Run bcbio-nextgen genomic sequencing analysis pipelines using code and tools on cloud platforms or isolated inside of lightweight containers. This enables:

• Improved installation: Pre-installing all required biological code, tools and system libraries inside a container removes the difficulties associated with supporting multiple platforms. Installation only requires setting up Docker and download of the latest container.
• Pipeline isolation: Third party software used in processing is fully isolated and will not impact existing tools or software. This eliminates the need for Environment Modules or PATH manipulation to provide partial isolation.
• Full reproducibility: You can maintain snapshots of the code and processing environment indefinitely, providing the ability to re-run an older analysis by reverting to an archived snapshot.

This currently supports running on Amazon Web Services (AWS) and locally with lightweight Docker containers. The bcbio documentation contains details on using bcbio-vm to run analyses on AWS. We also have in progress work on migrating bcbio's pipeline descriptions to use the Common Workflow Language (CWL).

We support using bcbio-vm for both AWS and local docker usage on Linux systems. On Mac OSX, only AWS usage currently works. Local docker support for Mac OSX is a work in progress and we have more details on the current status below. We welcome feedback and problem reports.

• Install bcbio-vm using Conda with an isolated Miniconda Python and link to a location on your PATH:

  wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
  bash Miniconda3-latest-Linux-x86_64.sh -b -p ~/install/bcbio-vm/anaconda
  ~/install/bcbio-vm/anaconda/bin/conda install --yes -c conda-forge -c bioconda bcbio-nextgen
  ~/install/bcbio-vm/anaconda/bin/conda install --yes -c conda-forge -c bioconda bcbio-nextgen-vm
  ln -s ~/install/bcbio-vm/anaconda/bin/bcbio_vm.py /usr/local/bin/bcbio_vm.py
  ln -s ~/install/bcbio-vm/anaconda/bin/conda /usr/local/bin/bcbiovm_conda

  If you're using bcbio-vm from your local machine to run on a pre-built remote AWS instance, or on an Arvados cloud instance this is all you need to get started. If you'd like to run locally or on a server with Docker, keep following the instructions to install the third party tools and data.

• Install docker on your system. You will need root permissions.

• Setup a docker group to provide the ability to run Docker without being root. Some installations, like Debian/Ubuntu packages do this automatically. You'll also want to add the trusted user who will be managing and testing docker images to this group:

  sudo groupadd docker
  sudo service docker restart
  sudo gpasswd -a ${USERNAME} docker
  newgrp docker

• Ensure the driver script is setgid to the docker group. This allows users to run bcbio-nextgen without needing to be in the docker group or have root access. To avoid security issues, bcbio_vm.py sanitizes input arguments and runs the internal docker process as the calling user using a small wrapper script so it will only have permissions available to that user:

  sudo chgrp docker /usr/local/bin/bcbio_vm.py
  sudo chmod g+s /usr/local/bin/bcbio_vm.py

• Install a dockerized bcbio-nextgen. This will get the latest bcbio docker image with software and tools, as well as downloading genome data:

  bcbio_vm.py --datadir=~/install/bcbio-vm/data install --data --tools \
    --genomes GRCh37 --aligners bwa

  For more details on expected download sizes, see the bcbio system requirements documentation. By default, the installation will download and import the default docker image as quay.io/bcbio/bcbio-vc. You can specify an alternative image location with --image your_image_name, and skip the --tools argument if this image is already present and configured.

  If you have an existing bcbio-nextgen installation and want to avoid re-installing existing genome data, first symlink to the current installation data:

  mkdir ~/install/bcbio-vm/data
  cd ~/install/bcbio-vm/data
  ln -s /usr/local/share/bcbio_nextgen/genomes
  ln -s /usr/local/share/gemini/data gemini_data

• If you didn't use the recommended installation organization (a shared directory with code under anaconda and data under data) set the data location configuration once for each individual user of bcbio-nextgen to avoid needing to specify the location of data directories on subsequent runs:

  bcbio_vm.py --datadir=~/install/bcbio-vm/data saveconfig

Usage of bcbio_vm.py is similar to bcbio_nextgen.py, with some cleanups to make the command line more consistent. To run an analysis on a prepared bcbio-nextgen sample configuration file:

bcbio_vm.py run -n 4 sample_config.yaml

To run distributed on a cluster using IPython parallel:

bcbio_vm.py ipython sample_config.yaml torque your_queue -n 64

bcbio-nextgen also contains tests that exercise docker functionality:

cd bcbio-nextgen/tests
./run_tests.sh docker
./run_tests.sh docker_ipython

bcbio-nextgen-vm enables easy updates of the wrapper code, tools and data. To update the wrapper code:

bcbio_vm.py install --wrapper

To update tools, with a download of the latest docker image:

bcbio_vm.py install --tools

To update the associated data files:

bcbio_vm.py install --data

Combine all commands to update everything concurrently.

These notes are for building containers from scratch or developing on bcbio-nextgen.

• Install Git, VirtualBox, and Vagrant
• Download bcbio-nextgen-vm and provision Vagrant VM:

  git clone git@github.com:bcbio/bcbio-nextgen-vm.git
  cd bcbio-nextgen-vm
  vagrant up

• Install bcbio-nextgen-vm:

  vagrant ssh
  wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
  bash Miniconda3-latest-Linux-x86_64.sh -b -p ~/bcbio-vm/anaconda
  conda install --yes -c conda-forge -c bioconda bcbio-nextgen-vm bcbio-nextgen

Optional steps:

• Inside the VM (vagrant ssh):
  • Set the time zone in the VM for easier log viewing, for example:

    sudo timedatectl set-timezone America/New_York

• Outside the VM:
  • To make any additional data from the host available inside the VM (for example: reference genomes, pipeline inputs, etc) set BCBIO_DATA_DIR environment variable on the host to a directory that contains the data, for example:

    export BCBIO_DATA_DIR=~/biodata
    vagrant reload

    This directory will be mounted inside Vagrant VM under /data

To run all tests:

cd tests
pytest -v

Install the current bcbio docker image into your local repository by hand with:

docker pull quay.io/bcbio/bcbio-vc

The installer does this automatically, but this is useful if you want to work with the bcbio-nextgen docker image independently from the wrapper.

To update bcbio-nextgen in a local docker instance during development, first clone the development code:

git clone https://github.com/chapmanb/bcbio-nextgen
cd bcbio-nextgen

Edit the code as needed, then update your local install with:

bcbio_vm.py devel setup_install

You can update the tools in your local container with:

bcbio_vm.py devel upgrade_tools

and register a GATK jar inside the container with:

bcbio_vm.py devel register gatk /path/to/GenomeAnalysisTK.tar.bz2

Docker hub builds the bcbio docker image. We manually trigger this build to avoid overloading Docker hub services with a long rebuild on every change to the bcbio repository.

bcbio_vm downloads pre-built reference genomes when running analyses, to avoid needing these to be present on the initial machine images. To create the pre-built tarballs for a specific genome, start and bootstrap a single bcbio machine using the elasticluster interface. On the machine start a screen session then run:

bcbio_vm.py devel biodata --genomes GRCh37 --aligners bwa --aligners bowtie2 --datatarget vep

This requires permissions to write to the biodata bucket.