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Overview
=========

GroopM is a metagenomic binning toolset. It leverages spatio-temoral 
dynamics to accurately (and almost automatically) extract genomes 
from multi-sample metagenomic datasets.

GroopM is largely parameter-free. Use: groopm -h for more info.

See also: http://minillinim.github.io/GroopM/

Installation
=========

Should be as simple as

    pip install GroopM

Data preparation and running GroopM
=========

Before running GroopM you need to prep your data. A typical workflow looks like this:

    1. Produce NGS data for your environment across mutiple (3+) samples (spearated spatially or temporally or both).
    2. Co-assemble your reads using Velvet or similar.
    3. For each sample, map the reads against the co-assembly. GroopM needs sorted indexed bam files. If you have 3 samples then you will produce 3 bam files. I use BWA / Samtools for this.
    4. Take your co-assembled contigs and bam files and load them into GroopM using 'groopm parse' saveName contigs.fa bam1.bam bam2.bam...
    5. Keep following the GroopM workflow. See: groopm -h for more info.

Licence and referencing
=========

Project home page, info on the source tree, documentation, issues and how to contribute, see http://github.com/minillinim/GroopM

This software is currently unpublished but a manuscript is being prepared. Please contact me at m_dot_imelfort_at_uq_dot_edu_dot_au for more information about referencing this software.

Copyright © 2012 Michael Imelfort. See LICENSE.txt for further details.