Source code to reproduce results in the paper "RNAdegformer: Accurate Prediction of mRNA Degradation at Nucleotide Resolution with Deep Learning".
I also made a web app to use the models. Check it out at https://github.com/Shujun-He/RNAdegformer-Webapp
In this page you can predict RNA degradation at each nucleotide and visualize the attention weights of the RNAdegformer
I included a file (environment.yml) to recreate the exact environment I used. Since I also use this environment for computer vision tasks, it includes some other packages as well. This should take around 10 minutes. After installing anaconda:
conda env create -f environment.yml
Then to activate the environment
conda activate torch
Additionally, you will need Nvidai Apex: https://github.com/NVIDIA/apex
git clone https://github.com/NVIDIA/apex
cd apex
pip install .
Also you need to install the Ranger optimizer
git clone https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
cd Ranger-Deep-Learning-Optimizer
pip install -e .The src folder includes all the code needed to reproduce results in the paper and the OpenVaccine competition. Additional instructions are in the folder
src/OpenVaccine includes all the code needed to run a ten-fold model for the openvaccine dataset
For original dataset, see https://www.kaggle.com/c/stanford-covid-vaccine/data
In addition to the secondary structure features given by Das Lab, I also generated additional secondary structure features at 2 temperatures with 6 biophysical packages (12x), for these features, see https://www.kaggle.com/shujun717/openvaccine-12x-dataset