DeepSV

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Calling deletions using deep convolutional neural

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DeepSV
===
![DeepSV](https://github.com/CSuperlei/DeepSV/raw/master/Pic/DeepSV.png)
## Introduction
DeepSV, an approach based on deep learning for calling long deletions from sequence reads.DeepSV is based on a novel method of visualizing sequence reads. The visualization is designed to capture multiple sources of information in the data that are relevant to long deletions. DeepSV also implements techniques for working with noisy training data. DeepSV trains a model from the visualized sequence reads and calls deletions based on this model. We demonstrate that DeepSV outperforms existing methods in terms of accuracy and efficiency of deletion calling on the data from the 1000 Genomes Project. Our work shows that deep learning can potentially lead to effective calling of different types of genetic variations that are complex than SNPs. 
![WorkFlow](https://github.com/CSuperlei/DeepSV/raw/master/Pic/WorkFlow.png)
## Requirements
  * python 3.6, Jupyter Notebook, numpy, scipy, pandas, Matplotlib
  * Cuda 8.0, Cudnn
  * TensorFlow
  * Digits
  * Pysam

## Installation
### Tools
 bash Anaconda3-4.3.1-Linux-x86_64.sh 
### Jupyter Notebook
 * pip install jupyter
 * Configure 
    jupyter notebook --generate-config 
 Create a ciphertext password:
 from notebook.auth import passwd
 * Modify the default configuration file 
 c.NotebookApp.ip='*' 
	 c.NotebookApp.password = u'sha:...' 
	 c.NotebookApp.open_browser = False 
	 c.NotebookApp.port =8888 
	 c.NotebookApp.notebook_dir = u’/home/...’ 
 
### Cuda & cudnn
 Installation tutorial can be downloaded from the official website
 
### TensorFlow
* pip install tensorflow-gpu

### Digits
    cd ~
    git clone https://github.com/NVIDIA/DIGITS.git digits
    cd digits
    sudo apt-get install graphviz gunicorn
    for req in $(cat requirements.txt); do sudo pip install $req; done 
    pip install -r ~/digits/requirements.txt 
    ./digits-devserver

### pysam
* pip install pysam

## Usage
### Data
BAM file & VCF file 
First provide the bam files and vcf files for program 
### Generation Candidates
Run Generate_Deletion_Image.py and Generate_Non_Deletion_Image.py in the custom path 
* python Generate_Deletion_Image.py --del_length 
* python Generate_Non_Deletion_Image.py --del_length

### Geerationg Images Path
Generate the path of all pictures for training the network
* python my_file_travel.py

### Using Digits training CNN
Send all the generated pictures to the network training
* Using the CNN architecture in CNN_Source.py

### Using a trained network for calling deletion
Generating whole genome pictures
* python Whole_genome_Image.py

### Extracting deletion information from test results
* python extract_breakpoint.py

### Generating VCF File
* python generate_final_vcf.py

DeepSV

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