Metadata-Version: 2.1
Name: deepnog
Version: 1.2.1
Summary: Deep learning tool for protein orthologous group predictions
Home-page: UNKNOWN
Author: Roman Feldbauer
Author-email: roman.feldbauer@univie.ac.at
License: UNKNOWN
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        # DeepNOG: protein orthologous groups prediction
        
        Assign proteins to orthologous groups (eggNOG 5) on CPUs or GPUs with deep networks.
        DeepNOG is much faster than alignment-based methods,
        while being as accurate as HMMER.
        
        The `deepnog` command line tool is written in Python 3.7+. 
        
        
        ## Installation guide
        
        The easiest way to install DeepNOG is to obtain it from PyPI:
        
        ```pip install deepnog```
        
        Alternatively, you can clone or download bleeding edge versions
        from GitHub and run
        
        ```pip install /path/to/DeepNOG```
        
        If you plan to extend DeepNOG as a developer, run
        
        ```pip install -e /path/to/DeepNOG```
        
        instead.
        
        ## Usage
        
        DeepNOG can be used through calling the above installed `deepnog`
        command with a protein sequence file (FASTA). 
        
        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 or gzipped)
        
        DeepNOG supports protein sequences stored in all file formats listed in
        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)
        - eggNOG 5.0, taxonomic level 2 (bacteria level)
        - eggNOG 5.0, taxonomic level 1236 (Gammaproteobacteria)
        - (for additional 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 (and minimum version)
        
        *  PyTorch 1.2.0
        *  NumPy 1.16.4
        *  pandas 0.25.1
        *  scikit-learn
        *  tensorboard
        *  pyyml
        *  Biopython 1.74
        *  tqdm 4.35.0
        *  pytest 5.1.2 (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
        A research article is currently under review.
        
Keywords: deep-learning neural-networks bioinformatics computational-biology protein-families orthologous-groups orthology eggnog
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3
Description-Content-Type: text/markdown
