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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
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 latest PyPi release (recommended) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. code-block:: bash
pip3 install --user numpy pip3 install --user coconet-binning
For more installation options, see the documentation <https://coconet.readthedocs.io/getting-started.html>_
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 -hFor more details, please see the documentation on ReadTheDocs <https://coconet.readthedocs.io>_
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
github <https://github.com/Puumanamana/CoCoNet/issues>__github <https://github.com/Puumanamana/CoCoNet>__