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deepacvir-0.2.2
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مشاهده بیشترتوضیحات
Detecting novel human viruses from DNA reads with reverse-complement neural networks.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepacvir-0.2.2 |
| نام | deepacvir |
| نسخه کتابخانه | 0.2.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jakub Bartoszewicz |
| ایمیل نویسنده | jakub.bartoszewicz@hpi.de |
| آدرس صفحه اصلی | https://gitlab.com/rki_bioinformatics/DeePaC |
| آدرس اینترنتی | https://pypi.org/project/deepacvir/ |
| مجوز | MIT |
<!-- {#mainpage} -->
# DeePaC-vir
DeePaC-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/10.1101/2020.01.29.925354v5>
# DeePaC
DeePaC 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/10.1101/535286v3> and the paper in *Bioinformatics*: <https://doi.org/10.1093/bioinformatics/btz541>.
For details regarding the interpretability functionalities of DeePaC, see the preprint here: <https://www.biorxiv.org/content/10.1101/2020.01.29.925354v2>
Documentation can be found here:
<https://rki_bioinformatics.gitlab.io/DeePaC/>.
See also the main repo here: <https://gitlab.com/rki_bioinformatics/DeePaC>.
## Installation
### With Bioconda (recommended)
[](http://bioconda.github.io/recipes/deepac/README.html)
You can install DeePaC with `bioconda`. Set up the [bioconda channel](
<https://bioconda.github.io/user/install.html#set-up-channels>) first (channel ordering is important):
```
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
```
We recommend setting up an isolated `conda` environment:
```
# python 3.6, 3.7 and 3.8 are supported
conda create -n my_env python=3.8
conda activate my_env
```
and then:
```
# For GPU support (recommended)
conda install tensorflow-gpu deepacvir
# Basic installation (CPU-only)
conda install deepacvir
```
### With pip
We recommend setting up an isolated `conda` environment (see above). Alternatively, you can use a `virtualenv` virtual 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/activate
```
You can then install DeePaC with `pip`. 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 deepacvir
```
Alternatively, if you don't need GPU support:
```
# Basic installation (CPU-only)
pip install deepacvir
```
## Usage
DeePaC-vir may be used exactly as the base version of DeePaC. To use the plugin, substitute the `deepac` command for `deepac-vir`.
Visit <https://gitlab.com/rki_bioinformatics/DeePaC> for 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.fasta
```
More examples are available at <https://gitlab.com/rki_bioinformatics/DeePaC>.
## Supplementary data and scripts
Training, 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 us
If 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}
}
```
نیازمندی
| مقدار | نام |
|---|---|
| >=0.11.0 | deepac |
| >=2.1 | tensorflow |
| >=0.22.1 | scikit-learn |
| >=1.17 | numpy |
| >=3.1.3 | matplotlib |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3 | Python |
نحوه نصب
نصب پکیج whl deepacvir-0.2.2:
pip install deepacvir-0.2.2.whl
نصب پکیج tar.gz deepacvir-0.2.2:
pip install deepacvir-0.2.2.tar.gz