Metadata-Version: 2.1
Name: hkgfinder
Version: 0.1.0
Summary: find housekeeping genes in prokaryotic (meta)genomic data
Home-page: https://github.com/Ebedthan/hkgfinder
License: MIT
Keywords: bioinformatics
Author: Anicet Ebou
Author-email: anicet.ebou@gmail.com
Requires-Python: >=3.8,<4.0
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Dist: bio (>=1.3.9,<2.0.0)
Requires-Dist: pyfastx (>=0.8.4,<0.9.0)
Requires-Dist: xphyle (>=4.4.2,<5.0.0)
Project-URL: Documentation, https://github.com/Ebedthan/hkgfinder
Project-URL: Repository, https://github.com/Ebedthan/hkgfinder
Description-Content-Type: text/markdown

# hkgfinder

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## Find housekeeping genes in prokaryotic (meta)genomes

### Introduction
hkgfinder is a fast and accurate housekeeping gene finder and classifier. Hkgfinder can run on raw sequences, genomes and metagenomes. The novel value of this method lies is in its ability to directly predict and classify gene sequences into housekeeping gene families at a high specificity and sensitivity, while being also faster than genome and metagenome annotator on genome and metagenome data.

### How hkgfinder works
![](img/hkgfinder.png)

### Installation

You will have first to install [Prodigal](https://github.com/hyattpd/Prodigal) and [HMMER 3](https://hmmer.org) to be able to run hkgfinder.


#### Install from Pip

```bash
pip install hkgfinder
```


#### Install from source

```bash
# Download hkgfinder development version
git clone https://github.com/Ebedthan/hkgfinder.git hkgfinder

# Navigate to directory
cd hkgfinder

# Install with poetry: see https://python-poetry.org
poetry install --no-dev

# Enter the Python virtual environment with
poetry shell

# Test hkgfinder is correctly installed
hkgfinder -h
```

If you do not want to go into the virtual environment just do:

```bash
poetry run hkgfinder -h
```

## Test

* Type `hkgfinder -h` and it should output something like:

```
usage: hkgfinder [options] [<FILE>]

optional arguments:
  -o [FILE]      output result to FILE [stdout]
  -g             activate genome mode [false]
  -m             activate metagenome mode [false]
  --faa FILE     output matched proteins sequences to FILE
  --fna FILE     output matched DNA sequences to FILE
  -t INT         number of threads [1]
  -q             decrease program verbosity
  -v, --version  show program's version number and exit
  -h, --help     show this help message and exit
```


## Invoking hkgfinder

```
hkgfinder --faa housekeeping.faa --fna housekeeping.fna file.fa.gz
```

* hkgfinder supports gzip, lzma, bz2 and zstd compressed files.
  
## Bugs

Submit problems or requests to the [Issue Tracker](https://github.com/Ebedthan/hkgfinder/issues).


## Dependencies

### Mandatory

* [**Prodigal**](https://github.com/sib-swiss/pftools3)  
  Used for protein-coding gene prediction.    
  *Hyatt, D., Chen, GL., LoCascio, P.F. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010). https://doi.org/10.1186/1471-2105-11-119*

* [**HMMER 3**](https://hmmer.org)  
  Used for HMM profile prediction.   
  *Eddy SR, Accelerated Profile HMM Searches. PLOS Computational Biology 2011, 10.1371/journal.pcbi.1002195*


## Licence

[MIT](https://github.com/Ebedthan/hkgfinder/blob/main/LICENSE).


## Author

* [Anicet Ebou](https://orcid.org/0000-0003-4005-177X)


