. . .d8888b. 888b d888 . d88P Y88b 8888b d8888 . 888 888 88888b.d88888 . 888 888d888 .d88b. .d88b. 88888b. 888Y88888P888 . 888 88888 888P" d88""88b d88""88b 888 "88b 888 Y888P 888 . 888 888 888 888 888 888 888 888 888 888 Y8P 888 . Y88b d88P 888 Y88..88P Y88..88P 888 d88P 888 " 888 . "Y8888P88 888 "Y88P" "Y88P" 88888P" 888 888 . 888 . 888 . 888 Overview ========= GroopM is a metagenomic binning toolset. It leverages spatio-temoral dynamics (differential coverage) to accurately (and almost automatically) extract population genomes from multi-sample metagenomic datasets. GroopM is largely parameter-free. Use: groopm -h for more info. For installation and usage instructions see : http://ecogenomics.github.io/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/ecogenomics/GroopM If you use this software then we'd love you to cite us. Our paper is now available at https://peerj.com/articles/603. The DOI is http://dx.doi.org/10.7717/peerj.603 Copyright © 2012-2015 Michael Imelfort. GroopM is licensed under the GNU GPL v3 See LICENSE.txt for further details.