Translatomer

This is our implementation for the paper:

Jialin He*, Lei Xiong*#, Shaohui Shi, Chengyu Li, Kexuan Chen, Qianchen Fang, Jiuhong Nan, Ke Ding, Yuanhui Mao, Carles A. Boix, Xinyang Hu, Manolis Kellis, Jingyun Li and Xushen Xiong#. Deep learning prediction of ribosome profiling with Translatomer reveals translational regulation and interprets disease variants.

Introduction

Translatomer is a transformer-based multi-modal deep learning framework that predicts ribosome profiling track using genomic sequence and cell-type-specific RNA-seq as input.

Citation

If you want to use our codes and datasets in your research, please cite:

@article{He2024.11.23.translatomer,
    title = {Deep learning prediction of ribosome profiling with Translatomer reveals translational regulation and interprets disease variants},
    author = {Jialin He and Lei Xiong, Shaohui Shi and Chengyu Li and Kexuan Chen and Qianchen Fang and Jiuhong Nan and Ke Ding, Yuanhui Mao and Carles A. Boix and Xinyang Hu and Manolis Kellis and Jingyun Li, Xushen Xiong},
    year = {2024},
    doi = {10.1038/s42256-024-00915-6},
    publisher = {},
    url = {https://doi.org/10.1038/s42256-024-00915-6},
    journal = {Nature Machine Intelligence}
}

Prerequisites

To run this project, you need the following prerequisites:

• Python 3.9
• PyTorch 1.13.1+cu117
• Other required Python libraries (please refer to requirements.txt)

You can install all the required packages using the following command:

conda create -n pytorch python=3.9.16
conda activate pytorch

pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu117

pip install -r requirements.txt 

Data Preparation

Example data for model training can be downloaded from Zenodo

• Put all input files in a data folder. The input files have to be organized as follows:

  + data
    + hg38
      + K562
        + GSE153597
          + input_features
            ++ rnaseq.bw 
          + output_features
            ++ riboseq.bw 
      + HepG2
        + GSE174419
          + input_features
            ++ rnaseq.bw 
          + output_features
            ++ riboseq.bw 
      *...
      ++ gencode.v43.annotation.gff3
      ++ hg38.fa
      ++ hg38.fai
      ++ mean.sorted.bw
    + mm10
      *...

• To generate training data, use the following command:

python generate_features_4rv.py [options]

[options]:
- --assembly  Genome reference for the data. Default = 'hg38'.
- --celltype  Name of the cell line. Default = 'K562'.
- --study  GEO accession number for the data. Default = 'GSE153597'.
- --region_len  The desired sequence length (region length). Default = 65536.
- --nBins  The number of bins for dividing the sequence. Default = 1024.

Example to run the codes:

find data/ -type d -name 'output_features' -exec mkdir -p '{}/tmp' \;
find data/ -type d -name 'input_features' -exec mkdir -p '{}/tmp' \;
nohup python generate_features_4rv.py --assembly hg38 --celltype HepG2 --study GSE174419 --region_len 65536 --nBins 1024 &
nohup python generate_features_4rv.py --assembly hg38 --celltype K562 --study GSE153597 --region_len 65536 --nBins 1024 &

Model Training

To train the Translatomer model, use the following command:

python train_all_11fold.py [options]

[options]:
- --seed  Random seed for training. Default value: 2077.
- --save_path  Path to the model checkpoint. Default = 'checkpoints'.
- --data-root  Root path of training data.  Default = 'data' (Required).
- --assembly  Genome assembly for training data. Default = 'hg38'.
- --model-type  Type of the model to use for training. Default = 'TransModel'.
- --fold  Which fold of the model training. Default='0',
- --patience  Epochs before early stopping. Default = 8.
- --max-epochs  Max epochs for training. Default = 128.
- --save-top-n  Top n models to save during training. Default = 20.
- --num-gpu  Number of GPUs to use for training. Default = 1.
- --batch-size  Batch size for data loading. Default = 32.
- --ddp-disabled  Flag to disable ddp (Distributed Data Parallel) for training. If provided, it will enable DDP with batch size adjustment.
- --num-workers  Number of dataloader workers. Default = 1.

Example to run the codes:

nohup python train_all_11fold.py --save_path results/bigmodel_h512_l12_lr1e-5_wd0.05_ws2k_p32_fold0 --data-root data --assembly hg38 --dataset data_roots_mini.txt --model-type TransModel --fold 0 --patience 6 --max-epochs 128 --save-top-n 128 --num-gpu 1 --batch-size 32 --num-workers 1 >DNA_logs/bigmodel_h512_l12_lr1e-5_wd0.05_ws2k_p32_fold0.log 2>&1 &
nohup python train_all_11fold.py --save_path results/bigmodel_h512_l12_lr1e-5_wd0.05_ws2k_p32_fold1 --data-root data --assembly hg38 --dataset data_roots_mini.txt --model-type TransModel --fold 1 --patience 6 --max-epochs 128 --save-top-n 128 --num-gpu 1 --batch-size 32 --num-workers 1 >DNA_logs/bigmodel_h512_l12_lr1e-5_wd0.05_ws2k_p32_fold1.log 2>&1 &

Tutorial

• Load pretrained model Pretrained model can be downloaded from Zenodo
• An example notebook containing code for applying Translatomer is here.

License

This project is licensed under MIT License.

Contact

For any questions or inquiries, please contact xiongxs@zju.edu.cn.