This is a project which is currently making use of HPC facilities at Newcastle University. It is active.
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This project investigates the use of deep neural networks (autoencoders) for DNA data storage encoding. The model learns to encode binary information into DNA sequences that satisfy biological constraints (period-avoidance constraints) while maintaining high reconstruction accuracy. We train convolutional neural network-based encoder-decoder architectures with differentiable constraint penalties, requiring GPU acceleration for longer sequence lengths and extended training.
Python 3.10+, PyTorch (deep learning framework), running on GPU nodes. The workflow involves training CNN-based autoencoder models with approximately 30,000-50,000 training epochs per experiment. We require GPU acceleration (CUDA) for matrix operations on sequence data. No specialised licensed software is needed — all tools are open-source.