Metadata-Version: 1.0
Name: GroopM
Version: 0.2.6
Summary: Metagenomic binning suite
Home-page: http://pypi.python.org/pypi/GroopM/
Author: Michael Imelfort
Author-email: mike@mikeimelfort.com
License: LICENSE.txt
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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        This software is currently unpublished but a manuscript is being prepared.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.
        
Platform: UNKNOWN
