A contig binning tool from viral metagenomes
Citation (Work in progress)
Arisdakessian C., Nigro O., Stewart G., Poisson G., Belcaid M. CoCoNet: An Efficient Deep Learning Tool for Viral Metagenome Binning
CoCoNet (Composition and Coverage Network) is a binning method for viral metagenomes. It leverages the flexibility and the effectiveness of deep learning models to learn the probability density function of co-occurrence of contigs in the same genome and therefore provides a rigorous probabilistic framework for binning contigs. The derived probability are then used to compute an adjacency matrix for a subset of strategically selected contigs, and infer homogenous clusters representing contigs of the same genome.
CoCoNet is available in the command line. For a list of all the options, open a terminal and run:
python coconet.py run -h
For more details, please see the documentation on ReadTheDocs
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size coconet-binning-0.1.1.tar.gz (27.2 kB)||File type Source||Python version None||Upload date||Hashes View hashes|