Package: deepnog
DeepNOG: protein orthologous groups assignmentAssign 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 guideThe easiest way to install DeepNOG is to obtain it from PyPI:pipinstalldeepnogAlternatively, you can clone or download bleeding edge versions
from GitHub and runpipinstall/path/to/DeepNOGIf you plan to extend DeepNOG as a developer, runpipinstall-e/path/to/DeepNOGinstead.deepnogcan also be installed from bioconda like this:condaconfig--addchannelspytorch
condainstallpytorchdeepnogUsageCall thedeepnogcommand line tool with a
protein sequence file in FASTA format.
Example usages:deepnog infer proteins.faaPredicted 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.csvWrite into prediction.csv insteaddeepnog infer proteins.faa -db eggNOG5 -t 1236 -V 3 -c 0.99Predict EggNOG5 Gammaproteobacteria (tax 1236) groupsdiscard individual predictions below 99 % confidenceShow 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/outdirTrain 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).deepnogautomatically downloads the models, and puts them into a
cache directory (default~/deepnog_data/). You can change this directory
by setting theDEEPNOG_DATAenvironment variable.For help and advanced options, calldeepnog --help,
anddeepnog infer --helpordeepnog train --helpfor specific options
for inference or training, respectively.
See also theuser & developer guide.File formats supportedPreferred: FASTA (raw, .gz, or .xz)DeepNOG supports protein sequences stored in all file formats listed inhttps://biopython.org/wiki/SeqIO,
but is tested for the FASTA-file format
only.Databases currently supportedeggNOG 5.0taxonomic level 1 (root level)taxonomic level 2 (bacteria level)For >100 additional eggNOG 5.0 levels, consult thedocs.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 supportedDeepNOGDeepFam (no precomputed model currently available)Required packagesdeepnogbuilds upon the following packages:PyTorchNumPypandasscikit-learntensorboardBiopythonPyYAMLtqdmpytest (for tests only)See alsorequirements/*.txtfor platform-specific recommendations
(sometimes, specific versions might be required due to platform-specific
bugs in the deepnog requirements)AcknowledgementsThis research is supported by the Austrian Science Fund (FWF): P27703, P31988;
and by the GPU grant program of Nvidia corporation.CitationIf you use DeepNOG, please consider citing our research article (click here for bibtex):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