Common Workflow Language (CWL)

bcbio has in-progress support for 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 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:

  • cwltool – a single core analysis engine, primarily used for testing.
  • Arvados – fully parallel distributed analyses. We include an example below of running on the public Curoverse instance running on Microsoft Azure.
  • toil – parallel local and distributed cluster runs. Distribution on cluster schedulers like SLURM and SGE is still under development.

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 evaluating Galaxy/Planemo for integration with the Galaxy community, and working on support for Broad’s Cromwell WDL runner.

Getting started

bcbio-vm organizes all dependencies required to run bcbio 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 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 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
    
  2. Run the analysis using cwltool. If you have Docker available on your machine, cwltool will download the bcbio/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 bcbio/bcbio. You can use the run_cwltool.sh script or run directly from the command line:

    bcbio_vm.py cwlrun cwltool run_info-cwl-workflow
    

    If you don’t have Docker, you can also use a local installation of bcbio. You don’t need to install genome data since the tests use small local data. Then run with:

    bcbio_vm.py cwlrun cwltool run_info-cwl-workflow --no-container
    

Generating CWL from bcbio

You can generate CWL from any standard bcbio sample configuration file. As an example, to generate the test data show above, clone the bcbio GitHub repository locally to get the test suite and run a minimal CWL workflow generated automatically by bcbio from the inputs:

$ git clone https://github.com/chapmanb/bcbio-nextgen.git
$ cd bcbio-nextgen
$ py.test -m cwl

This will create a CWL workflow inside tests/test_automated_output which you can run again manually with either a local bcbio installation or Docker as described above.

To generate CWL directly from a sample input and the test bcbio system file:

bcbio_vm.py cwl ../data/automated/run_info-cwl.yaml --systemconfig ../data/automated/post_process-sample.yaml

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
    

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. We’re at the early stage of testing bcbio runs on this architecture but have successfully run bcbio CWL workflows across these environments. Toil comes pre-installed with bcbio-vm.

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

bcbio_vm.py cwlrun toil run_info-cwl-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 `pwd`/run_info-cwl-workflow -- --batchSystem slurm

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.
  • Determine when we should skip steps based on configuration to avoid writing them to the CWL file. For instance, right now we include HLA typing even if it’s not defined and have an extra do-nothing step in the CWL output. We should have a clean way to skip writing this step if not needed based on the configuration.
  • Replace the custom python code in the bcbio step definitions with a higher level DSL in YAML we can parse and translate to CWL.