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deepnog-1.2.3
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مشاهده بیشترتوضیحات
Deep learning tool for protein orthologous group assignment
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepnog-1.2.3 |
| نام | deepnog |
| نسخه کتابخانه | 1.2.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Roman Feldbauer |
| ایمیل نویسنده | roman.feldbauer@univie.ac.at |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/deepnog/ |
| مجوز | - |
[](
https://ci.appveyor.com/project/VarIr/deepnog/branch/master)
[](
https://codecov.io/gh/univieCUBE/deepnog)
[](
https://lgtm.com/projects/g/univieCUBE/deepnog/context:python)
[](
https://deepnog.readthedocs.io/en/latest/?badge=latest)
[](
https://badge.fury.io/py/deepnog)
[](
https://anaconda.org/bioconda/deepnog)
# 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
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | pandas |
| - | scikit-learn |
| >=1.2 | torch |
| - | Biopython |
| - | PyYAML |
| - | tqdm |
| - | tensorboard |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3 | Python |
نحوه نصب
نصب پکیج whl deepnog-1.2.3:
pip install deepnog-1.2.3.whl
نصب پکیج tar.gz deepnog-1.2.3:
pip install deepnog-1.2.3.tar.gz