Code for the paper "High-Throughput DNA melt measurements enable improved models of DNA folding thermodynamics" https://doi.org/10.1101/2024.01.08.574731. "NNN" stands for "Not-Nearest-Neighbor"
Jupyter notebooks 01.1 to 01.5 correspond to the 5 main figures.
Notebook 01.0_DataPrep.ipynb performs data cleaning and train-val-test split from the output of the preprocessing pipeline.
Functions used for generation of figures are defined in nnn/.
Three major conda environments were used:
- `nnn.yml` most analysis in the repository
- `torch.yml` for training and running graph neural networks
- `nn_train.yml` for fitting and running linear regression models.
Also available as a singularity container as defined in `nn_train.def`.
Packages draw_rna and RiboGraphViz were installed from file as directed on Das lab github repositories https://github.com/DasLab/draw_rna and https://github.com/DasLab/RiboGraphViz.
NUPACK4 was also manually installed from file as it requires a free liscence for download (https://docs.nupack.org/).
Python scripts in scripts/ generates the sequences in the variant library and are helpful to understand design logics.
Run gnn_run.py in torch envoronment, pointing to the path of the saved model state dict file.
For any questions, contact Yuxi Ke (kyx@stanford.edu)
Jan. 2024