A deep neural network for robust identification and annotation of antibiotic resistance genes.
The input can be long amino acid sequences(full length/contigs), long nucleotide sequences,
short amino acid reads (30-50aa), short nucleotide reads (100-150nt) in fasta format.
If your input is short reads you should assign 'argnet-s' model, or if your input is full-length/contigs
you should assign 'argnet-l' to make the predict.
clone the program to your local machine:
git clone https://github.com/patience111/ARGNet
1. Setting up environment
1.1 Installation with conda
1.1.1 For CPU inference, you could install the program with conda YAML file in the installation directory with the following commands:
cd ./installation
conda env create -f ARGNet-CPU.yml -n ARGNet-cpu
conda activate ARGNet-cpu
(This was tested on Ubuntu 16.04, 20.04; Windows 10, macOS(14.1.1))
1.1.2 For GPU inference, you could install the program with conda YAML file in the installation directory with the following commands:
cd ./installation
conda env create -f ARGNet-GPU.yml -n ARGNet-gpu
conda activate ARGNet-gpu
(This was tested on Ubuntu 16.04, cuda 10.1, Driver Version: 430.64)
We also provide the colab link for users don't have local GPU but also want speeding up by GPU power
plese find the colab link and follow the instruction:
https://colab.research.google.com/drive/1Ui2mVw8zwvwL8uVnjwDreHNAJK5n8RuA#scrollTo=IjDYLkxOgnMo
(Thank Amos, Tracy, and Carlos for creating this link)
1.2 Or, if you prefer installing dependencies manually, you might find this information useful:
The program was tested with the following package version, you can install exactly the same version or other compatible versions.
Biopython: 1.79
tensorflow: 2.2.0
cuda: 10.2 (for GPU using)
cudnn: 7.6.5.32 (for GPU using)
numpy: 1.18.5
scikit-learn: 0.24.1
tqdm: 4.56.0
2. Getting trained models
cd ./model
bash get-models.sh
for long sequences
python argnet.py --input input_path_data --type aa/nt --model argnet-l --outname output_file_name
for short reads
python argnet.py --input input_path_data --type aa/nt --model argnet-s --outname output_file_name
general options:
--input/-i the test file as input
--type/-t molecular type of your test data (aa for amino acid, nt for nucleotide)
--model/-m the model you assign to make the prediction (argnet-l for long sequences, argnet-s for short reads)
--outname/-on the output file name
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
the test data as input
-t {aa,nt}, --type {aa,nt}
molecular type of your input file
-m {argnet-s,argnet-l}, --model {argnet-s,argnet-l}
the model to make the prediction
-on OUTNAME, --outname OUTNAME
the name of results output
if we predict the long amino acid contigs by using ARGNet-L model, we could use command line (if you are in ARGNet dirctory):
python3 ./scripts/argnet.py -i ./tests/aa/long/arg100p.fasta -t aa -m argnet-l -on argnet_lsaa_test.txt
output will be like and saved in the results folder:
the first column test_id is the sequence label of the test sequnece.
the second column ARG_prediction is the "ARG" or "non-ARG" prediction of the input sequence.
the third column resistance_category is the classifition of the 36 antibiotics categories of the input sequence resisting to.
the last column probability is the classifition probability of the antibiotic predition of the input sequence by the model.
Pei, Y., Shum, M.HH., Liao, Y. et al. ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences. Microbiome 12, 84 (2024). https://doi.org/10.1186/s40168-024-01805-0
If you'd like to contribute to ARGNet, check out https://github.com/patience111/argnet
Hope you enjoy ARGNet journey, any problem please contact scpeiyao@gmail.com
