# Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

### Jack Lanchantin, Ritambhara Singh, Beilun Wang, and Yanjun Qi

### Pacific Symposium on Biocomputing (PSB) 2017

https://arxiv.org/abs/1608.03644

### Talk slides:

https://github.com/QData/DeepMotif/blob/master/psb_talk_slides.pdf

### bibtex:

```

@inproceedings{lanchantin2017deep,

  title={Deep motif dashboard: Visualizing and understanding genomic sequences using deep neural networks},

  author={Lanchantin, Jack and Singh, Ritambhara and Wang, Beilun and Qi, Yanjun},

  booktitle={PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017},

  pages={254--265},

  year={2017},

  organization={World Scientific}

}

```

[![LICENSE](https://img.shields.io/badge/license-MIT-brightgreen.svg)](https://github.com/QData/DeepMotif/blob/master/LICENSE)

# Installation

## Lua setup

The main modeling code is written in Lua using [torch](http://torch.ch)

Installation instructions are located [here](http://torch.ch/docs/getting-started.html#_)

After installing torch, install / update these packages by running the following:

```bash

luarocks install torch

luarocks install nn

luarocks install optim

```

### CUDA support (Optional)

To enable GPU acceleration with [CUDA](https://developer.nvidia.com/cuda-downloads), you'll need to install CUDA 6.5 or higher as well as [cutorch](https://github.com/torch/cutorch) and [cunn](https://github.com/torch/cunn). You can install / update the torch CUDA libraries by running:

```bash

luarocks install cutorch

luarocks install cunn

```

## LFS

Install git large file storage (LFS) in order to download the dataset directly from this git repository.

https://git-lfs.github.com/

## Visualization Method Dependencies

Weblogo: http://weblogo.berkeley.edu/

R: https://www.r-project.org/

# Usage

## Step 1: Get the Data

tar xvzf data/deepbind.tar.gz -C data/

## Step 2: Train the model

You can train one of the 3 types of models (CNN, RNN, or CNN-RNN). Check the flags in main.lua for parameters to run the code.

For CNN model:

```bash

th main.lua -cnn

```

For CNN model:

```bash

th main.lua -rnn

```

For CNN-RNN model:

```bash

th main.lua -cnn -rnn

```

## Step 3: Visualize the Model's Predictions

Once you have trained models, you can visualize the predictions. 

Saliency Map

```bash

th saliency_map.lua

```

Temporal Output Values

```bash

th temporal_output_values.lua

```

Class Optimization

```bash

th class_optimization.lua

```