Learning to quantify uncertainty in off-target activity for CRISPR guide RNAs
importance 6/10
ai assisted
CRISPR and Genetic Engineering genome editing stage: research open-source model
Problem
Off-target effects limit CRISPR clinical use, and current point predictions do not capture the range of possible outcomes.
Why it matters
Unintended off-target edits could cause harmful consequences in clinical genome editing.
What the AI did
crispAI, a neural-network approach using a Zero Inflated Negative Binomial noise model, predicts uncertainty estimates for off-target cleavage activity to guide sgRNA design.
Results
not stated in source
Impact
Peer-reviewed tool intended to improve risk assessment in sgRNA design; no benchmarks stated.
Record
| Venue | Nucleic Acids Research |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Furkan Özden, Péter Mináry |
| Citations | 21 (OpenAlex, at import time) |
| References | 68 |
| Model or method | crispAI (neural network with ZINB noise model) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1093/nar/gkae759 |
| Open access | gold |
| Keywords | CRISPR, off-target activity, uncertainty quantification, sgRNA design, neural network |
Importance rating
6
significant — thousands to millions affected
Off-target effects are stated to limit CRISPR clinical applicability; uncertainty-aware sgRNA risk assessment is a meaningful enabling advance for genome editing.