MiCoNE - Microbial Co-occurrence Network Explorer

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MiCoNE is a Python package for the exploration of the effects of various possible tools used during the 16S data processing workflow on the inferred co-occurrence networks. It is also developed as a flexible and modular pipeline for 16S data analysis, offering parallelized, fast and reproducible runs executed for different combinations of tools for each step of the data processing workflow. It incorporates various popular, publicly available tools as well as custom Python modules and scripts to facilitate inference of co-occurrence networks from 16S data.

The MiCoNE framework is introduced in:

Kishore, D., Birzu, G., Hu, Z., DeLisi, C., Korolev, K., & Segrè, D. (2023). Inferring microbial co-occurrence networks from amplicon data: A systematic evaluation. mSystems. doi:10.1128/msystems.00961-22.

Data related to the publication can be found on Zenodo: https://doi.org/10.5281/zenodo.7051556.


  • Plug and play architecture: allows easy additions and removal of new tools

  • Flexible and portable: allows running the pipeline on local machine, compute cluster or the cloud with minimal configuration change through the usage of nextflow

  • Parallelization: automatic parallelization both within and across samples (needs to be enabled in the nextflow.config file)

  • Ease of use: available as a minimal Python library (without the pipeline) or as a full conda package

Know issues

  1. If you have a version of julia that is preinstalled, make sure that it does not conflict with the version downloaded by the micone-flashweave environment

  2. The data directory (nf_micone/data) needs to be manually downloaded using this link.

Table of Contents

Indices and tables


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