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Allows users to explore the pairwise interactions (positive or negative) that occur in a microbial network. From an association network and 16S rDNA sequence data, MMinte identifies corresponding genomes, reconstructs metabolic models, estimates growth under specific metabolic conditions, analyzes pairwise interactions, assigns interaction types to network links, and generates the corresponding network of interactions. This pipeline is composed of 7 widgets that may be run sequentially or individually. MMinte is a fundamental tool for exploring a large number of interactions, allowing researchers to move beyond the use of statistical measures of association into biologically relevant analysis of interactions between the species in a microbiome.

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MMinte classification

MMinte specifications

Software type:
Pipeline
Restrictions to use:
None
Output data:
Network diagram representing the interactions between the pairs of species.
Programming languages:
Python
Computer skills:
Advanced
Stability:
Stable
Interface:
Command line interface
Input format:
TXT, FASTA
Operating system:
Unix/Linux, Windows
License:
BSD 3-clause “New” or “Revised” License
Version:
1.3
Requirements:
Python, Biopython, CherryPy, Cobra, Cycler, DataSpyre, Jinja2, MarkupSafe, Numpy, Pandas, Pyparsing, Python-dateutil, Pytz, Scipy, Six, Wheel
Issue URL:
https://github.com/mendessoares/MMinte/issues

MMinte support

Documentation

Maintainer

  • Nicholas Chia <>

Credits

Publications

Institution(s)

Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Chinahester, MN, USA; Department of Surgery, Mayo Clinic, Chinahester, MN, USA; Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Chinahester, MN, USA; Harvard Medical School, Boston, MA, USA; Microbiome Program, Center for Individualized Medicine, Mayo Clinic, Chinahester, MN, USA; Department of Surgery, Mayo Clinic, Chinahester, MN, USA; Department of Physiology and Biomedical Engineering, Mayo College, Chinahester, MN, USA

Funding source(s)

This work was supported by the Mayo Clinic Center for Individualized Medicine and the National Institutes of Health under award number R01CA179243.

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