Getting Started

miniwdl is a local runner and developer toolkit for the bioinformatics-focused Workflow Description Language (WDL). In this tutorial, we’ll install miniwdl and use its runner to assemble an Ebola virus (EBOV) genome from short sequencing reads.

TIP: If you are new to working with WDL workflow language, you may want to review the open source ‘learn-wdl’ course’ - link.

Also there is an embedded short course ‘learn-miniwdl’ which includes screencasts reviewing the tutorial on this page in more detail - link

Install miniwdl


  1. GNU/Linux or macOS (specific steps required)
  2. Python 3.6 or higher
  3. Docker Engine 17 or higher (if unable, see alternate container runtimes)
  4. Unix user must have permission to control Docker

Installation options:

  • via PyPI: pip3 install miniwdl
  • via conda: conda install -c conda-forge miniwdl
  • see the GitHub repo README to install from source

Then open a command prompt and try,

miniwdl run_self_test

…to test the installation with a trivial built-in workflow. This should print numerous log messages, and conclude with miniwdl run_self_test OK in about 30 seconds.

Fetch viral-pipelines

For this exercise we’ll use the Broad Institute’s viral sequencing pipeline, which includes a small EBOV dataset for testing. Start by fetching a copy,

wget -nv -O - | tar zx
cd viral-pipelines-*

Run assemble_refbased workflow

First we can use miniwdl to preview the inputs and outputs of the reference-based assembly workflow:

$ miniwdl run pipes/WDL/workflows/assemble_refbased.wdl

missing required inputs for assemble_refbased: reads_unmapped_bams, reference_fasta

required inputs:
  Array[File]+ reads_unmapped_bams
  File reference_fasta

optional inputs:
  String sample_name

  File assembly_fasta
  Int assembly_length
  Int assembly_length_unambiguous
  Int reference_genome_length
  Float assembly_mean_coverage

To invoke the workflow, miniwdl can accept the inputs as command-line arguments in most cases. Here we’ll start it on the test reads and EBOV reference genome included in the repository:

$ miniwdl run pipes/WDL/workflows/assemble_refbased.wdl   \
    reads_unmapped_bams=test/input/G5012.3.testreads.bam  \
    reference_fasta=test/input/ebov-makona.fasta          \
    sample_name=G5012.3 --verbose

The workflow should finish in just a few minutes.

  • Adding --verbose shows more status detail, including a realtime log of each task’s standard error stream (often informative for debugging).
  • A space may be included after a = and before an input value, allowing shell filename autocompletion on the latter.
  • Array inputs can be supplied on the command-line by repeating, e.g. array_input1=/path/to/file1 array_input1=/path/to/file2 translates to {"array_input1": ["/path/to/file1", "/path/to/file2"]}
  • Strings with spaces can be supplied by quoting the whole pair, "name=Wid L. Hacker"
  • For other cases or to separate inputs from the invocation, you can supply a Cromwell-style JSON file with --input inputs.json.

Inspect results

By default, miniwdl run creates a new subdirectory of the current working directory, used for all of the workflow’s operations. The subdirectory’s name is timestamp-prefixed, so that multiple runs sort in the order they were invoked. The workflow directory can be overridden on the command line; see miniwdl run --help for details.

The standard output from miniwdl run provides the subdirectory along with JSON describing the workflow outputs, for example (abbreviated):

  "outputs": {
    "assemble_refbased.assembly_length": 18865,
    "assemble_refbased.assembly_length_unambiguous": 18865,
    "assemble_refbased.assembly_mean_coverage": 94.95885858958806,
    "assemble_refbased.assembly_fasta": "/tmp/viral-pipelines-",
    "assemble_refbased.reference_genome_length": 18959,
  "dir": "/tmp/viral-pipelines-"

This is also stored in outputs.json in the subdirectory. For your convenience, miniwdl furthermore generates a symbolic link _LAST pointing to the timestamped subdirectory for most recent run; and an out directory tree containing symbolic links to the output files.

$ tree _LAST/out/
├── align_to_ref_merged_aligned_trimmed_only_bam
│   └── G5012.3.align_to_ref.trimmed.bam -> ../../call-merge_align_to_ref/work/G5012.3.align_to_ref.trimmed.bam
├── align_to_ref_merged_coverage_plot
│   └── G5012.3.coverage_plot.pdf -> ../../call-plot_ref_coverage/work/G5012.3.coverage_plot.pdf
├── align_to_ref_merged_coverage_tsv
│   └── G5012.3.coverage_plot.txt -> ../../call-plot_ref_coverage/work/G5012.3.coverage_plot.txt
├── align_to_ref_multiqc_report
│   └── multiqc.html -> ../../call-multiqc_align_to_ref/work/multiqc-output/multiqc.html
├── align_to_ref_per_input_aligned_flagstat
│   └── 0
│       └── G5012.3.testreads.all.bam.flagstat.txt -> ../../../call-align_to_ref-0/work/G5012.3.testreads.all.bam.flagstat.txt
├── align_to_ref_variants_vcf_gz
│   └── G5012.3.sites.vcf.gz -> ../../call-call_consensus/work/G5012.3.sites.vcf.gz
├── align_to_self_merged_aligned_only_bam
│   └── G5012.3.merge_align_to_self.bam -> ../../call-merge_align_to_self/work/G5012.3.merge_align_to_self.bam
├── align_to_self_merged_coverage_plot
│   └── G5012.3.coverage_plot.pdf -> ../../call-plot_self_coverage/work/G5012.3.coverage_plot.pdf
├── align_to_self_merged_coverage_tsv
│   └── G5012.3.coverage_plot.txt -> ../../call-plot_self_coverage/work/G5012.3.coverage_plot.txt
└── assembly_fasta
    └── G5012.3.fasta -> ../../call-call_consensus/work/G5012.3.fasta

The out links are often more convenient to consume than the JSON, but they only capture outputs that are files. Individual tasks and sub-workflows run in their own nested subdirectories, each with a similar structure.

Next steps

The following pages document features and optimization for miniwdl run, including numerous available configuration options. Use miniwdl configure to create a configuration file with common options interactively.

To aid the workflow development cycle, miniwdl also includes a static code quality checker, miniwdl check. Lastly, installing miniwdl makes available a Python WDL package, providing programmatic access to miniwdl’s WDL parser and runtime.

The miniwdl runner schedules WDL tasks in parallel up to the CPUs & memory available on the local host; so a more-powerful host enables larger workloads. Separately-maintained extensions can distribute tasks to cloud & HPC backends: