Package: deepacvir
DeePaC-virDeePaC-vir is a plugin for DeePaC (see below) shipping built-in models for novel human virus detection directly from NGS reads.
For details, see our preprint on bioRxiv:https://www.biorxiv.org/content/10.1101/2020.01.29.925354v5DeePaCDeePaC is a python package and a CLI tool for predicting labels (e.g. pathogenic potentials) from short DNA sequences (e.g. Illumina
reads) with interpretable reverse-complement neural networks. For details, see our preprint on bioRxiv:https://www.biorxiv.org/content/10.1101/535286v3and the paper inBioinformatics:https://doi.org/10.1093/bioinformatics/btz541.
For details regarding the interpretability functionalities of DeePaC, see the preprint here:https://www.biorxiv.org/content/10.1101/2020.01.29.925354v2Documentation can be found here:https://rki_bioinformatics.gitlab.io/DeePaC/.
See also the main repo here:https://gitlab.com/rki_bioinformatics/DeePaC.InstallationWith Bioconda (recommended)You can install DeePaC withbioconda. Set up thebioconda channelfirst (channel ordering is important):conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forgeWe recommend setting up an isolatedcondaenvironment:# python 3.6, 3.7 and 3.8 are supported
conda create -n my_env python=3.8
conda activate my_envand then:# For GPU support (recommended)
conda install tensorflow-gpu deepacvir
# Basic installation (CPU-only)
conda install deepacvirWith pipWe recommend setting up an isolatedcondaenvironment (see above). Alternatively, you can use avirtualenvvirtual environment (note that deepac requires python 3):# use -p to use the desired python interpreter (python 3.6 or higher required)
virtualenv -p /usr/bin/python3 my_env
source my_env/bin/activateYou can then install DeePaC withpip. For GPU support, you need to install CUDA and CuDNN manually first (see TensorFlow installation guide for details).
Then you can do the same as above:# For GPU support (recommended)
pip install tensorflow-gpu
pip install deepacvirAlternatively, if you don't need GPU support:# Basic installation (CPU-only)
pip install deepacvirUsageDeePaC-vir may be used exactly as the base version of DeePaC. To use the plugin, substitute thedeepaccommand fordeepac-vir.
Visithttps://gitlab.com/rki_bioinformatics/DeePaCfor a DeePaC readme describing basic usage.For example, you can use the following commands:# See help
deepac-vir --help

# Run quick tests (eg. on CPUs)
deepac-vir test -q
# Full tests
deepac-vir test -a

# Predict using a rapid CNN (trained on VHDB data)
deepac-vir predict -r input.fasta
# Predict using a sensitive LSTM (trained on VHDB data)
deepac-vir predict -s input.fastaMore examples are available athttps://gitlab.com/rki_bioinformatics/DeePaC.Supplementary data and scriptsTraining, validation and test datasets are available here:https://doi.org/10.5281/zenodo.3630803.
In the main DeePaC repository (https://gitlab.com/rki_bioinformatics/DeePaC) you can find the R scripts and data files used in the papers for dataset preprocessing and benchmarking.Cite usIf you find DeePaC useful, please cite:@article{10.1093/bioinformatics/btz541,
    author = {Bartoszewicz, Jakub M and Seidel, Anja and Rentzsch, Robert and Renard, Bernhard Y},
    title = "{DeePaC: predicting pathogenic potential of novel DNA with reverse-complement neural networks}",
    journal = {Bioinformatics},
    year = {2019},
    month = {07},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btz541},
    url = {https://doi.org/10.1093/bioinformatics/btz541},
    eprint = {http://oup.prod.sis.lan/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btz541/28971344/btz541.pdf},
}

@article {Bartoszewicz2020.01.29.925354,
    author = {Bartoszewicz, Jakub M. and Seidel, Anja and Renard, Bernhard Y.},
    title = {Interpretable detection of novel human viruses from genome sequencing data},
    elocation-id = {2020.01.29.925354},
    year = {2020},
    doi = {10.1101/2020.01.29.925354},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2020/02/01/2020.01.29.925354},
    eprint = {https://www.biorxiv.org/content/early/2020/02/01/2020.01.29.925354.full.pdf},
    journal = {bioRxiv}
}