File size: 3,473 Bytes
3ce1caf aa5f969 3ce1caf aa5f969 3ce1caf |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 |
---
license: apache-2.0
base_model:
- zhihan1996/DNABERT-2-117M
tags:
- biology
- medical
---
#### Citation
If you find the model useful in your research, we ask that you cite the relevant paper:
- Nakamae *et al*. Risk Prediction of RNA Off-Targets of CRISPR Base Editors in Tissue-Specific Transcriptomes Using Language Models. *International Journal of Molecular Sciences* **2025**, 26(4), 1723.; [DOI: 10.3390/ijms26041723](https://doi.org/10.3390/ijms26041723)
---
This is one of the fine-tuned models, named RNAOffScan v2 (previously named as SNL model), from [zhihan1996/DNABERT-2-117M
](https://huggingface.co/zhihan1996/DNABERT-2-117M).
The RNAOffScan v2 can predict the RNA offtarget induced by cytosine base editors (CBEs).
Here is an example of using the model for RNA-off-target prediction.
**pred_rna_offtarget.py:**
```python
import sys
import numpy as np
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
__authors__ = ["Kazuki Nakamae"]
__version__ = "1.0.0"
def pred_rna_offtarget(dna, model_dir):
try:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(model_dir, trust_remote_code=True).to(device)
except Exception as e:
print(f"Error loading model from {model_dir}: {e}")
sys.exit(1)
inputs = tokenizer(dna, return_tensors='pt')
model.eval()
with torch.no_grad():
outputs = model(
inputs["input_ids"].to(device),
inputs["attention_mask"].to(device),
)
print("[Negative, Positive]")
print(outputs.logits)
y_preds = np.argmax(outputs.logits.to('cpu').detach().numpy().copy(), axis=1)
def id2label(x):
return model.config.id2label[x]
y_dash = [id2label(x) for x in y_preds]
print("Result:")
print(y_dash)
# LABEL_0: Not RNA-offtarget / LABEL_1: RNA-offtarget
return (dna, y_dash)
def print_usage():
print(f"Usage: {sys.argv[0]} <input DNA sequence> <DNABERT-2 model directory>")
print("Options:")
print(" -h, --help Show this help message and exit")
print(" -v, --version Show version information and exit")
def print_version():
print(f"{sys.argv[0]} version {__version__}")
print("Authors:", ", ".join(__authors__))
if __name__ == "__main__":
if len(sys.argv) != 3:
if len(sys.argv) == 2 and sys.argv[1] in ("-h", "--help"):
print_usage()
sys.exit(0)
elif len(sys.argv) == 2 and sys.argv[1] in ("-v", "--version"):
print_version()
sys.exit(0)
else:
print_usage()
sys.exit(1)
dna = sys.argv[1]
model_dir = sys.argv[2]
pred_rna_offtarget(dna, model_dir)
```
```bash
$ python pred_rna_offtarget.py GGCAGGGCTGGGGAAGCTTACTGTGTCCAAGAGCCTGCTG KazukiNakamae/SNLmodel;
[Negative, Positive]
tensor([[-0.7521, 0.4817]])
Result:
['LABEL_1']
$ python pred_rna_offtarget.py GTCATCTAACAAAAATATTCCGTTGCAGGAAAAGCAAGCT KazukiNakamae/SNLmodel;
[Negative, Positive]
tensor([[ 0.9211, -0.8157]])
Result:
['LABEL_0']
```
#### Developers of the fine-tuned model
- [Takayuki Suzuki](https://github.com/szktkyk)
- [Kazuki Nakamae](https://github.com/KazukiNakamae) |