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.

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.