CRISPR–Cas9 gRNA efficiency prediction: an overview of predictive tools and the role of deep learning
Problem
Designing efficient and specific guide RNAs for CRISPR-Cas9 gene editing is difficult, and many predictive computational tools exist but their performance and best-use scenarios are unclear.
Why it matters
Poor gRNA design wastes research effort and risks failed or off-target gene-editing experiments.
What the AI did
The paper reviews computational and new deep learning tools that predict gRNA on-target cleavage efficiency, and evaluates them on independent datasets with usage suggestions.
Results
not stated in source
Impact
Provides guidance on choosing gRNA efficiency prediction tools; no adoption or concrete benchmark numbers stated.
Record
| Venue | Nucleic Acids Research |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Vasileios Konstantakos, Anastasios Nentidis, Anastasia Krithara, Γεώργιος Παλιούρας |
| Citations | 241 (OpenAlex, at import time) |
| References | 95 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1093/nar/gkac192 |
| Open access | gold |
| Keywords | CRISPR-Cas9, gRNA design, deep learning, efficiency prediction, benchmarking |
Importance rating
significant — thousands to millions affected
CRISPR-Cas9 is a successful, promising gene-editing technology per abstract; better gRNA design affects many research efforts, but no scale stated.