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https://github.com/dohlee/spliceai-pytorch

Implementation of SpliceAI, Illumina's deep neural network to predict variant effects on splicing, in PyTorch.
https://github.com/dohlee/spliceai-pytorch

artificial-intelligence bioinformatics biology computational-biology deep-learning deep-neural-networks reproduction reproduction-code splicing variant-effect-prediction

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Implementation of SpliceAI, Illumina's deep neural network to predict variant effects on splicing, in PyTorch.

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README 

        
# spliceai-pytorch



![model](img/banner.png)



Implementation of SpliceAI, Illumina's deep neural network to predict variant effects on splicing, in PyTorch. You can find the Illumina's official implementation [here](https://github.com/Illumina/SpliceAI).



## Installation



```bash

pip install spliceai-pytorch

```



## Usage

```python

import torch

from spliceai_pytorch import SpliceAI



model_80nt = SpliceAI.from_preconfigured('80nt')

# model_400nt = SpliceAI.from_preconfigured('400nt')

# model_2k = SpliceAI.from_preconfigured('2k')

# model_10k = SpliceAI.from_preconfigured('10k')



# ... training ...



x = torch.randn([1, 4, 80 + 5000])  # Predicts Donor/Acceptor probs only for core 5000nt region.



probs = model_80nt(x)  # (1, 5000, 3)

```



## Generating train/test sets



First, download 'SpliceAI train code' directory from [here](https://basespace.illumina.com/s/5u6ThOblecrh) and unzip it to `spliceai_train_code` directory.

Also, download human reference genome (version hg19) to `spliceai_train_code/reference` directory.



Then, run the following command to generate train/test sets after moving into `spliceai_train_code/Canonical`.



```bash

# Before running `grab_sequence.sh`,

# make sure that the variable CL_max is configured properly in `constants.py` (80, 400, 2000 or 10000)

chmod 755 grab_sequence.sh

./grab_sequence.sh



# Requires Python 2.7, with numpy, h5py, scikit-learn installed

python create_datafile.py train all  # ~4 miniutes, creates datafile_train_all.h5 (27G)

python create_datafile.py test 0     # ~1 minute, creates datafile_test_0.h5 (2.4G)



python create_dataset.py train all   # ~11 minutes, creates dataset_train_all.h5 (5.4G)

python create_dataset.py test 0      # ~1 minute, creates dataset_test_0.h5 (0.5G)

```



## Training

```shell

$ python -m spliceai_pytorch.train --model 80nt \  # 80nt, 400nt, 2k, 10k

  --train-h5 spliceai_train_code/Canonical/dataset_train_all.h5 \

  --test-h5 spliceai_train_code/Canonical/dataset_test_0.h5 \

  --use-wandb  # Optional, for logging.

```



## Reproduction status (wip)



Currently on the reproduction of Figure 1E. Results are as below, and you can view [model training logs here (W&B)](https://wandb.ai/dohlee/spliceai-pytorch/reports/SpliceAI-reproduction-Single-model---VmlldzozNjAyNTE5?accessToken=mfmsivay143tqauivt18mxvuna3j1s7ff54c6lg749hjuf11r8xnsllj3ecs1okm).



NOTE: Target results are from ensemble of 5 models, while reproduced results are from a single model.



|Model|Top-k acc. (target)|PR-AUC (target)|Top-k acc. (reproduced)|PR-AUC (reproduced)|

|-----|:-----------------:|:-------------:|:---------------------:|:-----------------:|

SpliceAI-80nt|0.57|0.60|0.54355|0.56435|

SpliceAI-400nt|0.90|0.95|0.87265|0.93160|

SpliceAI-2k|0.93|0.97|0.9083|0.9541|

SpliceAI-10k|0.95|0.98|0.9286|0.96475|



## Citation

```bibtex

@article{jaganathan2019predicting,

  title={Predicting splicing from primary sequence with deep learning},

  author={Jaganathan, Kishore and Panagiotopoulou, Sofia Kyriazopoulou and McRae, Jeremy F and Darbandi, Siavash Fazel and Knowles, David and Li, Yang I and Kosmicki, Jack A and Arbelaez, Juan and Cui, Wenwu and Schwartz, Grace B and others},

  journal={Cell},

  volume={176},

  number={3},

  pages={535--548},

  year={2019},

  publisher={Elsevier}

}

```