Metadata-Version: 2.1
Name: deepnog
Version: 1.2.3
Summary: Deep learning tool for protein orthologous group assignment
Home-page: UNKNOWN
Author: Roman Feldbauer
Author-email: roman.feldbauer@univie.ac.at
License: UNKNOWN
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        # DeepNOG: protein orthologous groups assignment
        
        Assign proteins to orthologous groups (eggNOG 5) on CPUs or GPUs with deep networks.
        DeepNOG is much faster than alignment-based methods,
        providing accuracy similar to HMMER.
        
        
        ## Installation guide
        
        The easiest way to install DeepNOG is to obtain it from PyPI:
        ``` bash
        pip install deepnog
        ```
        
        Alternatively, you can clone or download bleeding edge versions
        from GitHub and run
        ``` bash
        pip install /path/to/DeepNOG
        ```
        
        If you plan to extend DeepNOG as a developer, run
        ``` bash
        pip install -e /path/to/DeepNOG
        ```
        
        instead.
        
        ``deepnog`` can also be installed from bioconda like this:
        ``` bash
        conda config --add channels pytorch
        conda install pytorch deepnog
        ```
        
        ## Usage
        
        Call the `deepnog` command line tool with a
        protein sequence file in FASTA format.
        Example usages: 
        
        *  `deepnog infer proteins.faa`
            * Predicted groups of proteins in proteins.faa will be written to the console.
              By default, eggNOG5 bacteria level is used.
        *  `deepnog infer proteins.faa --out prediction.csv`
            * Write into prediction.csv instead
        *  `deepnog infer proteins.faa -db eggNOG5 -t 1236 -V 3 -c 0.99`
            * Predict EggNOG5 Gammaproteobacteria (tax 1236) groups
            * discard individual predictions below 99 % confidence
            * Show detailed progress report (-V 3)
        *  `deepnog train train.fa val.fa train.csv val.csv -a deepnog -e 15 --shuffle
                         -r 123 -db eggNOG5 -t 3 -o /path/to/outdir`
            * Train a model for the (hypothetical) tax level 3 of eggNOG5 with a fixed
              random seed for reproducible results.
        
        
        The individual models for OG predictions are not stored on GitHub or PyPI,
        because they exceed file size limitations (up to 200M).
        `deepnog` automatically downloads the models, and puts them into a
        cache directory (default `~/deepnog_data/`). You can change this directory
        by setting the `DEEPNOG_DATA` environment variable.
        
        For help and advanced options, call `deepnog --help`,
        and `deepnog infer --help` or `deepnog train --help` for specific options
        for inference or training, respectively.
        See also the [user & developer guide](doc/guide.pdf).
        
        ## File formats supported
        
        Preferred: FASTA (raw, .gz, or .xz)
        
        DeepNOG supports protein sequences stored in all file formats listed in
        [https://biopython.org/wiki/SeqIO](https://biopython.org/wiki/SeqIO),
        but is tested for the FASTA-file format
        only.
        
        ## Databases currently supported
        
        - eggNOG 5.0
          * taxonomic level 1 (root level)
          * taxonomic level 2 (bacteria level)
          * For >100 additional eggNOG 5.0 levels, consult the
          [docs](https://deepnog.readthedocs.io/en/latest/documentation/models.html).
        - COG 2020
        - (for additional databases/levels, please create an issue on Github,
           or train a model yourself---new in v1.2)
        
        ## Deep network architectures currently supported
        
        * DeepNOG
        * DeepFam (no precomputed model currently available)
        
        
        ## Required packages
        
        ``deepnog`` builds upon the following packages:
        *  PyTorch
        *  NumPy
        *  pandas
        *  scikit-learn
        *  tensorboard
        *  Biopython
        *  PyYAML
        *  tqdm
        *  pytest (for tests only)
        
        See also `requirements/*.txt` for platform-specific recommendations
        (sometimes, specific versions might be required due to platform-specific
        bugs in the deepnog requirements)
        
        ## Acknowledgements
        This research is supported by the Austrian Science Fund (FWF): P27703, P31988;
        and by the GPU grant program of Nvidia corporation.
        
        ## Citation
        If you use DeepNOG, please consider citing our research article ([click here for bibtex](https://academic.oup.com/Citation/Download?resourceId=6050698&resourceType=3&citationFormat=2)):
        
        Roman Feldbauer, Lukas Gosch, Lukas Lüftinger, Patrick Hyden,
        Arthur Flexer, Thomas Rattei,
        DeepNOG: Fast and accurate protein orthologous group assignment,
        *Bioinformatics*, 2020, btaa1051, https://doi.org/10.1093/bioinformatics/btaa1051
        
Keywords: deep-learning neural-networks bioinformatics computational-biology protein-families orthologous-groups orthology eggnog
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Classifier: Programming Language :: Python :: 3.7
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