https://github.com/Puumanamana/CoCoNet
Tool for unsupervised contig binning from viral metagenomes
https://github.com/Puumanamana/CoCoNet
Last synced: about 2 months ago
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Tool for unsupervised contig binning from viral metagenomes
- Host: GitHub
- URL: https://github.com/Puumanamana/CoCoNet
- Owner: Puumanamana
- License: apache-2.0
- Created: 2019-06-23T01:44:58.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2021-10-22T10:48:07.000Z (over 3 years ago)
- Last Synced: 2025-03-05T16:24:57.424Z (2 months ago)
- Language: Python
- Homepage:
- Size: 6.99 MB
- Stars: 16
- Watchers: 2
- Forks: 1
- Open Issues: 4
-
Metadata Files:
- Readme: README.rst
- License: LICENSE
Awesome Lists containing this project
- awesome-virome - CoCoNet - Neural networks for viral contig identification. [pip] [Python] (Virus and Phage Identification / Metagenome Analysis)
- awesome-virome - CoCoNet - Neural networks for viral contig identification. [pip] [Python] (Virus and Phage Identification / Metagenome Analysis)
README
CoCoNet documentation
=====================
.. image:: https://travis-ci.org/Puumanamana/CoCoNet.svg?branch=master
:target: https://travis-ci.org/Puumanamana/CoCoNet
.. image:: https://codecov.io/gh/Puumanamana/CoCoNet/branch/master/graph/badge.svg
:target: https://codecov.io/gh/Puumanamana/CoCoNet
.. image:: https://readthedocs.org/projects/coconet/badge/?version=latest
:target: https://coconet.readthedocs.io/
.. image:: https://api.codacy.com/project/badge/Grade/552eeafebb52496ebb409f421bd4edb6
:target: https://www.codacy.com/manual/Puumanamana/CoCoNet?utm_source=github.com&utm_medium=referral&utm_content=Puumanamana/CoCoNet&utm_campaign=Badge_Grade
.. image:: https://anaconda.org/bioconda/coconet-binning/badges/version.svg
:target: https://anaconda.org/bioconda/coconet-binning
Citation
---------------------------
Cédric G Arisdakessian, Olivia Nigro, Grieg Steward, Guylaine Poisson, Mahdi Belcaid, CoCoNet: An Efficient Deep Learning Tool for Viral Metagenome Binning, Bioinformatics, 2021;, btab213, https://doi.org/10.1093/bioinformatics/btab213
Description
-----------
CoCoNet (Composition and Coverage Network) is a binning method for viral metagenomes. It leverages deep learning to abstract the modeling of the k-mer composition and the coverage for binning contigs assembled form viral metagenomic data. Specifically, our method uses a neural network to learn from the metagenomic data a flexible function for predicting the probability that any pair of contigs originated from the same genome. These probabilities are subsequently combined to infer bins, or clusters representing the species present in the sequenced samples. Our approach was specifically designed for diverse viral metagenomes, such as those found in environmental samples (e.g., oceans, soil, etc.).
Install
-------
Install latest PyPi release (recommended)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: bash
pip3 install --user numpy
pip3 install --user coconet-binning
For more installation options, see the `documentation `_
Basic usage
-----------
CoCoNet is available as the command line program. For a list of all the options, open a terminal and run:
.. code-block:: bash
coconet run -h
For more details, please see the documentation on `ReadTheDocs `_
Checking the installation
-------------------------
A test dataset is provided in this repository in tests/sim_data. To quickly verify the installation worked, you can simply download the repository and run the test command as follows:
.. code-block:: bash
git clone https://github.com/Puumanamana/CoCoNet
cd CoCoNet
make test
Contribute
----------
- Issue Tracker: `github `__
- Source Code: `github `__