Computational Tools and Resources for CRISPR/Cas Genome Editing
importance 7/10
ai assisted
CRISPR and Genetic Engineering genome editing stage: research
Problem
Designing effective and specific sgRNAs for CRISPR/Cas genome editing while avoiding undesired off-target effects.
Why it matters
Off-target edits can harm crops and biomedical applications, making reliable sgRNA design critical.
What the AI did
The review reports that machine learning models and empirical scoring algorithms have facilitated sgRNA design and off-target prediction.
Results
not stated in source
Impact
The review documents that machine learning has facilitated CRISPR sgRNA design, per the abstract.
Record
| Venue | Genomics Proteomics & Bioinformatics |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Chao Li, Wen Chu, Rafaqat A. Gill, Shifei Sang, Yuqin Shi, Xuezhi Hu, Yuting Yang, Qamar U. Zaman et al. |
| Citations | 129 (OpenAlex, at import time) |
| References | 177 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1016/j.gpb.2022.02.006 |
| Open access | gold |
| Keywords | CRISPR, sgRNA design, off-target prediction, machine learning, genome editing |
Importance rating
7
significant — thousands to millions affected
sgRNA specificity underpins CRISPR applications in biomedical innovation and crop improvement per the abstract—broad life-sciences impact, though this is a review.