Available Web Servers

Web server

CRISPR–Cas variants / orthologs covered

Scaffold

gRNA architecture

Key predictions and features

Typical input

Key reference

DeepSpCas9variants

SpCas9 and 8 other variants: SpCas9-NG, VRQR variant, Sniper-Cas9, xCas9, eSpCas9(1.1), SpCas9-HF1, HypaCas9, evoCas9

Optimized

(G/g)N19 and tRNA-N20

Default starting point: Designs guide RNAs and predicts efficiency across 9 editors; fidelity ranking: evoCas9 ≫ HypaCas9 ≥ SpCas9-HF1 ≈ eSpCas9(1.1) > xCas9 > Sniper-Cas9 > SpCas9

FASTA (≥30 bp), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID

Kim et al. Nature Biotechnology (2020)

DeepCas9variants (via DeepBE)

SpCas9 and 8 other variants — SpCas9-NG, VRQR variant, SpCas9-NRRH, SpCas9-NRTH, SpCas9-NRCH, SpG, SpRY, Sc++

Optimized

(G/g)N19

Reached through Comparison Mode inside DeepBE; use when you need variants beyond DeepSpCas9variants

FASTA (≥30 bp), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID

Kim et al. Nature Biotechnology (2024)

DeepSmallCas9

17 compact orthologs/variants: sRGN3.1; SlugCas9 and SlugCas9-HF; SaCas9, SaCas9-KKH, SaCas9-HF, SaCas9-KKH-HF, eSaCas9, efSaCas9; SauriCas9 and SauriCas9-KKH; Sa-SlugCas9; St1Cas9; Nm1Cas9; Nm2Cas9; CjCas9 and enCjCas9

Variant-specific

(G/g)N19-23 (variant-specific)

Designs guide RNAs and predicts efficiency for small nucleases; includes whole-genome off-target screening (bulge screening for sRGN3.1 and SlugCas9); recommended under strict size limits such as single-AAV packaging

FASTA (≥32–38 bp, depending on enzyme), gene symbol (human or mouse), NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID

Seo et al. Nature Methods (2023)

DeepCpf1

CRISPR–Cas12a (AsCas12a / Cpf1 / AsCpf1)

N/A (Cas12a)

Two models — DeepCpf1 (uses chromatin accessibility / DNase-seq) and Seq-DeepCpf1 (sequence only); the TTTV PAM of Cas12a enables editing in A/T-rich regions

FASTA (≥34 bp), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID

Kim et al. Nature Biotechnology (2018)

DeepSpCas9

SpCas9

Conventional

(G/g)N19

Efficiency prediction and guide design at endogenous sites

FASTA (≥30 bp), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID

Kim et al. Science Advances (2019)

DeepSpCas9-NG

SpCas9-NG

Conventional

(G/g)N19

Efficiency prediction and guide design for the relaxed-PAM SpCas9-NG

FASTA (≥30 bp), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID

Kim et al. Nature Biomedical Engineering (2020)

DeepxCas9

xCas9

Conventional

(G/g)N19

Efficiency prediction and guide design for the broad-PAM xCas9

FASTA (≥30 bp), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID

Kim et al. Nature Biomedical Engineering (2020)

DeepSniper

Sniper2L and Sniper1 (Sniper-Cas9)

Optimized

(G/g)N19 and tRNA-N20

Predicts both on-target and off-target activity of high-fidelity Sniper variants across diverse PAMs

FASTA (≥30 bp), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID

Kim et al. Nature Chemical Biology (2023)

CINDEL

CRISPR–Cas12a / Cpf1 (AsCpf1)

N/A (Cas12a)

Logistic-regression model with Guide Finder, Single Sequence, and Batch Scoring modes (TTTV PAM); for deep-learning accuracy, prefer DeepCpf1

Finder: FASTA (≥27 bp), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID. Batch: exactly 27 bp TTTV per line; Single: ≥27 bp, from which a 27 bp TTTV candidate is selected

Kim et al. Nature Methods (2017)

