DeeReCT-PolyA

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A Deep Learning model for PAS recognition

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# DeeReCT-PolyA: a robust and generic deep learning <br /> method for PAS identification

This distribution provides an implementation, along with the data and trained models used in our paper:

Xia, Zhihao, et al. "**[DeeReCT-PolyA: a robust and generic deep learning method for PAS identification](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/bty991/5221014)**".  Bioinformatics, 2018.

If you find the code useful for your research, please cite our paper.
```
@article{deepolyA,
    author = {Xia, Zhihao and Li, Yu and Zhang, Bin and Li, Zhongxiao and Hu, Yuhui and Chen, Wei and Gao, Xin},
    title = "{DeeReCT-PolyA: a robust and generic deep learning method for PAS identification}",
    year = {2018},
    month = {11},
    doi = {10.1093/bioinformatics/bty991},
    url = {https://dx.doi.org/10.1093/bioinformatics/bty991},
}
```

The code has been tested with Python3 + Tensorflow1.7. Tensorflow GPU edition is recommended. However, running the code with CPU is still pretty fast if the dataset is not too large.

The repository contains pre-trained models in the `models` directory for PAS identification for Dragon-human, Omni-human, C57BL/6J (BL) and SPRET/EiJ (SP) mouse. You may use the pre-trained models to test or fine-tune the model with your own data. Note that each pre-trained models is trained with 4 out 5 folds of the data while the results in the paper are evaluated using 5-fold cross validation.

Please look at each script for a list of parameters that you can specify or run
```
python script.py -h
```
If you have any question, please contact zhihao.xia@wustl.edu.

## Data preparation
To prepare your data for training or fine-tuning, sequences should be put in `.txt` files in which each line is a ATGC sequence of length 206 with the centered 6-mers as the true or pseudo poly(A) motif. Positive data and negative data should be put in two different sub-directories. Then, run 
```
python data_prep.py pos_root neg_root outfile [--nfolds n]
```
to encode the raw sequences with one-hot encoding and split the data into training, validation and test set. The processed dataset will be saved as a `.npz` file. Note that if you just want to use our pre-trained model for inference on your own data or you don't have the ground truth labels, we provide [testing code](#test-trained-models) that can directly take the sequences without the preparation as inputs and make predictions.

## Training
After the data preparation, you can train a DeeReCT-PolyA model from scratch by running
```
python train.py data [--out outfile] [--hparam hyperparam_file]
```
The input data should be the `.npz` file generated from last step. There are some hyper-parameters, e.g. learning rate, that you can specify for the model (set randomly as default). We suggest using random search to find the best set of hyper-parameters based on the performance on the validation dataset. For reference, we provide some sets of hyper-parameters in the `models` directory. The trained model can be saved to the output file.

## Fine-tuning
As discussed in our paper, when you need a DeeReCT-PolyA model for your own data, instead of training from scratch, it is usually beneficial to fine-tune a pre-trained model, especially when the new training data is insufficient. To fine-tune a pre-trained with your own data, run
```
python train.py data [--out outfile] [--hparam hyperparam_file] --pretrained model_file
```

## Test trained models
 To test the model with your data, run 
```
python test.py data model [--out outfile]
```
The data can be a `.txt` file in which each line is a ATGC sequence of length 206 with the centered 6-mers as the true or pseudo poly(A) motif. It can also be a `.npz` file containing the one-hot encoded sequences generated by `data_prep.py` and the test split in the `.npz` file will be used. The binary predictions for input sequences can be saved by specifying the output file.

***
## Reference
Dragon-human Poly(A) dataset: Kalkatawi, Manal, et al. "Dragon PolyA Spotter: predictor of poly (A) motifs within human genomic DNA sequences." Bioinformatics 28.1 (2011): 127-129.

Omni-human Poly(A) dataset: Arturo, Magana-Mora et al. "Omni-PolyA: a method and tool for accurate recognition of Poly (A) signals in human genomic DNA." BMC genomics 18.1 (2017): 620.

DeeReCT-PolyA

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