No Reagents for the In Silico Phase
Confidence-Scored Results
Publication-Ready Figures
Validation Planned With Partners
Response Within One Business Day
Service Overview
ICDMO's AI-Driven Gene Editing & Therapy Design service combines current deep learning models for guide RNA activity scoring, genome-wide off-target risk stratification against reference genome databases, and delivery-selection analysis to generate complete, ready-to-execute gene editing strategies. The platform supports traditional Cas9/Cas12a nucleases, high-fidelity variants (eSpCas9, HiFi Cas9), cytidine/adenine base editors (CBE/ABE), prime editors (PE2, PE3, PE3b), and transcriptional activators/repressors (CRISPRa/i) — covering every major research and clinical editing modality in a single AI workflow.
Technical Advantages
✓
Comprehensive gRNA Design & Scoring
Every candidate guide is scored for on-target activity, genome-wide off-target risk, PAM accessibility and sequence composition, and the ranked shortlist is delivered with the reasoning behind the order.
✓
Off-Target Risk Stratification
Genome-wide off-target site prediction with mismatch tolerance up to 5 bp, RNA:DNA loop modeling, and chromatin accessibility weighting (ATAC-seq integration) for therapeutic-grade safety profiling.
✓
HDR Template & Donor Design
Automated HDR donor design with optimized homology arm lengths, silent mutations to prevent re-cutting, codon optimization for the target organism, and ssODN vs. dsDNA template recommendation.
✓
Base & Prime Editing Strategy
AI selects optimal CBE/ABE editor variant and PE pegRNA sequence, models editing window, bystander edit risk, and RT template length — delivering complete editing reagent specifications.
✓
Delivery System Optimization
Algorithm-guided selection of delivery modality (plasmid, RNP, LNP, AAV, lentivirus) optimized for cell type, payload size, and safety profile — with electroporation condition recommendations.
✓
Experimental Validation Integration
AI strategy directly feeds into ICDMO's wet-lab CRISPR services — gene knockout, knockin, cell line generation, base editing, and NGS editing efficiency measurement.
Supported Gene Editing Modalities
CRISPR-Cas9 & Cas12a Knockout / Knockin
▸SpCas9, SaCas9, LbCas12a, AsCas12a gRNA design with PAM selection guidance
▸High-fidelity variants: eSpCas9 1.1, HiFi Cas9, HypaCas9 — recommended for off-target-sensitive applications
▸Large insert knockin (≥5 kb): AAV-donor HDR design, piggybac transposon strategy, HITI for non-dividing cells
▸Multiplexed editing: up to 6 simultaneous targets, orthogonal Cas protein pair selection
Base Editing (CBE & ABE)
▸Cytosine base editors (BE4max, AncBE4max): C→T conversion at disease-relevant SNPs
▸Adenine base editors (ABE8e, ABEmax): A→G conversion with minimal RNA off-targets
▸Bystander edit prediction and editor variant selection to maximize precision
▸Therapeutic SNP correction: hemoglobinopathies, metabolic disorders, neurological disease-relevant loci
Prime Editing
▸PE2, PE3, PE3b, PEmax pegRNA design with RT template and PBS optimization
▸EpegRNA (structured pegRNA) recommendations to enhance prime editing efficiency
▸All mutation types: substitutions, small insertions (≤44 nt), and small deletions (≤80 bp)
▸Cell type-specific prime editing efficiency benchmarking from published datasets
CRISPRa / CRISPRi & Epigenome Editing
▸dCas9-VPR, dCas9-KRAB, dCas9-p300 gRNA design for transcriptional activation or repression
▸Promoter and enhancer targeting for gene regulation studies
▸Epigenome editors: dCas9-DNMT3A (methylation), dCas9-TET1 (demethylation)
▸Multi-gene perturbation design for pathway analysis and CRISPR screen guide library design
What We Compute for Every Guide
1On-target activity score for each candidate guide, computed with models matched to the nuclease and delivery format, and reported as a ranking rather than as a predicted editing percentage
2Genome-wide off-target site list with mismatch and bulge tolerance, annotated by genomic context so coding and regulatory hits are separated from intergenic ones
3Predicted repair outcome distribution per guide, including frameshift probability and the expected indel spectrum
4Editing window and bystander position analysis for base editing strategies, computed per editor chemistry
5Efficiency and secondary-structure assessment for prime editing guides, including the stabilising elements considered
6Delivery recommendation drawn from published cell-type and format performance data, with the sources cited
Application Areas
1Disease Modeling: Generate knockout, knockin, and point-mutation cell line models for drug target validation in oncology, rare disease, and neuroscience
2Gene Therapy Development: Design therapeutic gene correction strategies for clinical programs — IND-enabling precision editing with full off-target risk documentation
3Functional Genomics: Design CRISPR screening libraries (genome-wide, pathway, essential gene sets) with optimized guide density and coverage
4Agricultural Biotech: Plant gene editing (rice, maize, soybean) with species-specific PAM and codon optimization
5Ex Vivo Cell Therapy: CAR-T, TCR-T, NK cell engineering — multi-gene knockout + transgene knockin strategy for clinical-grade editing
What We Need From You
1Target gene, locus or sequence with the reference genome build and species specified
2The editing outcome required: knockout, knock-in, base edit, prime edit or activation and repression
3The delivery system and cell type, since these constrain guide chemistry, length and nuclease choice
4Any existing guide designs or editing data, including guides that have already failed
5Constraints on off-target tolerance, which differ sharply between research and therapeutic contexts
How the Analysis Is Performed
1Locus analysis covering exon structure, functional domains, isoforms and regulatory elements
2Guide enumeration across compatible nucleases with PAM availability and accessibility assessment
3On-target activity prediction using established scoring models for the nuclease in question
4Genome-wide off-target search with mismatch and bulge tolerance, filtered by genomic context
5Repair outcome and editing-window modelling for base and prime editing strategies
6Donor design with homology arm optimisation and silent mutation planning where re-cutting must be prevented
What You Receive
1Ranked guide designs with on-target efficiency prediction and the reasoning for the ranking
2Genome-wide off-target assessment per guide, with mismatch and bulge tolerance stated
3Repair outcome prediction for the leading guides, including expected indel spectra where applicable
4Donor or template design for knock-in and precise editing strategies
5Validation plan: which amplicons to sequence, which off-target sites to check and by what method
Interpretation and Limitations
Guide efficiency prediction correlates with measured activity but does not replace testing; observed editing rates vary substantially with cell type, delivery method and chromatin state. Computational off-target prediction covers sequence-similar sites and will not capture all genomic off-target activity, which requires empirical methods such as GUIDE-seq or CIRCLE-seq. Designs are research-use only and are not a substitute for the safety assessment required in a therapeutic programme.