DeepTrunc-cell

SpCas9-HF1 and SpCas9 (base models); FnCas9 and the variants FnCas9-en1 and FnCas9-en31 (fine-tuned from the SpCas9-HF1 model)

Not specified

Spacers of 16–21 nt starting with G (16–19 nt for SpCas9)

Predicts cellular indel frequencies of matched, mismatched and bulge-containing target–sgRNA pairs; scores a guide you supply and does not design one

Comma-separated pairs of a 35 bp target and an sgRNA, one pair per line, without a header

Unpublished

Web server

Base editors covered

Key predictions and features

Typical input

Key reference

DeepBE

7 deaminase modules — ABE8e(V106W), ABE8.17m+V106W, YE1-BE4max, SsAPOBEC3B, CGBE1, miniCGBE1, APOBEC-nCas9-Ung — each paired with 9 Cas9 variants (SpCas9, SpCas9-NG, VRQR, SpCas9-NRRH, SpCas9-NRTH, SpCas9-NRCH, SpG, SpRY, Sc++), spanning ABE, CBE, and CGBE

Default and most comprehensive: Predicts efficiency and outcome proportions; offers single-editor and comparison modes; also hosts the DeepCas9variants models

Comparison mode: FASTA (≥30 bp; edit direction selected), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID. Single-editor mode: one target sequence (≥30 bp), from which a 30 bp candidate is selected

Kim et al. Nature Biotechnology (2024)

DeepBaseEditor

ABE7.10 (ABE) and BE4 (CBE)

Designs optimized guide RNAs and predicts efficiency plus outcome proportions for the two classic editors

FASTA (≥25 bp for CBE, ≥26 bp for ABE), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID

Song et al. Nature Biotechnology (2020) and Park and Kim Methods in Molecular Biology (2023)

BEvariants

ABE / CBE sgRNA library screen (not an efficiency predictor)

Reports the functional effects of cancer-related transition mutations on lung-cell survival and proliferation, searchable by gene name; for efficiency on arbitrary sequences, use DeepBE

Gene-name lookup

Kim et al. Nature Biotechnology (2022)

Web server

Prime editor systems / models

Key predictions and features

Typical input

Key reference

DeepPrime

Diverse prime editing (PE) systems across multiple cell types

Default for single-target design: Evaluates and ranks all possible pegRNAs for one target by the DeepPrime score

Unedited/edited sequence pair (a 121 bp pair gives the fullest set of candidates; a shorter pair gives fewer), ClinVar ID, or COSMIC ID

Yu et al. Cell (2023)

SynDesign

Built on DeepPrime / DeepPrime-FT

Default for multi-target design and SGE: Automated pegRNA design, evaluation, and library construction with extensive saturation genome editing support

FASTA (≥121 bp recommended), unedited/edited sequence pair (121 bp recommended), gene symbol, RefSeq NM, Ensembl, HGNC, ClinVar, COSMIC, or genomic position

Park et al. Nucleic Acids Research (2024)

DeepPE

PE2 (DeepPE + PE_type + PE_position models)

Predicts PE2 efficiency for 57 pegRNA designs per target (24 + 23 + 10)

FASTA (≥47 bp), gene symbol, NMID, Ensembl ID, HGNC ID, genomic position, unedited/edited sequence pair, ClinVar ID, or COSMIC ID

Kim et al. Nature Biotechnology (2021)

PEselector

hyPE2

Scores hyPE2 prime editors and returns regression and classification scores plus a DeepSpCas9 score

Target / RTT / PBS sequence triplets

Song et al. Nature Communications (2021)

EGFRvariants

EGFR tyrosine kinase domain variants, exons 18–24 (a published screen, not an efficiency predictor)

Reports how each EGFR variant responds to afatinib, dacomitinib or osimertinib on an activating EGFR L858R or exon 19 deletion background, with the variants installed by prime editing; for efficiency on arbitrary targets use DeepPrime or SynDesign

Dataset, activating context and tyrosine kinase inhibitor selection

Kim et al. Nature Biotechnology (2025) and Lee et al. npj Precision Oncology (2026)

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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