Translatomer

predicting ribosome profiling reveals translational regulation and interprets disease variants

Translatomer

This is our implementation for the paper:

Jialin He, Lei Xiong#, Shaohui Shi, Chengyu Li, Kexuan Chen, Qianchen Fang, Jiuhong Nan, Ke Ding, Jingyun Li, 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. (Nature Machine Intelligence)

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:

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.