Design highly efficient CRISPR sgRNAs with our AI-powered platform. We combine deep learning-based on-target efficiency prediction with comprehensive off-target analysis for precision genome editing in research and therapeutic applications.
Traditional sgRNA design relies on simple scoring rules and limited experimental data. Our AI platform leverages millions of CRISPR activity measurements to predict sgRNA performance with unprecedented accuracy.
Our deep learning models trained on diverse cell types and organisms achieve >90% correlation with experimental validation for on-target efficiency.
Genome-wide screening identifies potential off-target sites with mismatch tolerance scoring. Our models predict cleavage frequencies to select the safest guides.
Optimized design for SpCas9, Cas12a, Cas12j, eSpCas9, HiFi Cas9, Cas9-NG, and emerging Cas systems. Species-specific models available.
From sgRNA design to cloning and experimental validation. We provide end-to-end CRISPR services including plasmid construction and NGS verification.
From target specification to validated sgRNAs, our AI-powered platform covers the complete workflow.
Deep learning models trained on millions of sgRNA activity measurements predict cutting efficiency with high accuracy across diverse cell types and organisms.
Comprehensive genome-wide screening for potential off-target sites with mismatch and indel tolerance scoring. Identify safest sgRNA candidates for therapeutic applications.
Specialized models for different Cas nucleases and variants. Optimize PAM requirements, spacer length, and secondary structure for maximum editing efficiency.
Streamlined process from target gene to validated sgRNA sequences.
Define target gene, desired edit type, and organism. Specify Cas protein and delivery method preferences.
Our deep learning models screen all potential target sites for efficiency and specificity scores.
Comprehensive genome-wide analysis identifies off-target risks and ranks sgRNA candidates.
Final sgRNA sequences synthesized and cloned. Optional experimental validation services available.
Comprehensive CRISPR sgRNA design and validation solutions for your research.
AI-optimized sgRNA design for single gene targets with efficiency ranking and off-target report.
Optimized designs for simultaneous editing of multiple targets with combinatorial efficiency analysis.
High-fidelity sgRNA design for therapeutic applications with comprehensive safety profiling and GMP-ready documentation.
Our sgRNA design services support diverse genome editing applications across research and clinical development.
High-throughput sgRNA libraries for genome-wide knockout screens and pathway discovery. Optimized for CRISPR pooled screens with consistent coverage.
Clinical-grade sgRNA design for gene therapy candidates. Safety-focused design with comprehensive off-target analysis for IND-enabling studies.
Plant-optimized sgRNA design supporting crop improvement programs. Species-specific models for major agricultural species available.
Specialized sgRNA design for CRISPR base editors (CBE, ABE). PAM requirement optimization and spacer design for maximum editing precision.
dCas9-based sgRNA design for epigenetic modifiers. Optimized targeting for CRISPRa/CRISPRi applications with enhancer mapping support.
Microbial genome editing with bacterial-optimized sgRNA design. High-efficiency knockout and knock-in strategies for industrial strains.
Our platform builds upon peer-reviewed research in AI-driven CRISPR sgRNA design and deep learning for genome editing.
Baisya, D., Ramesh, A., Schwartz, C., Lonardi, S., & Wheeldon, I. (2022). Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and Cas12a guides in Yarrowia lipolytica. Nature Communications, 13(1), 922.
Nature Communications | PubMed: PMID: 35177617Kim, H. K., Min, S., Song, M., Jung, S., Choi, J. W., Kim, Y., Lee, S., Yoon, S., & Kim, H. (2018). Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nature Biotechnology, 36(3), 239-241.
Nature Biotechnology | DOI: 10.1038/nbt.4061Zhang, G., Luo, Y., Dai, X., & Dai, Z. (2023). Benchmarking deep learning methods for predicting CRISPR/Cas9 sgRNA on- and off-target activities. Briefings in Bioinformatics, 24(6), bbad333.
Briefings in Bioinformatics | DOI: 10.1093/bib/bbad333Wang, D., Zhang, C., Wang, B., Li, B., Wang, Q., Liu, D., ... & Gao, Z. (2019). Optimized CRISPR guide RNA design for two high-fidelity SpCas9 variants using deep learning. Nature Communications, 10(1), 4284.
Nature Communications | DOI: 10.1038/s41467-019-12381-5Common questions about our CRISPR sgRNA design services.
We support design for SpCas9, Cas12a (Cpf1), Cas12j, Cas9 variants (eSpCas9, HiFi Cas9, Cas9-NG), and emerging Cas systems. Our platform models are trained on species-specific and cell-type-specific datasets for optimal performance.
Our deep learning models achieve >90% correlation with experimental validation across multiple cell types and organisms. We use ensemble models trained on millions of sgRNA activity measurements to ensure robust predictions.
We perform genome-wide off-target screening considering mismatch tolerance, insertion/deletion patterns, and epigenetic context. Our models predict off-target cleavage frequencies with high sensitivity, helping you select the safest sgRNAs for your application.
Yes. We can train organism-specific models for non-standard species by integrating genomic data and limited experimental validation. The Enginoma platform supports bacterial, fungal, plant, and mammalian systems.
Yes. We offer complete sgRNA synthesis, cloning into expression vectors, and experimental validation including T7E1 assay, surveyor assay, and next-generation sequencing verification.
Contact our team to discuss your CRISPR project requirements. We'll provide customized sgRNA recommendations for your targets.
Tell us about your project and we'll get back within 24 hours.
