Advancing CRISPR-based experiment design to unlock the full potential of precision genome editing.
Model Configuration
Configure and run our AI-powered CRISPR analysis models.Note: This demo currently supports only precalculated configurations and does not allow arbitrary inputs.
Physically Informed CRISPR Guide RNA Design
Our research focuses on developing AI-driven and epigenetically aware design of gRNAs, uncertainty quantification models, and Cas9 sequence-aware prediction systems to minimize off-target effects and unlock CRISPR's full potential as a transformative medical innovation.
Gen AI-Driven Guide RNA Design
Our generative AI-driven approach enables epigenetically aware design of gRNAs for specific genomic targets, taking into account crucial epigenetic descriptors for optimal targeting efficiency.
- Epigenetically aware design system
- High-throughput optimization
- Advanced target specificity
Beyond Conventional CRISPR Design
Our proprietary platform combines generative AI-driven gRNA design, uncertainty quantification, and Cas9 sequence-aware prediction to revolutionize CRISPR technology's precision and safety.
- Generative AI-driven and epigenetically aware gRNA design
- Full activity distribution prediction with epigenetic factors
- Novel Cas9 sequence-aware prediction models
- Probabilistic safety assessment for medical applications
Scientific Expertise
Our team combines years of experience in genome editing, structural biology, genomics and AI.
Dr. Peter Minary
Co-Founder & Chief Scientific Officer
Research Lecturer & Associate Professor, Department of Computer Science at the University of Oxford
DPhil cand. Jeffrey Mak
Co-Founder & Chief Information Officer
Stipendiary Lecturer at Keble College & Final year DPhil candidate in Computer Science at the University of Oxford
DPhil cand. Furkan Ozden
Co-Founder & Chief Technology Officer
Final year DPhil candidate in Computer Science at the University of Oxford, Google DeepMind Scholar
Scientific Research
Our team regularly publishes research in top-tier scientific journals.
Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs
Özden F, Minary P
Be-dataHIVE: a base editing database
Schneider L, Minary P
CRISPR-DBA: a deep learning framework for uncertainty quantification of CRISPR off-target activities
Cao X, Minary P
The Evolution of Nucleic Acid–Based Diagnosis Methods from the (pre-) CRISPR to CRISPR era and the Associated Machine/Deep Learning Approaches in Relevant RNA Design
Chakraborty SS, et al.
piCRISPR: physically informed deep learning models for CRISPR/Cas9 off-target cleavage prediction
Störtz F, Mak JK, Minary P
Comprehensive computational analysis of epigenetic descriptors affecting CRISPR-Cas9 off-target activity
Mak JK, Störtz F, Minary P
crisprSQL: a novel database platform for CRISPR/Cas off-target cleavage assays
Störtz F, Minary P