Common Workflow Language (CWL)

bcbio supports running with Common Workflow Language (CWL) compatible parallelization software. bcbio generates a CWL workflow from a sample YAML description file. Any tool that supports CWL input can run this workflow. CWL-based tools do the work of managing files and workflows, and bcbio performs the biological analysis using either a Docker container or a local installation.

This is a work in progress and not yet a complete production implementation. The documentation orients anyone interested in helping with development.

Current status

bcbio currently supports creation of CWL for alignment, small variant calls (SNPs and indels), coverage assessment, HLA typing and quality control. It generates a CWL v1.0.2 compatible workflow. The actual biological code execution during runs works with either the bcbio docker container (bcbio/bcbio) or a local installation of bcbio.

The implementation includes bcbio’s approaches to splitting and batching analyses. At the top level workflow, we parallelize by samples. Using sub-workflows, we split fastq inputs into sections for parallel alignment over multiple machines following by merging. We also use sub-workflows, along with CWL records, to batch multiple samples and run in parallel. This enables pooled and tumor/normal cancer calling with parallelization by chromosome regions based on coverage calculations.

Variant calling overview

bcbio supports these CWL-compatible tools:

We plan to continue to expand CWL support to include more components of bcbio, and also need to evaluate the workflow on larger, real life analyses. This includes supporting additional CWL runners. We’re working on supporting DNAnexus, evaluating Galaxy/Planemo for integration with the Galaxy community, and generating inputs for Broad’s Cromwell WDL runner.

Getting started

bcbio-vm installs all dependencies required to generate CWL and run bcbio, along with supported CWL runners. To install using Miniconda and bioconda packages:

wget http://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh
bash Miniconda2-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-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 have Docker present on your system this is all you need to get started running examples. If you instead prefer to use a local installation, install bcbio and make it available in your path. To only run the tests, you don’t need a full data installation so can install with --nodata.

To make it easy to get started, we have a pre-built CWL description that uses test data. This will run in under 5 minutes on a local machine and doesn’t require a bcbio installation if you have Docker available on your machine:

  1. Download and unpack the test repository:

    wget -O test_bcbio_cwl.tar.gz https://github.com/bcbio/test_bcbio_cwl/archive/master.tar.gz
    tar -xzvpf test_bcbio_cwl.tar.gz
    cd test_bcbio_cwl-master/somatic
    
  2. Run the analysis using either Toil or Rabix bunny. If you have Docker available on your machine, the runner will download the correct bcbio container and you don’t need to install anything else to get started. If you have an old version of the container you want to update to the latest with docker pull quay.io/bcbio/bcbio-vc. There are shell scripts that provide the command lines for running:

    bash run_toil.sh
    bash run_bunny.sh
    

    Or you can run directly using the bcbio_vm.py wrappers:

    bcbio_vm.py cwlrun toil somatic-workflow
    bcbio_vm.py cwlrun bunny somatic-workflow
    

    Thes wrappers automatically handle temporary directories, permissions, logging and re-starts. If running without Docker, use a local installation of bcbio add --no-container to the commands in the shell scripts.

Generating CWL for local or cluster runs

The first step in running your analysis project in bcbio is to generate CWL. The inputs to this are:

  • A standard bcbio sample configuration file defining the samples. This can either be a full prepared YAML file or a template file and CSV with sample data.

  • A bcbio_system.yaml file defining the system environment for running the program. This includes the resource specification with cores and memory per core for your machines. You generally want to set this to match the parameters of a single machine either for a local run or on a cluster. It also includes paths to the reference biodata and optionally input files if you want to avoid specifying full paths in your inputs. Here is an example for a 16 core machine with 3.5Gb of memory per core:

    local:
      ref: /path/to/bcbio/genomes/Hsapiens
      inputs:
        - /path/to/input/files
    resources:
      default:
        cores: 16
        memory: 3500M
        jvm_opts: [-Xms1g, -Xmx3500m]
    

Generate CWL with:

bcbio_vm.py template --systemconfig bcbio_system.yaml template.yaml samples.csv
bcbio_vm.py cwl --systemconfig bcbio_system.yaml samples/config/samples.yaml

producing a sample-workflow output directory with the CWL. You can run this with any CWL compatible runner. The bcbio_vm.py cwlrun wrappers described above make this easier for local runs with Toil or Bunny.

