A Python simulation evaluating the use of LDPC error correction and photolithography for reliable, cost-effective DNA-based data storage.
This project is a Python simulation for encoding, introducing noise, and decoding data using DNA as a storage medium. The script tests the application of the LDPC (Low-Density Parity-Check) algorithm for error correction in DNA storage, simulating real-world mutation rates of up to 15%. By leveraging photolithography for DNA synthesis, the project evaluates the feasibility of reliable and cost-effective DNA-based data storage.
This project was undertaken during my second year of engineering studies (Bac+4 in the French education system) as part of a school assignment, involving the writing of a research paper on a novel topic. Collaborating as a team of four, we combined biotechnology and computer science to propose and simulate an experimental approach, gaining hands-on experience in interdisciplinary research and teamwork.
To install and run the Restore Data With LDPC Algorithm simulation script, follow these steps:
- Clone the repository:
git clone https://github.com/Victor-Pavageau/RestoreDataWithLDPCAlgorithm.git- Navigate to the project directory:
cd RestoreDataWithLDPCAlgorithm-
Install the required dependencies:
Make sure you have Python installed.
Open the main.ipynb Jupyter Notebook file in Visual Studio Code or Google Colab and run it.
Development: The development of this project was completed in May 2023.
Maintenance: This project is no longer maintained.
Future updates: No future updates are planned for this project.