This repository is the official implementation of "Single Read Reconstruction for DNA Data Storage Using Transformers", a novel approach leveraging an encoder-decoder Transformer architecture for DNA-based data storage. In the face of increasing global data storage demands, DNA storage emerges as a promising solution with high information density. However, DNA synthesis and sequencing are prone to errors. Our model uniquely addresses this by facilitating single-read reconstruction, significantly reducing reading costs and achieving lower error rates compared to traditional multi-read methods.

• Transformer Architecture: A groundbreaking deep learning model specifically designed for error correction in DNA sequences.
• Single-Read Reconstruction: First-of-its-kind approach using deep learning for single-read reconstruction in DNA-based storage, significantly reducing overall costs.
• Versatility in Data Types: Capable of handling and reconstructing diverse data types, including text, images, and code scripts.
• Efficient and Accurate: Demonstrates lower error rates in reconstruction from single DNA strand reads than existing state-of-the-art algorithms.

Run pip install -r requirements.txt to install required packages.

• Configuration: Customize model configurations in configuration.py.
• Data Processing: Employ data_utils for efficient data loading and preparation.
• Model Training: Initiate model training through DNAmodel.py.
• Diverse Data Handling: Utilize image_utils.py and text_utils.py for conversion and reconstruction of images and text data.
• Prediction and Evaluation: Execute predictions and evaluate performance using Predict.py.

• configuration.py: Settings and parameters for the model.
• data_utils: Modules for data handling and preprocessing.
• DNAmodel.py: Core deep learning model and associated methods.
• image_utils.py, text_utils.py: Tools for processing image and text data.
• Predict.py: Functions for model predictions and evaluations.
• utils.py: General utilities, including high-fidelity error simulation methods.

Python packages required are listed in requirements.txt.

If you find this work useful, please cite our paper:

@misc{nahum2021singleread,
      title={Single-Read Reconstruction for DNA Data Storage Using Transformers}, 
      author={Yotam Nahum and Eyal Ben-Tolila and Leon Anavy},
      year={2021},
      eprint={2109.05478},
      archivePrefix={arXiv},
      primaryClass={cs.ET}
}

This project is licensed under the MIT License.

For more details, refer to the full paper: Single-Read Reconstruction for DNA Data Storage Using Transformers.