Questions We Are Asked Before Scoping
How many guides should we test?
▸Three to five per target is the usual practical choice, because predicted efficiency and measured efficiency correlate only moderately. We rank the candidates and explain what distinguishes them.
Can you guarantee no off-target editing?
▸No. We can minimise predicted off-target risk and rank guides on it, but empirical off-target profiling is required before any claim about specificity, particularly for therapeutic applications.
Do you design knock-in donors as well?
▸Yes, including homology arm length, silent mutations to prevent re-cutting and the sequencing strategy needed to confirm correct integration.
Technical Workflow
1
Target & Goal Definition
Provide target gene(s), species/cell type, editing goal (KO, KI, correction, regulation), and any safety requirements. ICDMO retrieves latest genome assembly and annotation.
2
AI Guide RNA Design & Scoring
Multi-algorithm ensemble scores all candidate guides for on-target efficiency, GC content, seed region quality, PAM accessibility, and predicted off-target risk. Top 3–5 guides selected.
3
Off-Target Risk Assessment
Genome-wide off-target site prediction (up to 5 mismatches + bulges) with chromatin accessibility weighting. Full risk report with predicted cleavage probability at each site.
4
Donor Template / pegRNA Design
For HDR, base editing, or prime editing: automated donor/pegRNA design with homology arm optimization, silent blocking mutations, and codon optimization for the target species.
5
Delivery Strategy Recommendation
Optimal delivery modality selected (RNP, plasmid, LNP, AAV, lentivirus) based on cell type, transfection efficiency data, and payload size. Electroporation conditions included where applicable.
6
Strategy Report & Reagent Specifications
Comprehensive report: gRNA sequences, donor DNA or pegRNA specifications, off-target report, delivery protocol, NGS analysis plan, and optional link to ICDMO wet-lab execution services.
Service Timeline & Deliverables
Is This the Right Service for You?
Choose this when
Guides must be designed and ranked with genome-wide off-target assessment
A knock-in, base edit or prime edit strategy needs designing end to end
A previous editing attempt failed and the design needs diagnosing
Consider another service when
A stable edited cell line is the deliverable
Stable Knockout Cell Line Design Knockdown rather than editing suits the biology
Gene Knockdown Cell Line Design What Goes Wrong, and How We Handle It
These are the failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.
Predicted efficiency treated as expected editing rate
Scores are delivered as a ranking; measured rates depend on cell type, delivery and chromatin state, and that is stated.
Off-target risk considered only at sequence level
Genomic context filtering prioritises coding and regulatory sites, and empirical profiling is named as the confirmatory step.
Donor designed without preventing re-cutting
Silent PAM or seed changes are built into every knock-in donor.
A Worked Example
Diagnosing a knock-in that never worked
The situation
Three attempts at a point-mutation knock-in had produced only indels.
What the work involved
The guide-to-edit distance was measured, the donor design was reviewed for re-cutting protection, and arm lengths were checked against the delivery format in use.
What came back
The cut site sat 34 bases from the intended edit and the donor lacked silent protection. Redesigned guides and a donor with blocking mutations were delivered with a revised screening strategy.
Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.
Service Process
1
Online Consultation
2
Solution Matching
3
Service Contract
4
AI Computation
5
Project Report
Note: All services are for research use only and not intended for diagnostic or therapeutic purposes.
Get a Custom Quote
Our scientific team responds within 24 hours with a detailed technical proposal and pricing tailored to your research goals.
Contact Us Online ConsultationStandard Deliverables
✓Detailed analysis report (PDF)
✓Raw data files & processed outputs
✓High-resolution publication figures
✓Interaction scoring tables (Excel)
✓Project summary presentation
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