MSNet-4mC

Author: Liu Chunting Affiliation: Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University E-mail: liuchunting@kuicr.kyoto-u.ac.jp

Details

• Users can run the MSNet_4mC.py to identify DNA 4mC sites.
• The folders of "Li_2020_dataset" and "Lin_2017 dataset" contain the datasets and the files for experiments.
• The Lin_2017 dataset and Li_2020 dataset can be also accessed at http://DeepTorrent.erc.monash.edu/
• The trained model weights for the test are provided in the folder "Models".

Dependency

• Python 3.8.8 and Pytorch 1.11.0 or later versions

Installation Guide

• Download from GitHub at https://github.com/LIU-CT/MSNet-4mC
• cd MSNet-4mC
• pip install -r requirements.txt

Usage

For evaluation:

• Run the default dataset

python MSNet_4mC.py --Dataset Lin_2017_Dataset --Species <Species>

python MSNet_4mC.py --Dataset Li_2020_Dataset --Species <Species> 

• Make the prediction for customized data

python MSNet_4mC.py --Dataset User_Dataset --Species <Species> --Fasta_file <Fasta_file>  

• For evaluation on the default dataset, users can also directly run test.py with corrected paths to the datasets and models.

For training

• The training codes are given in the Dataset directories.
• Train the default dataset
  • Run training_basemodel.py to train the base model.
  • Run training_scratch_and_finetuning.py to train the species-specific models from scratch or fine-tuning the hyperparameters to retrain the species-specific models based the base model with the different settings of load_pretrain and load_path.
• Train the customized data
  • Firstly, use class_weight.py to calculate class weights for different species.
  • Secondly, use training_basemodel.py to train a base model on the merged training dataset.
  • Finally, use training_scratch_and_finetuning.py to retrain the species-specific model on each species training dataset.