Table 1.
Latest biased/unbiased off-target detection methods.
| Tools | Description Features | Cell Line | Study | Limitations | Ref. |
|---|---|---|---|---|---|
| DeepCRISPR | Deep learning tool to predict off/on-target hits together with DNA methylation factors | Human and mouse cell lines | In vitro/ in vivo |
Not suitable for base editors and prime editors | [26] |
| MOFF | The latest multi-layer regression-based model to predict off-target effects by incorporating the GMT and new epigenetic factors along with other factors, such as sequence features, structure features, and epigenetic features | Human and mouse cell lines | In vitro/ in vivo |
Specificity | [27] |
| PEM-Seq | Latest generated off-target detection method, which is highly sensitive in detecting genomic translocations in edited cells | Human and mouse lines | In vivo | Not suitable for base editors and prime editors | [28] |
| GUIDE-Tag | Latest in vivo developed method to detect off-target effects where editing efficiencies are ≥0.2%. | Mouse and human cell lines | In vivo | Cannot provide specificity information | [29] |
| PEAC-Seq | Unbiased method of off-target effect identification in the prime-edited cells. | Mouse and human cell lines | In vivo | Sensitivity | [32] |
| TAPE-Seq | In vivo method to detect both on- and off-target events generated by prime editors | Human cell lines | In vivo | Sensitivity | [33] |