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DeepSecE-0.1.0
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مشاهده بیشتر
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
A Deep Learning Framework for Multi-class Secreted Effector Prediction in Gram-negative Bacteria.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | DeepSecE-0.1.0 |
| نام | DeepSecE |
| نسخه کتابخانه | 0.1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Yumeng Zhang |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/zhangyumeng1sjtu/DeepSecE |
| آدرس اینترنتی | https://pypi.org/project/DeepSecE/ |
| مجوز | MIT |
# DeepSecE
Implementation of Effector-specific Transformer model used in secretion effector prediction in Gram-negative bacteria. DeepSecE achieves state-of-the-art performance in multi-class effector prediction leveraging the power of pre-trained protein language model [ESM-1b](https://github.com/facebookresearch/esm). An additional transformer layer enhances the understanding of secreted patterns. It also provide a rapid pipeline to identify type I-IV and VI secretion systems with corresponding effectors.
## Performance Comparison
We choose various model architecture with different pre-trained models and training strategies, and evalute their model capacity on cross-validation and independent testing. Performance metrics are reported in the table.
| Pre-trained Model | Strategy | ACC | | F1 | | AUPRC | |
| :-----------------: | :---------------------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: | :-------------: |
| | | Valid | Test | Valid | Test | Valid | Test |
| / | PSSM+CNN | 0.799 | 0.822 | 0.712 | 0.724 | 0.752 | 0.774 |
| TAPEBert | Linear probing | 0.816 | 0.838 | 0.764 | 0.770 | 0.802 | 0.822 |
| ESM-1b | Linear probing | 0.876 | 0.870 | 0.841 | 0.810 | 0.880 | 0.871 |
| ESM-1b | Finetuning | 0.878 | 0.850 | 0.846 | 0.808 | 0.887 | **0.883** |
| ESM-1b | Effector-specific transformer | **0.883** | **0.898** | **0.848** | **0.849** | **0.892** | 0.879 |
## Set up
### Requirements
- python==3.9.7
- torch==1.10.2
- biopython==1.79
- einops==0.4.1
- fair-esm>=0.4.0
- tqdm==4.64.0
- numpy==1.21.2
- scikit-learn==0.23.2
- matplotlib==3.5.1
- seaborn==0.11.0
- tensorboardX==2.0
- umap-learn==0.5.3
- warmup-scheduler==0.3.2
While we have not tested with other versions, any reasonably recent versions of these requirements should work.
### Installation
As a prerequisite, you must have PyTorch installed. It is recommended to create a new virtual environment for installation. For model training and prediction from seperate protein sequence(s), You can use this one-liner for installation.
```shell
pip install git+https://github.com/zhangyumeng1sjtu/DeepSecE.git
```
If you want to plot the sequence attention, you should install package `logomarker` first.
```shell
pip install logomaker
```
If you want to predict secretion systems and effectors, you should install `macsyfinder` and `hmmer` first. Meanwhile, you need to download the TXSS profiles from [here](https://tool2-mml.sjtu.edu.cn/DeepSecE/TXSS_profiles.tar.gz), and decompress it into data directory.
```shell
pip install macsyfinder
conda install -c bioconda hmmer
cd data
wget https://tool2-mml.sjtu.edu.cn/DeepSecE/TXSS_profiles.tar.gz
tar -zxvf TXSS_profiles.tar.gz
```
The weights of DeepSecE model can be downloaded from https://tool2-mml.sjtu.edu.cn/DeepSecE/checkpoint.pt.
## Usage
### Train model
You can train the DeepSecE model by running `train.py` or `scripts/kfold_train.sh` for cross-validation.
```shell
for i in {0..4}
do
python train.py --model effectortransformer \
--data_dir data \
--batch_size 32 \
--lr 5e-5 \
--weight_decay 4e-5 \
--dropout_rate 0.4 \
--num_layers 1 \
--num_heads 4 \
--warm_epochs 1 \
--patience 5 \
--lr_scheduler cosine \
--lr_decay_steps 30 \
--kfold 5 \
--fold_num $i \
--log_dir runs/attempt_cv
done
```
Parameters:
- `--model` train a effector transformer or finetue a ESM-1b model.
- `--data_dir` directory that stores training data (default: ./data).
- `--num_layers` numbers of trainable transformer layer. (default: 1)
- `--num_heads` numbers of attention heads in effector-specific transformer (default: 4).
- `--patience` patience for early stopping used in training.
- `--lr_schedular` learning rate schedular [step, consine].
- `--log_dir` directory that stores training outputs (default: logs).
### Prediction
You can predict your interested type of secreted effectors only or predict secretion systems and corresponding effectors from scratch.
#### Predict secretion effector
```shell
python predict.py --fasta_path examples/Test.fasta \
--model_location [path to model weights] \
--secretion_systems [I II III IV VI] \
--out_dir examples [--save_attn --no_cuda]
```
Parameters:
- `--fasta_path` path to the input protein FASTA file.
- `--model_location` path to the model weights (download from [here](https://tool2-mml.sjtu.edu.cn/DeepSecE/checkpoint.pt)).
- `--secretion_systems` type(s) of secretion system to predict (default: I II III IV VI).
- `--out_dir` directory that stores prediction outputs.
- `--save_attn` add to save sequence attention of effector.
- `--no_cuda` add when CUDA is not available.
#### Predict secretion system and effectors
**Note:** Make sure the input file is **ordered protein sequences coded in a bacterial genome**.
```shell
python predict_genome.py --fasta_path examples/NC_002516.2_protein.fasta \
--model_location [path to model weights] \
--data_dir data \
--out_dir examples/NC_002516.2 [--save_attn --no_cuda]
```
Parameters:
- `--fasta_path` path to the input protein FASTA file.
- `--model_location` path to the model weights (download from [here](https://tool2-mml.sjtu.edu.cn/DeepSecE/checkpoint.pt)).
- `--data_dir` directory that stores TXSS profiles (download from [here](https://tool2-mml.sjtu.edu.cn/DeepSecE/TXSS_profiles.tar.gz)).
- `--out_dir` directory that stores prediction outputs.
- `--save_attn` add to save sequence attention of effector.
- `--no_cuda` add when CUDA is not available.
It takes about 5 minutes to predict effectors from a bacterial genome containing 3000 proteion coding sequences on a NVIDIA GeForce RTX 2080 Super GPU.
### Plot attention
If you save the attention output of the putative effectors (add `--save_attn`), you can run `python scripts /plot_attention.py [directory of prediction output]` to plot the saliency map from attention, and infer potentially import regions related to protein secretion.
## Contact
Please contact Yumeng Zhang at [zhangyumeng1@sjtu.edu.cn](mailto:zhangyumeng1@sjtu.edu.cn) for questions.
نیازمندی
| مقدار | نام |
|---|---|
| - | torch |
| - | biopython |
| - | einops |
| - | fair-esm |
| - | tqdm |
| - | numpy |
| - | scikit-learn |
| - | matplotlib |
| - | seaborn |
| - | tensorboardX |
| - | umap-learn |
| - | warmup-scheduler |
نحوه نصب
نصب پکیج whl DeepSecE-0.1.0:
pip install DeepSecE-0.1.0.whl
نصب پکیج tar.gz DeepSecE-0.1.0:
pip install DeepSecE-0.1.0.tar.gz