Metadata-Version: 2.1
Name: cocopye
Version: 0.3
Summary: Feature-based prediction of genome quality indices
Author: Niklas Birth, Nicolina Leppich, Dr. Peter Meinicke
Maintainer-email: Niklas Birth <birth@posteo.de>
License: GPL-3.0-or-later
Project-URL: Homepage, https://cocopye.uni-goettingen.de
Project-URL: Source, https://github.com/gobics/cocopye
Project-URL: Documentation, https://github.com/gobics/cocopye/wiki
Keywords: bioinformatics,biology,microbiology,metagenomics,genome,genome quality
Classifier: Programming Language :: Python :: 3
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Development Status :: 4 - Beta
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: COPYING
Requires-Dist: numpy>=1.23
Requires-Dist: numba>=0.56.4
Requires-Dist: biopython==1.81
Requires-Dist: scikit-learn==1.3.1
Requires-Dist: pandas
Requires-Dist: packaging
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Requires-Dist: tomlkit~=0.11.6
Requires-Dist: requests~=2.31.0
Requires-Dist: tqdm~=4.65.0
Requires-Dist: numba-progress==1.0.0
Provides-Extra: web
Requires-Dist: celery[redis]~=5.3.1; extra == "web"
Requires-Dist: uvicorn~=0.22.0; extra == "web"
Requires-Dist: fastapi~=0.99.1; extra == "web"
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Requires-Dist: Jinja2~=3.1.2; extra == "web"

# CoCoPyE

**CoCoPyE is a fast tool for quality assessment of microbial genomes. It is able to reliably predict
completeness and contamination of bacterial and archaeal genomes. Additionally, it can provide a
taxonomic classification of the input.**

Background: The classical approach for estimation of quality indices solely relies on a relatively small
number of universal single copy genes. Because these classical markers only cover a small fraction of the
whole genome the quality assessment can be rather unreliable. Our method is based on a novel
two-stage feature extraction and transformation scheme. It first performs a flexible extraction
of genomic markers and then refines the marker-based estimates with a machine learning approach based on
count-ratio histograms. In our simulation studies CoCoPyE showed a more accurate prediction of  quality
indices than existing tools.

## Getting started

CoCoPyE is available via pip and conda (conda-forge channel). See the [project wiki](https://github.com/gobics/cocopye/wiki)
for installation and usage instructions.

- [Quickstart](https://github.com/gobics/cocopye/wiki/Quickstart)
- [Installation](https://github.com/gobics/cocopye/wiki/Installation)
- [Usage](https://github.com/gobics/cocopye/wiki/Usage)

### Online Demo

You can test CoCoPyE without installation on [our project homepage](https://cocopye.uni-goettingen.de). Please note that the online demo can process only 
one query genome per request and is less performant than a local installation. Therefore it is highly recommended to use the online
version only for evaluation purposes and install CoCoPyE on your own machine for productive use.

## Additional notes

### Contact

For bug reports, suggestions or questions, please open an issue on [GitHub](https://github.com/gobics/cocopye/issues)
or send an email to [pmeinic@gwdg.de](mailto:pmeinic@gwdg.de).

### API documentation

You can find the API documentation of the CoCoPyE package on [https://gobics.github.io/cocopye](https://gobics.github.io/cocopye).

### License

CoCoPyE is available under the terms of the GNU General Public License, version 3 or later. See [`COPYING`](https://github.com/gobics/cocopye/blob/master/COPYING) for the full license text.
