Microbial Interaction Database Pipeline

mindpipe, 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://dileep-kishore.github.io/mindpipe.

Manuscript in preparation.

Features

• 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
• Ease of use: available as conda package as well as a docker container

Workflow

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.

Usage

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

mindpipe --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:

mindpipe 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.

Credits

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