Metadata-Version: 2.1
Name: deepnog
Version: 1.1.0
Summary: Deep learning tool for protein orthologous group predictions
Home-page: UNKNOWN
Author: Lukas Gosch
Author-email: gosch.lukas@gmail.com
License: UNKNOWN
Keywords: deep-learning bioinformatics neural-networks protein-familiesorthologous-groups 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
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: torch (>=1.2)
Requires-Dist: Biopython
Requires-Dist: tqdm

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# DeepNOG: protein orthologous groups prediction

Predict orthologous groups of proteins on CPUs or GPUs with deep networks.
DeepNOG is both faster and more accurate than assigning OGs with HMMER.

The `deepnog` command line tool is written in Python 3.7+. 

Current version: 1.1.0

## 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 proteins.faa 
    * OGs prediction of proteins in proteins.faa will be written into out.csv
*  deepnog proteins.faa --out prediction.csv
    * Write into prediction.csv instead
*  deepnog proteins.faa --tab
    * Instead of semicolon (;) separated, generate tab separated output-file

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`,
or see 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 supported

- eggNOG 5.0, taxonomic level 1 (root level)
- eggNOG 5.0, taxonomic level 2 (bacteria level)
- (for additional level, please create an issue)

## Neural network architectures supported

*  DeepEncoding (=DeepNOG in the research article)


## Required packages (and minimum version)

*  PyTorch 1.2.0
*  NumPy 1.16.4
*  pandas 0.25.1
*  Biopython 1.74
*  tqdm 4.35.0
*  pytest 5.1.2 (for tests only)

## 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 in preparation.


