MiCoNE, is a flexible and modular pipeline for 16S data analysis. 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.

• Free software: MIT license
• Documentation: https://micone.readthedocs.io/

Manuscript can be found on bioRxiv

• 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. Uses the nextflow under the hood
• Parallelization: automatic parallelization both within and across samples (needs to be enabled in the config file)
• Ease of use: available as a minimal Python library (without the pipeline) or the full conda package

Installing the minimal Python library:

pip install micone

Installing the conda package:

git clone https://github.com/segrelab/MiCoNE.git
cd MiCoNE
conda env create -n micone -f env.yml
pip install .

NOTE: The conda package is currently being updated and will be available soon.

It supports the conversion of raw 16S sequence data or counts matrices into co-occurrence networks through multiple methods. Each process in the pipeline supports alternate tools for performing the same task, users can use the configuration file to change these values.

The MiCoNE pipelines comes with an easy to use CLI. To get a list of subcommands you can type:

micone --help

Supported subcommands:

1. init - Creates conda environments for various pipeline processes
2. run - The main subcommand that runs the pipeline
3. clean - Cleans temporary data, log files and other extraneous files

To run the pipeline:

micone run -p local -c run.toml -m 4

This runs the pipeline in the local machine using run.toml for the pipeline configuration and with a maximum of 4 processes in parallel at a time.

The configuration of the pipeline can be done using a .toml file. The details can be found in the relevant section in the docs. Here is an example config file that performs:

1. grouping of OTUs by taxonomy level
2. correlation of the taxa using fastspar
3. calculates p-values
4. constructs the networks

title = "A example pipeline for testing"

order = """
  otu_processing.filter.group
  otu_processing.export.biom2tsv
  network_inference.bootstrap.resample
  network_inference.correlation.sparcc
  network_inference.bootstrap.pvalue
  network_inference.network.make_network_with_pvalue
"""

output_location = "/home/dileep/Documents/results/sparcc_network"

[otu_processing.filter.group]
  [[otu_processing.filter.group.input]]
    datatype = "otu_table"
    format = ["biom"]
    location = "correlations/good/deblur/deblur.biom"
  [[otu_processing.filter.group.parameters]]
    process = "group"
    tax_levels = "['Family', 'Genus', 'Species']"

[otu_processing.export.biom2tsv]

[network_inference.bootstrap.resample]
  [[network_inference.bootstrap.resample.parameters]]
    process = "resample"
    bootstraps = 10

[network_inference.correlation.sparcc]
  [[network_inference.correlation.sparcc.parameters]]
    process = "sparcc"
    iterations = 5

[network_inference.bootstrap.pvalue]

[network_inference.network.make_network_with_pvalue]
  [[network_inference.network.make_network_with_pvalue.input]]
    datatype = "metadata"
    format = ["json"]
    location = "correlations/good/deblur/deblur_metadata.json"
  [[network_inference.network.make_network_with_pvalue.input]]
    datatype = "computational_metadata"
    format = ["json"]
    location = "correlations/good/deblur/deblur_cmetadata.json"

Other example config files can be found at tests/data/pipelines

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.