---
dataset_info:
  features:
  - name: seq
    dtype: string
  - name: 'y'
    dtype: float64
  splits:
  - name: train
    num_bytes: 20823
    num_examples: 248
  - name: val
    num_bytes: 4422
    num_examples: 53
  - name: test
    num_bytes: 4545
    num_examples: 54
  download_size: 12650
  dataset_size: 29790
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: test
    path: data/test-*
license: mit
---


📊 **Tc-Riboswitch Dataset**

This dataset contains synthetic tetracycline (Tc) riboswitches along with their functional performance metrics. 
It's a curated resource for training machine learning models to predict gene expression from RNA sequences.
The original dataset is from CodonBERT.

⁉️ **Dataset Contents**

- Sequence: The mRNA sequence of the synthetic riboswitch.
- Switching_factor: A numerical value that quantifies the riboswitch's regulatory effect.
  This value represents the change in gene expression in the presence versus the absence of tetracycline.

🎯 **Purpose**

This dataset serves as a benchmark for fine-tuning models on a **regression task**, predicting riboswitch behavior from its sequence. 
We used this dataset to fine-tune **CDS-BART**, a BART-based foundation model trained on massive mRNA sequences.
Demonstrating its ability to perform downstream tasks related to mRNA regulation which are fine-tuned for various mRNA-related downstream task.
**CDS-BART** available at [GitHub](https://github.com/mogam-ai/CDS-BART)

🔧**Usage**
```python
from datasets import load_dataset

dataset = load_dataset('mogam-ai/CDS-BART-Tc-Riboswitches')
```

📚 **Dataset Reference**

- Groher, Ann-Christin, et al. "Tuning the performance of synthetic riboswitches using machine learning." *ACS Synthetic Biology* 8.1 (2018): 34-44.
- Li, Sizhen, et al. "CodonBERT large language model for mRNA vaccines." Genome research 34.7 (2024): 1027-1035.