Antibiotic resistance is turning into a global healthcare problem. The exponential growth of metagenomics data has contributed to creation of more accurate and fastest algorithms.
DeepARG is a machine learning solution that uses deep learning to characterize and annotate antibiotic resistance genes in metagenomes. It is composed of two models for two types of input: short sequence reads and gene-like sequences.
DeepARG is able to annotate short sequence reads from Next Generation Sequencing (NGS) technologies such as Ilummina. This model has been trained with simulated antibiotic resistance reads to perform better with metagenomic samples.
DeepARG is able to predict antibiotic resistance in long gene-like sequences. This model is suitable for annotating full sequence genes and to discover novel antibiotic resistance genes from assembled samples.
Important! DeepARG-DB has been under continuous inspection in order to improve the quality of the models. As result, we created a new online resource called ARGminer that allows the manual inspection of ARGs.
For more information please visit the ARGminer website
Figure 1: Automatic annotation of highly homologous ARGs
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.
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.
DeepARG requires the next python modules (all can be installed via pip):
Open a terminal and clone the source code:
git clone https://bitbucket.org/gusphdproj/deeparg-ss
Go to the directory where the program was saved and open the file
path = '/home/gustavo1/tmp/deeparg-ss/'; with the current directory (deepARG path).
For instance, deepARG was cloned at
The options.py file should looks like
path = '/home/user/deeparg-ss/';
./bin under deeparg-ss and run
chmod +x diamond (only for LINUX)
Please visit our repository for details