WILDS WDL Library Documentation

Technical documentation for a centralized collection of bioinformatics WDL infrastructure providing reusable, well-tested components for genomics research.

Welcome to the WILDS WDL Library Documentation

This site provides comprehensive technical documentation for all WDL modules and pipelines in the WILDS WDL Library. Use the sidebar navigation to explore available components and view detailed task descriptions, inputs/outputs, and usage examples.

Navigating This Documentation

Modules - Foundational building blocks

Tool-specific collections of reusable WDL tasks
Each module page shows available tasks with complete parameter documentation
Includes test workflows demonstrating basic usage

Pipelines - Complete analysis workflows

Workflows combining multiple modules into functional analysis pipelines
Range from basic educational examples (2-3 modules) to advanced production pipelines (10+ modules)
Complexity levels documented in each pipeline's README
Serve as templates for custom workflows or production analyses

Quick Start: Using Components

Importing Modules into Your Workflows

All components can be imported directly via GitHub URLs:

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

workflow my_analysis {
  call sra_tasks.fastqdump { input: sra_id = "SRR12345678" }
  call star_tasks.star_align_two_pass {
    input: sample_data = { "name": "sample1", "r1": fastqdump.r1_end, "r2": fastqdump.r2_end }
  }
}

Benefits of GitHub imports:

No local cloning required - use modules directly
Pin to specific commits or tags for reproducibility
Easy version switching by changing the URL
Import only the modules you need

Running Pipelines Directly (No Clone Required)

Thanks to GitHub URL imports, you can download and run any pipeline without cloning the entire repository:

# Download a pipeline and its example inputs
# Option 1: Use curl from the command line
curl -O https://raw.githubusercontent.com/getwilds/wilds-wdl-library/main/pipelines/ww-sra-star/ww-sra-star.wdl
curl -O https://raw.githubusercontent.com/getwilds/wilds-wdl-library/main/pipelines/ww-sra-star/inputs.json
# Option 2: Download directly from GitHub by navigating to the file and clicking the download button

# Modify inputs.json as necessary for your data, then run via the command line or PROOF's point-and-click interface
sprocket run ww-sra-star.wdl inputs.json

Running Components Locally

If you prefer to clone the full repository:

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

# Run a module test workflow (no inputs needed)
cd modules/ww-star
sprocket run testrun.wdl

# Run a pipeline (modify inputs.json as necessary)
cd ../../pipelines/ww-sra-star
sprocket run ww-sra-star.wdl inputs.json

Library Architecture

The WILDS WDL Library is organized into two complementary tiers:

Modules (`modules/`)

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

Pipelines (`pipelines/`)

Purpose: Functional pipelines ranging from educational examples to production-ready analyses Content: Multiple modules combined into analysis workflows of varying complexity Complexity Levels: Basic (2-3 modules), Intermediate (4-6 modules), Advanced (10+ modules) Testing: Integration tests verify modules work together seamlessly Usage: Templates for common workflows, learning examples, or production analyses

Supported WDL Executors

All components are tested with multiple WDL executors to ensure broad compatibility:

Cromwell - Production-grade workflow engine
miniWDL - Lightweight local execution
Sprocket - Modern WDL executor with enhanced features

Container Images

All tasks use versioned, tested Docker images from the WILDS Docker Library, ensuring reproducible execution across different computing environments.

Getting Help

Documentation Issues: Found something unclear or incorrect? Report an issue
General Questions: Contact the Fred Hutch Data Science Lab at wilds@fredhutch.org
Additional Resources:
- WILDS Guide - Comprehensive guides and best practices
- Contributing Guidelines - How to contribute
- GitHub Repository - Source code and development

Ready to explore? Use the sidebar to browse available modules and pipelines. Each component page provides complete technical documentation including task signatures, parameter descriptions, and usage examples.

WILDS WDL Library