Nucleic Transformer: Classifying DNA sequences with Self-attention and Convolutions

Source code to reproduce results in the paper "Nucleic Transformer: Classifying DNA sequences with Self-attention and Convolutions".

How to use the models

I also made a web app to use the models. Check it out at https://github.com/Shujun-He/Nucleic-Transformer-WebApp

Requirements

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 .

Repo file structure

The src folder includes all the code needed to reproduce results in the paper and the OpenVaccine competition. Additional instructions are in each folder

src/Ecoli_Promoter_classification includes all the code and file needed to reproduce results for E.coli promoter classification

src/Eukaryotic_Promoters_Classification includes all the code and file needed to reproduce results for eukaryotic promoter classification

src/Non_Coding_Variant_Effects includeds all the code needed to reproduce results for the deepsea dataset

src/Viral_identification includeds all the code needed to reproduce results for the viraminer dataset

src/Enchancer_classification includeds all the code needed to reproduce results for the enhancer dataset