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---
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license: apache-2.0
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base_model:
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- zhihan1996/DNABERT-2-117M
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tags:
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- biology
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- medical
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---
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#### Citation
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If you find the model useful in your research, we ask that you cite the relevant paper:
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- 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)
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---
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This is one of the fine-tuned models, named RNAOffScan v2 (previously named as SNL model), from [zhihan1996/DNABERT-2-117M
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](https://huggingface.co/zhihan1996/DNABERT-2-117M).
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The RNAOffScan v2 can predict the RNA offtarget induced by cytosine base editors (CBEs).
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Here is an example of using the model for RNA-off-target prediction.
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**pred_rna_offtarget.py:**
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```python
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import sys
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import numpy as np
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification
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__authors__ = ["Kazuki Nakamae"]
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__version__ = "1.0.0"
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def pred_rna_offtarget(dna, model_dir):
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try:
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
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model = AutoModelForSequenceClassification.from_pretrained(model_dir, trust_remote_code=True).to(device)
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except Exception as e:
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print(f"Error loading model from {model_dir}: {e}")
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sys.exit(1)
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inputs = tokenizer(dna, return_tensors='pt')
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model.eval()
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with torch.no_grad():
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outputs = model(
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inputs["input_ids"].to(device),
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inputs["attention_mask"].to(device),
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)
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print("[Negative, Positive]")
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print(outputs.logits)
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y_preds = np.argmax(outputs.logits.to('cpu').detach().numpy().copy(), axis=1)
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def id2label(x):
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return model.config.id2label[x]
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y_dash = [id2label(x) for x in y_preds]
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print("Result:")
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print(y_dash)
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# LABEL_0: Not RNA-offtarget / LABEL_1: RNA-offtarget
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return (dna, y_dash)
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def print_usage():
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print(f"Usage: {sys.argv[0]} <input DNA sequence> <DNABERT-2 model directory>")
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print("Options:")
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print(" -h, --help Show this help message and exit")
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print(" -v, --version Show version information and exit")
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def print_version():
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print(f"{sys.argv[0]} version {__version__}")
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print("Authors:", ", ".join(__authors__))
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if __name__ == "__main__":
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if len(sys.argv) != 3:
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if len(sys.argv) == 2 and sys.argv[1] in ("-h", "--help"):
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print_usage()
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sys.exit(0)
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elif len(sys.argv) == 2 and sys.argv[1] in ("-v", "--version"):
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print_version()
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sys.exit(0)
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else:
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print_usage()
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sys.exit(1)
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dna = sys.argv[1]
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model_dir = sys.argv[2]
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pred_rna_offtarget(dna, model_dir)
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```
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```bash
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$ python pred_rna_offtarget.py GGCAGGGCTGGGGAAGCTTACTGTGTCCAAGAGCCTGCTG KazukiNakamae/SNLmodel;
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[Negative, Positive]
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tensor([[-0.7521, 0.4817]])
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Result:
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['LABEL_1']
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$ python pred_rna_offtarget.py GTCATCTAACAAAAATATTCCGTTGCAGGAAAAGCAAGCT KazukiNakamae/SNLmodel;
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[Negative, Positive]
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tensor([[ 0.9211, -0.8157]])
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Result:
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['LABEL_0']
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```
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#### Developers of the fine-tuned model
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- [Takayuki Suzuki](https://github.com/szktkyk)
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- [Kazuki Nakamae](https://github.com/KazukiNakamae) |