Running bcbio CWL on Toil

The Toil pipeline management system runs CWL workflows in parallel on a local machine, on a cluster or at AWS. Toil comes pre-installed with bcbio-vm.

To run a bcbio CWL workflow locally with Toil using Docker:

bcbio_vm.py cwlrun toil sample-workflow

If you want to run from a locally installed bcbio add --no-container to the commandline.

To run distributed on a Slurm cluster:

bcbio_vm.py cwlrun toil sample-workflow -- --batchSystem slurm

Running bcbio CWL on Arvados

We’re actively testing bcbio generated CWL workflows on Arvados. These instructions detail how to run on the Arvdos public instance. Arvados cwl-runner comes pre-installed with bcbio-vm.

Retrieve API keys from the Arvados public instance. Login, then go to ‘User Icon -> Personal Token’. Copy and paste the commands given there into your shell. You’ll specifically need to set ARVADOS_API_HOST and ARVADOS_API_TOKEN.

To run an analysis:

  1. Create a new project from the web interface (Projects -> Add a new project). Note the project ID from the URL of the project (an identifier like qr1hi-j7d0g-7t73h4hrau3l063).

  2. Upload reference data to Aravdos Keep. Note the genome collection portable data hash:

    arv-put --portable-data-hash --name hg19-testdata --project-uuid qr1hi-j7d0g-7t73h4hrau3l063 testdata/genomes
    
  3. Upload input data to Arvados Keep. Note the collection portable data hash:

    arv-put --portable-data-hash --name input-testdata --project-uuid qr1hi-j7d0g-7t73h4hrau3l063 testdata/100326_FC6107FAAXX testdata/automated testdata/reference_material
    
  4. Create an Arvados section in a bcbio_system.yaml file specifying locations to look for reference and input data. input can be one or more collections containing files or associated files in the original sample YAML:

    arvados:
      reference: a84e575534ef1aa756edf1bfb4cad8ae+1927
      input: [a1d976bc7bcba2b523713fa67695d715+464]
    resources:
         default:
           cores: 4
           memory: 1G
         bwa:
           cores: 4
           memory: 2G
         gatk:
           jvm_opts: [-Xms750m, -Xmx2500m]
    
  5. Generate the CWL to run your samples. If you’re using multiple input files with a CSV metadata file and template then start with creation of a configuration file:

    bcbio_vm.py template --systemconfig bcbio_system_arvados.yaml
    testcwl_template.yaml testcwl.csv
    

    To generate the CWL from the system and sample configuration files:

    bcbio_vm.py cwl --systemconfig bcbio_system_arvados.yaml testcwl/config/testcwl.yaml
    
  6. Run the CWL on the Arvados public cloud using the Arvados cwl-runner:

    bcbio_vm.py cwlrun arvados arvados_testcwl-workflow -- --project-uuid qr1hi-your-projectuuid
    

Development notes

bcbio generates a common workflow language description. Internally, bcbio represents the files and information related to processing as a comprehensive dictionary. This world object describes the state of a run and associated files, and new processing steps update or add information to it. The world object is roughly equivalent to CWL’s JSON-based input object, but CWL enforces additional annotations to identify files and models new inputs/outputs at each step. The work in bcbio is to move from our laissez-faire approach to the more structured CWL model.

The generated CWL workflow is in run_info-cwl-workflow:

  • main-*.cwl – the top level CWL file describing the workflow steps
  • main*-samples.json – the flattened bcbio world structure represented as CWL inputs
  • wf-*.cwl – CWL sub-workflows, describing sample level parallel processing of a section of the workflow, with potential internal parallelization.
  • steps/*.cwl – CWL descriptions of sections of code run inside bcbio. Each of these are potential parallelization points and make up the nodes in the workflow.

To help with defining the outputs at each step, there is a WorldWatcher object that can output changed files and world dictionary objects between steps in the pipeline when running a bcbio in the standard way. The variant pipeline has examples using it. This is useful when preparing the CWL definitions of inputs and outputs for new steps in the bcbio CWL step definitions.

ToDo

  • Support the full variant calling workflow with additional steps like ensemble calling, structural variation, heterogeneity detection and disambiguation.
  • Port RNA-seq and small RNA workflows to CWL.
  • Replace the custom python code in the bcbio step definitions with a higher level DSL in YAML we can parse and translate to CWL.