DeeplyEssential

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DeeplyEssential

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Deep neural network based prediction model for gene essentiality prediction in microbes

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# DeeplyEssential

DeeplyEssential is a Deep neural network for the identification of Essential genes in bacteria species. The dataset used for the learning of the nework contains 30 bacterial species collected from DEG.

<h3>Dependency </h3>

1. Python 2.7
2. keras==2.1.5
3. numpy==1.14.2
4. pandas==0.22.0
5. scikit-learn==0.19.1
6. tensorflow==1.6.0

<h3>GPU </h3>
Titan GTX 1080 Ti

<h3>Parameters</h3>

DeeplyEssential takes 6 parameters
1. Essential gene directory path. The directory contains
	- A essential gene sequence file
	- A essential protein sequence file
	- An gene annotation file
2. Non Essential gene directory path. This directory contains
	- A essential gene sequence file
	- A essential protein sequence file
	- An gene annotation file
3. Clustered gene file path clusted by OrthoMCL (sample given, `orthoMCL.txt`)
4. Text file containing bacteria species information (sample given, `dataset.txt`)
5. Experiment option
	- '-gp' for Gram Positive (GP) Dataset
	- '-gn' for Gram Negative (GN) Dataset
	- '-c' for GP + GN Dataset 
6. Name of the experiment

```
$ python main.py <essential gene dir> <non-essential gene dir> <cluster gene file> <dataset> -c <experiment name>
```

The dataset are collected from [DEG](http://www.essentialgene.org/). 
Update: The dataset from current version of DEG may have been changed. Download the exact data that was used for this project from here. (https://drive.google.com/drive/folders/1zhtTP164Ae6MVHrB7A38z8C48tSe0W83?usp=sharing)

<h4>Output</h4>

DeeplyEssential generates a report containing experiment name, basic statistics about the dataset and evaluation metics for each iteration of experiment. A sample (`sample_output.tab`) is provided.

DeeplyEssential

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