CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets

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├── preprocessed_data/                          # Preprocessed data folder
├── train.py                                    # Model training code
├── model.py                                    # Model construction code        
├── requirements.txt                            # required python packages
└── README.md

This repository contains code for "CRMnet: a deep learning model for predicting gene expression from large regulatory sequence datasets"

• To setup environment on TPU vm (take v2-8 as example):

  1. initiate a tpu-vm with tensorflow 2.8.0 preinstalled:

      gcloud alpha compute tpus tpu-vm create tpu_v2 --zone=asia-east1-c --accelerator-type=v2-8 --version=tpu-vm-tf-2.8.0
     

  2. install supporting packages:

      pip install -r requirements.txt
     

• To download the original data and preprocess the data from scratch:

  1. Download the original data from: https://zenodo.org/record/4436477#.Y4a_PS0RoUE

  2. Copy "complex_media_training_data_Glu.txt" to "./Yeast_Original_Data/"

  3. Preprocessed the data:

      python3 data_preprocessing.py
     

  4. The final dataset in tf.data.Dataset format is about 250GB

• To use the trained model:

  1. Download the model weight from: https://zenodo.org/record/7375243#.Y9W0iS0RoUG

  2. Load the trained model:

      model = tf.keras.models.load_model(PATH)
     

• To train model from scratch:

  1. on tpu v2-8 about 4 hours to converge:

      python3 train.py
     

     trained model will be saved on folder "/saved_model"