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NetCooperate | A network-based tool for inferring host-microbe and microbe-microbe cooperation

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A package for determining host-microbe and microbe-microbe cooperative potential. NetCooperate specifically calculates two previously developed and validated metrics for species interaction: the Biosynthetic Support Score and Metabolic Complementarity Index, which provide insight into host-microbe and microbe-microbe metabolic interactions. NetCooperate determines these interaction indices from metabolic network topology, and can be used for small- or large-scale analyses.

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NetCooperate classification

NetCooperate specifications

Unique identifier:
OMICS_12613
Interface:
Command line interface
Input data:
The metabolic networks of two species, each encoded as a directed graph with nodes representing compounds and edges connecting substrates to products.
Operating system:
Unix/Linux, Mac OS, Windows
License:
GNU General Public License version 3.0
Stability:
Stable
Software type:
Package/Module
Restrictions to use:
None
Output data:
The pairwise metrics as well a list of metabolic compounds complementary between the two species.
Programming languages:
Python
Computer skills:
Advanced
Maintained:
Yes

NetCooperate distribution

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NetCooperate classification

NetCooperate specifications

Unique identifier:
OMICS_12613
Restrictions to use:
None
Programming languages:
Python
Computer skills:
Basic
Maintained:
Yes
Interface:
Web user interface
Input data:
The metabolic networks of two species, each encoded as a directed graph with nodes representing compounds and edges connecting substrates to products.
License:
GNU General Public License version 3.0
Stability:
Stable

NetCooperate support

Documentation

Maintainer

  • Elhan Borenstein <>
  • Elhan Borenstein <>

Credits

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Publications

Institution(s)

Department of Genome Sciences, University of Washington, Seattle, WA, USA; Department of Electrical Engineering & Computer Science, Center for Computational Biology, UC Berkeley, Berkeley, CA, USA; Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, CA, USA; Newe Ya’ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel; Department of Computer Science and Engineering, University of Washington, Seattle, WA, USA; Santa Fe Institute, Santa Fe, NM, USA

Funding source(s)

This work was supported in part by New Innovator Award DP2 AT007802-01.

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