The WILDS WDL Library is a collection of workflow description language (WDL) scripts that provide reusable, well-tested bioinformatics tasks that can be combined into pipelines for research. All components are validated with real-world bioinformatics datasets to ensure they perform correctly in production environments. The library eliminates the need to write WDL workflows from scratch, ensures reproducibility through standardized containerization, and reduces debugging time with pre-tested, validated components.

You can:

• Build workflows faster from reusable, tested modules without reinventing common tasks
• Ensure reproducibility with versioned Docker containers and standardized components
• Execute workflows confidently on multiple platforms including PROOF, Cromwell, miniWDL, and Sprocket
• Learn WDL best practices through well-documented examples and vignettes

Library Organization

The library is organized into three complementary levels:

Modules

Tool-specific collections of reusable WDL tasks with comprehensive testing.

• Purpose: Foundational building blocks for larger workflows
• Content: Individual bioinformatics tools (STAR, BWA, GATK, etc.)
• Testing: Unit tests ensure each task functions correctly over time
• Usage: Import tasks into custom workflows or run demonstration workflows

Vignettes

Compact workflows demonstrating common bioinformatics patterns.

• Purpose: Educational examples of module integration
• Content: 2-3 modules combined into standard analysis patterns
• Testing: Integration tests verify modules work together seamlessly
• Usage: Templates for common workflows or learning examples

Workflows

Complete, publication-ready analysis pipelines.

• Purpose: End-to-end analyses suitable for research publications
• Content: Complex workflows combining multiple modules and custom logic
• Testing: Comprehensive validation with realistic datasets
• Usage: Production analyses requiring minimal customization

Testing & Validation

All WILDS WDLs undergo rigorous testing with real-world bioinformatics data to ensure production readiness:

Automated Testing

• Cross-platform validation: All modules are automatically tested using Cromwell, miniWDL, and Sprocket executors
• Continuous integration: Every code change triggers automated test runs before merging
• Real data requirements: Tests use authentic bioinformatics datasets from the ww-testdata module

Validation by Component Type

• Modules: Unit tests with real sequencing files (FASTQ, BAM, VCF) verify each task functions correctly
• Vignettes: Integration tests confirm modules work together using representative analysis datasets
• Workflows: End-to-end validation with realistic research datasets suitable for publication-quality analyses

This multi-layered testing approach ensures that WILDS WDLs perform reliably from individual tasks through complete analytical pipelines.

Getting Started

We recommend users start their exploration at the vignette level to learn how tasks can be imported and strung together in a WDL workflow. From there, you can dive into task details at the module level. From there, you can customize the workflow as necessary or dive right in and create your own!

Once you have a WDL that you like, you can run it several ways:

Fred Hutch Users

You can use PROOF to submit WDL workflows to the cluster through a user-friendly interface:

1. Ensure you have access to the data you plan to analyze via the cluster
2. Make sure you are connected to the FH network and navigate to proof.fredhutch.org
3. Log in with your Fred Hutch credentials
4. Upload a WDL and its accompanying JSON files
5. Submit and monitor your job through the PROOF dashboard

Everyone

You can run WDLs locally if you have a WDL executor and Docker/Apptainer installed:

• Basic WDL executor options: miniWDL, sprocket
• Docker Desktop for container execution

For example, you can run WILDS WDLs from the terminal using miniwdl like so:

# Clone the WILDS WDL repository
git clone https://github.com/getwilds/wilds-wdl-library.git
cd wilds-wdl-library

# Run a vignette (update inputs json as needed)
cd vignettes/ww-sra-star
miniwdl run ww-sra-star.wdl -i inputs.json

Importing into Your Workflows

You can import a WILDS WDL into your own WDL script:

version 1.0

import "https://raw.githubusercontent.com/getwilds/wilds-wdl-library/refs/heads/main/modules/ww-sra/ww-sra.wdl" as sra_tasks

workflow my_analysis {
  input {
    String sra_id
  }
  call sra_tasks.fastqdump { input: sra_id = sra_id }
  output {
    File fastq = fastqdump.fastq
  }
}

