cas12a (AsCas12a, Cpf1, or AsCpf1) guide RNA design and efficiency prediction, published in Kim et al. Nature Biotechnology (2018).

DeepSmallCas9 — Download Model
Deep learning-based guide RNA design and efficiency prediction for 17 small Cas9 orthologs and variants, published in Seo et al. Nature Methods (2023).

DeepSpCas9-NG — Download Model
Deep learning-based guide RNA design and efficiency prediction of SpCas9-NG efficiency with conventional scaffold, published in Kim et al. Nature Biomedical Engineering (2020).

DeepxCas9 — Download Model
Deep learning-based guide RNA design and efficiency prediction of xCas9 efficiency with conventional scaffold, published in Kim et al. Nature Biomedical Engineering (2020).

DeepSniper — Download Model
Deep learning-based guide RNA design and efficiency prediction for Sniper2L and Sniper1 (Sniper-Cas9), published in Kim et al. Nature Chemical Biology (2023).

CINDEL — Download Model
Prediction of CRISPR–Cas12a/Cpf1 (AsCpf1) guide activity from sequence context, published in Kim et al. Nature Methods (2017).

DeepBE — Download Model
Deep learning-based base editor and Cas9 variant guide RNA design and efficiency prediction tool, published in Kim et al. Nature Biotechnology (2024).

Base Editing

DeepBE — Download Model
Deep learning-based base editor and Cas9 variant guide RNA design and efficiency prediction tool, published in Kim et al. Nature Biotechnology (2024).

DeepBaseEditor — Download Model
Deep learning-based guide RNA design and prediction of Adenine Base Editor (ABE) and Cytosine Base Editor (CBE) activities, published in Song et al. Nature Biotechnology (2020).

Prime Editing

DeepPrime — Download Model
Deep learning-based model predicting prime editing efficiencies for all possible pegRNAs of a given target sequence, published in Yu et al. Cell (2023).

SynDesign — Download Model
Web-based prime editing guide RNA design and evaluation tool with saturation genome editing (SGE) capabilities, published in Park et al. Nucleic Acids Research (2024).

DeepPE — Download Model
Deep learning-based prime editing guide RNA design and efficiency prediction, published in Kim et al. Nature Biotechnology (2021).

PEselector — Download Model
Machine learning-based tool for selecting and predicting hyPE2 prime editors from Target, Reverse transcription template (RTT), and Primer binding site (PBS) sequences, published in Song et al. Nature Communications (2021).

Saturation Genome Editing (SGE)

DataThese web servers report a published screen rather than running a model. The archives hold the tables the web server reads, and the same terms apply. Please cite the study any figure or table of yours draws on; each archive's README names it.

BEvariants — Download Data
Evaluations of functional effects of cancer-related transition mutations using base editors and single guide RNA (sgRNA) libraries, published in Kim et al. Nature Biotechnology (2022).

EGFRvariants — Download Data
Resistance profiling of EGFR kinase domain variants in EGFR-mutant NSCLC cells, screened by prime editing, published in Kim et al. Nature Biotechnology (2025) and Lee et al. npj Precision Oncology (2026).

LicensesThe websiteThis website is free and open to all users and there is no login requirement.A downloaded modelA model you download is not covered by the paragraph above. Each archive carries a LICENSE file, and that agreement governs everything in the archive — the code, the trained weights and the data beside them. The same agreement is published as LICENSE.txt, so it can be read in full before downloading. In summary, and the LICENSE itself governs:

Academic, non-commercial research only, by the individual who obtained it. Commercial or clinical use requires a purchased license.

No serving and no redistribution without prior written permission. You may not host a downloaded model, expose it through a website or an interface of your own, run it on behalf of anyone else, or pass any part of it on.

Anything the agreement does not expressly permit requires prior written permission.

No use in developing other models: not for training, fine-tuning, distilling, benchmarking, or building a dataset for any other model.

Not a medical device. Reference and educational use only, and not for clinical diagnosis or decision-making.

Using this website is not permission to redistribute a model, and downloading a model is not required to use this website. Requests for permission, and commercial licensing: ssy@liquidcrispr.com, eastwise@yuhs.ac, aquamd@gmail.com.QuestionsA web server that is not listed above offers no model download. If you need a model that is not listed, or terms other than the ones above, please write to ssy@liquidcrispr.com, eastwise@yuhs.ac, aquamd@gmail.com rather than assuming either way.

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