Skip to main content

A contig binning tool from viral metagenomes

Project description

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 run -h

For more details, please see the documentation on ReadTheDocs


Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

coconet-binning-0.2.tar.gz (27.5 kB view hashes)

Uploaded Source

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page