Pythia is a neural network capable of encoding the context-free grammar of RNA structure.
A dilated convolutional operation can detect base pairing of two nucleotides at a fixed distance. A neural network layer capable of learning structure, therefore, must detect base pairing of nucleotides at a range of distances, as opposed to a single dilation width.
Through custom design of fixed weights within the weight tensor of a convolutional layer, we developed an efficient means for simultaneous detection of base pairing at a range of dilation widths while allowing convolutional filters to capture asymmetrical bulges as well.
Our fixed dilated convolutional layer can capture base pairing of nucleotides within a defined range of distances, allowing for asymmetrical bulges of defined lengths to occur within the RNA structure as well. The fixed dilated convolutional layer, therefore, learns the context-free grammar of RNA structure. Combined with other convolutional and dense layers, Pythia can learn minimum free energy of RNA folding, complex RNA structures such as pseudo-knots, and learn binding preferences of RBPs
Please see our notebooks .
Run the following commands to install the dependencies:
conda create -n pytorch -c pytorch pytorch=1.4.0
pip install apex
pip install adabound
conda install -c bioconda ushuffle
conda install -c conda-forge transformers
conda install -c anaconda scikit-learn
conda install -c anaconda seaborn
If using a GPU, make sure nvidia-smi returns a meaningful output such as:
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 470.103.01 Driver Version: 470.103.01 CUDA Version: 11.4 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|===============================+======================+======================|
| 0 Tesla P100-PCIE... On | 00000000:2F:00.0 Off | 0 |
| N/A 53C P0 28W / 250W | 0MiB / 12198MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
| No running processes found |
+-----------------------------------------------------------------------------+
To run the notebooks, also add:
conda install -c anaconda ipykernel
python -m ipykernel install --user --name=pytorch
jupyter notebook