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Deeply sequenced metagenome and metatranscriptome of a biogas-producing microbial community from an agricultural production-scale biogas plant

Bremges et al. (2015). GigaScience, 4:33. doi:10.1186/s13742-015-0073-6

Data availablility

Raw sequencing data are available in the European Nucleotide Archive (ENA) under study accession PRJEB8813.
The datasets supporting the results of our manuscript are available in GigaScience's GigaDB:

Bremges et al. (2015). GigaScience Database. doi:10.5524/100151

Reproducibility

Excluding the KEGG analysis, which relies on a commercial license of the KEGG database, all steps are performed using free and open-source software.

Makefile

The complete workflow is organized in a single GNU Makefile. It downloads all data and re-runs all analysis steps (w/o the time-consuming BLASTP search against KEGG, for that please adjust the Makefile). All data and results can be reproduced by a simple invocation of make.

By default, the metagenome assembly (Ray Meta) will run with 48 threads. Read preprocessing (Trimmomatic) and mapping (Bowtie2) with 8 threads each. This suits e.g. a single-node machine with 48 cores and parallel execution of make with make -j. Please adjust the default values accordingly.

Note: Ray Meta is nondeterministic on 2 or more cores, and thus the assembly results will slightly vary from run to run. Downstream analyses will be affected by this, but the results are of comparable quality and mostly consistent.

Docker container

@pbelmann implemented and tested the accompanying Docker container.

  1. docker pull metagenomics/2015-biogas-cebitec
  2. docker run -v /path/to/workspace/directory:/home/biogas/data metagenomics/2015-biogas-cebitec

Note: The workspace directory, /path/to/workspace/directory, mounted to the container should be on a volume with >83GB space.
After the container finished, all results can be found in here.

Per default the container runs with 8 threads (and a serial execution of make).
You can change this by specifying --threads=NUMBER after the name, e.g.
docker run -v /path/to/workspace/directory:/home/biogas/data metagenomics/2015-biogas-cebitec --threads=32

Docker on AWS

We tested the Docker container on an r3.8xlarge instance with 32 Cores, 244GB RAM and a 320GB SSD volume. On such an instance, setting --threads=32, execution takes less than 24 hours to complete (21 hours and 16 minutes in our latest test). It should work on smaller instances, too: reproduction requires roughly 89GB memory and 83GB storage.

Steps by step guide:

  1. Choose an instance with >83GB local volume size or mount an additional volume (>83GB) using the description provided by AWS: http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/ec2-add-volume-to-instance.html

  2. Run sudo apt-get update

  3. Install the newest docker version by using the description on docker.com:
    https://docs.docker.com/installation/ubuntulinux/
    (This image is tested with Docker version 1.6)

  4. Start the container with
    sudo docker run -v /path/to/workspace/directory:/home/biogas/data metagenomics/2015-biogas-cebitec, where /path/to/workspace/directory is the path to a directory in your local storage volume or in a volume you mounted to your instance (see step 1). Set the number of threads to the number of available cores to fully utilize your instance.

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If you have any questions or run into problems, please file an issue!

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