预测已知细菌新菌株的致病潜力。
deepacstrain的Python项目详细描述
DeePaC应变
DeePaC菌株是DeePaC(见下文)航运内置模型的插件,用于预测已知细菌种类的新菌株的致病潜力。在
迪帕克
DeePaC是一个python包和一个CLI工具,用于从短DNA序列(例如Illumina)预测标签(例如致病潜能 读)与可解释的反向补足神经网络。有关详细信息,请参阅bioRxiv的预印本: https://www.biorxiv.org/content/10.1101/535286v3和{em1}$生物信息学的论文。 有关DeePaC可解释性功能的详细信息,请参阅此处的预印本:https://www.biorxiv.org/content/10.1101/2020.01.29.925354v2
可在此处找到文档: https://rki_bioinformatics.gitlab.io/DeePaC/。在
安装
使用Bioconda(推荐)
您可以使用bioconda
安装DeePaC。首先设置bioconda channel(通道顺序很重要):
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
我们建议设置一个独立的conda
环境:
然后:
# For GPU support (recommended)
conda install tensorflow-gpu deepacvir
# Basic installation (CPU-only)
conda install deepacvir
有pip
我们建议设置一个独立的conda
环境(见上文)。或者,您可以使用virtualenv
虚拟环境(注意deepac需要python3):
# 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
然后可以使用pip
安装DeePaC。对于GPU支持,您需要先手动安装CUDA和CuDNN(有关详细信息,请参阅TensorFlow安装指南)。
然后您可以执行上述操作:
# For GPU support (recommended)
pip install tensorflow-gpu
pip install deepacvir
或者,如果不需要GPU支持:
# Basic installation (CPU-only)
pip install deepacvir
使用
DeePaC应变可完全用作DeePaC的基础版本。要使用插件,请用deepac
命令替换deepac-strain
。
访问https://gitlab.com/rki_bioinformatics/DeePaC获取描述基本用法的DeePaC自述文件。在
例如,可以使用以下命令:
# See help
deepac-strain --help
# Run quick tests (eg. on CPUs)
deepac-strain test -q
# Full tests
deepac-strain test -a
# Predict using a rapid CNN (trained on VHDB data)
deepac-strain predict -r input.fasta
# Predict using a sensitive LSTM (trained on VHDB data)
deepac-strain predict -s input.fasta
更多示例可在https://gitlab.com/rki_bioinformatics/DeePaC上找到。在
补充数据和脚本
在DeePaC主存储库(https://gitlab.com/rki_bioinformatics/DeePaC)中,您可以找到论文中用于数据集预处理和基准测试的R脚本和数据文件。在
引用我们
如果您认为DeePaC有用,请引用:
@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}
}
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