Metadata-Version: 2.1
Name: deepacstrain
Version: 0.2.0
Summary: Predicting pathogenic potentials of novel strains of known bacterial species.
Home-page: https://gitlab.com/rki_bioinformatics/DeePaC
Author: Jakub Bartoszewicz
Author-email: jakub.bartoszewicz@hpi.de
License: MIT
Description: <!-- {#mainpage} -->
        
        # DeePaC-strain
        
        DeePaC-strain is a plugin for DeePaC (see below) shipping built-in models for predicting pathogenic potentials of novel strains of known bacterial species.
        
        # 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/>.
        
        
        ## Installation
        
        ### Recommended: set up an environment
        
        We recomment setting up an isolated `conda` environment:
        ```
        conda create -n my_env python=3.6
        conda activate my_env
        ```
        
        or, alternatively, a `virtualenv`:
        ```
        virtualenv --system-site-packages my_env
        source my_env/bin/activate
        ```
        
        
        ### With conda (recommended)
         [![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/deepacstrain/README.html)
         
        You can install DeePaC-strain with `bioconda`. Set up the [bioconda channel](
        <https://bioconda.github.io/user/install.html#set-up-channels>) first, and then:
        ```
        conda install deepacstrain
        ```
        
        DeePaC will be installed automatically.
        
        ### With pip
        
        You can also install DeePaC-strain with `pip`:
        ```
        pip install deepacstrain
        ```
        Note: TensorFlow 2.0 is not yet supported.
        
        ### GPU support
        
        To use GPUs, you need to install the GPU version of TensorFlow. In conda, install tensorflow-gpu from the `defaults` channel before deepac:
        ```
        conda remove tensorflow
        conda install -c defaults tensorflow-gpu=1.15 
        conda install deepacstrain
        ```
        DeePaC will be installed automatically. Note: TensorFlow 2.0 is not yet supported.
        
        If you're using `pip`, you need to install CUDA and CuDNN first (see TensorFlow installation guide for details). Then
        you can do the same as above:
        ```
        pip uninstall tensorflow
        pip install tensorflow-gpu==1.15
        ```
        
        ## Usage
        DeePaC-strain may be used exactly as the base version of DeePaC. To use the plugin, substitute the `deepac` command for `deepac-strain`.
        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-strain --help
        
        # Run quick tests (eg. on CPUs)
        deepac-strain test -q
        # Full tests on a GPU
        deepac-strain test -a -g 1
        
        # Predict using a rapid CNN (trained on VHDB data) using a GPU
        deepac-strain predict -r -g 1 input.fasta
        # Predict using a sensitive LSTM (trained on VHDB data) using a GPU
        deepac-strain predict -s -g 1 input.fasta
        ```
        
        ## Supplementary data and scripts
        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}
        }
        
        ```
Keywords: deep learning DNA sequencing synthetic biology pathogenicity prediction
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3
Description-Content-Type: text/markdown
