Deep Learning Models

The deepARG models have been designed for computational analysis of next generation sequencing data such as Metagenomes. The main contribution of the deepARG models are their low false negative rate during predictions. Also, the gene-like sequences model is designed to find novel ARGs based on the sequence homology.

DeepARG pipeline

The pipeline can be used as an stand alone program. It was developed in python 2.7 and requires (optional) DIAMOND for making the alignments. The source code can be downloaded from this Git Repository hosted in BitBucket.

Dependencies

DeepARG requires the next python modules (all can be installed via pip):

• Nolearn lasagne deep learning library.
• Sklearn machine learning routines.
• Theano for fast computation. For GPU usage (see theano documentation)

Instalation

Open a terminal and clone the source code:

Configuration

• Go to the directory where the program was saved and open the file options.py

  Replace path = '/home/gustavo1/tmp/deeparg-ss/'; with the current directory (deepARG path).
  
  For instance, deepARG was cloned at /home/user/deeparg-ss/
  The options.py file should looks like
  path = '/home/user/deeparg-ss/';

• Go to ./bin under deeparg-ss and run chmod +x diamond (only for LINUX)

Usage

Please visit our repository for details