Note: Fred Hutch users must use WDL version 1.0 to run on PROOF

Then you can provide custom inputs using an inputs.json file:

{
  "sra_star.sra_id_list": ["SRR12345678"]
}

Available WDLs

Modules

Vignettes

Workflows

If there’s a tool you’d like to see or a task you want written you can file an issue, reach out to us directly (see below), or make a contribution

Key Features of the WILDS WDL Library

Quality Assurance

• Automated Testing: all scripts automatically execute full test runs before merging to main
• Multi-Executor Support: Compatible with Cromwell, miniWDL, and Sprocket
• Real Data Validation: Tested with realistic bioinformatics datasets

Different research institutions use different WDL execution engines (e.g., St. Jude’s uses Sprocket, CZI uses miniWDL, Broad Institute uses Cromwell). While these engines follow the same WDL specification, they have subtle differences in how they handle file paths and other execution details. The WILDS WDL Library is designed and tested to work seamlessly across all three major engines, ensuring your workflows remain portable regardless of which platform you or your collaborators use. This is particularly valuable as the WDL ecosystem continues to evolve, with institutions gradually migrating from Cromwell to newer engines like Sprocket and miniWDL.

Container Management

• Versioned, tested Docker images from the WILDS Docker Library
• Consistent container standards across all modules

Standards and Best Practices

• Standardized structure across all components
• Comprehensive documentation and inline comments
• WDL 1.0 specification compliance

Frequently Asked Questions

Can I use these workflows on my own compute infrastructure?

Yes! All workflows are designed to be portable and work with any WDL executor (Cromwell, miniWDL, Sprocket). Be sure you have one of these WDL executors (and Docker) installed.

Do I need to manually download Docker containers locally?

No, Docker will take care of pulling the necessary containers automatically when you run a workflow. You’ll need Docker installed but you don’t have to worry about pulling individual containers.

How can I contribute to the library?

We are very open to contributions from the Fred Hutch community and beyond! Our Contributing Guidelines describe these processes in detail, but feel free to reach out to us at wilds@fredhutch.org if you have questions.

Are these workflows production-ready?

Yes! All components undergo rigorous testing:

• Modules: Automated unit tests run on every update using Cromwell, miniWDL, and Sprocket to ensure cross-platform compatibility. Each module is validated with authentic bioinformatics datasets.
• Vignettes: Integration tests verify that modules work together seamlessly. These are manually tested with real data to ensure practical usability.
• Workflows: Complete end-to-end workflows in the workflows/ directory undergo comprehensive validation with realistic datasets and are suitable for research publications.

All testing is run through our continuous integration system. Users can reproduce these tests locally using the repository Makefile and test data from the ww-testdata module.

How do I get help with a specific workflow?

Contact the WILDS team at wilds@fredhutch.org, schedule a Data House Call with us, post on the #workflow-managers channel in FH-Data Slack, or open an issue on GitHub.

Release Notes

October 2025 - WILDS WDL Library v0.1.0

• Three-tier architecture with modules, vignettes, and workflows
• Comprehensive automated testing with multiple WDL executors at the module level
• Manual testing with real bioinformatics data at the vignette and workflow levels
• Standardized container management through the WILDS Docker Library
• Full compatibility with the Fred Hutch PROOF platform
• 17+ tested modules covering essential bioinformatics tools
• Example vignettes demonstrating module integration
• Production-ready workflows for variant calling and RNA-seq analysis

Links

Get Started

• Browse Available Modules
• Explore Vignettes
• Run Complete Workflows
• Use PROOF for Easy Execution

Learn More

• WDL Best Practices Guide
• WILDS Docker Library
• Contributing Guidelines

Contact Us

• Issues and Bug Reports: GitHub Issues
• General Questions: Email wilds@fredhutch.org
• Data House Calls: Schedule a 30-minute consultation session with us
• Slack: Join the #workflow-managers channel in FH-Data Slack