Available Web Servers

BEvariants – 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).

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

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

DeepBaseEditor – 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).

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

DeepGE – Comprehensive web server for predicting and designing guide RNAs for CRISPR–Cas9 Variants, Base Editors, and Prime Editors.

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

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

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

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

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

DeepSpCas9-NG – 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).

DeepSpCas9variants – 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).

DeepTrunc-cell – DeepTrunc-cell predicts cellular CRISPR/Cas indel frequencies of matched, mismatched, and bulge-mutated target–sgRNA pairs.

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

EGFRvariants – 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).

PEselector – 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).

SynDesign – 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).

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