Converting Nextflow Workflows to Constellab

Converting Nextflow Workflows to Constellab

This guide explains how to migrate your Nextflow pipelines to the Constellab platform using AI-assisted conversion.

Simple Conversion Powered by AI

Converting Nextflow pipelines to Constellab is remarkably simple thanks to AI automation. The AI assistant analyzes your Nextflow pipeline, understands the process logic, and automatically generates equivalent Constellab tasks with minimal user intervention. You don't need to manually rewrite code or understand the intricacies of both systems—the AI handles the heavy lifting for you.

Key benefits:

• 🤖 Fully automated analysis of your Nextflow pipeline structure
• ⚡ Instant task generation with proper inputs, outputs, and configurations
• 🎯 Smart script conversion from bash to Python for better maintainability
• 📝 Automatic documentation generation for all converted tasks
• ✅ Built-in validation to ensure conversion accuracy

Simply point the AI to your Nextflow file, answer a few validation questions, and let it create production-ready Constellab tasks in seconds.

What is Nextflow?

Nextflow is an open-source workflow management system designed for data-intensive computational pipelines. It enables:

• Scalable workflows: Execute processes in parallel across multiple computing environments
• Reproducibility: Container-based execution (Docker, Singularity) ensures consistent results
• Portability: Run on local machines, HPC clusters, or cloud platforms (AWS, Google Cloud, Azure)
• Domain-specific language: Write workflows using a Groovy-based DSL optimized for data pipelines

Nextflow has become a standard tool in bioinformatics and scientific computing, particularly for genomics workflows.

Nextflow vs. Constellab: A Comparison

Both Nextflow and Constellab are open-source workflow management systems, but they serve different needs:

Key Advantages of Constellab

Constellab's main strength is being a complete platform, not just a workflow engine:

1. Unified Interface: Manage pipelines, data, experiments, and results in one place
2. Full Traceability: Every execution, parameter change, and data transformation is tracked and auditable
3. Data-Centric: First-class data management with visualization, annotation, and sharing capabilities
4. Collaboration: Teams can share protocols, datasets, and results seamlessly
5. Extensibility: Modular "brick" architecture allows easy addition of new capabilities
6. No-Code Options: Build and run workflows through the GUI without writing code
7. Hybrid Approach: Supports both GUI-based and code-based workflow development

Converting Nextflow to Constellab

Overview

Converting a Nextflow pipeline to Constellab involves transforming each Nextflow process into a Constellab Task. The AI-powered conversion command automates most of this process while preserving the original logic.

Conversion Concept

Using the AI Conversion Command

The conversion is performed using the AI command /gws-nextflow-to-constellab.

Step 1: Prepare Your Nextflow Pipeline

Ensure your Nextflow pipeline is accessible in your workspace:

# Example structure
/lab/user/
├── nextflow/
│   ├── main.nf              # Your Nextflow pipeline
│   ├── nextflow.config      # Configuration file
│   └── modules/             # Process modules (optional)

Step 2: Invoke the Conversion Command

In the Constellab AI assistant, use:

/gws-nextflow-to-constellab /lab/user/nextflow/main.nf

Step 3: Review the Analysis

The AI will analyze your pipeline and present:

1. Pipeline Parameters: All params.* declarations with default values
2. Process Inventory: Each process with its inputs, outputs, and description
3. Workflow Structure: How processes are connected
4. Validation Questions:Which processes to convert?Convert bash scripts to Python or keep as shell commands?Confirm task namesTarget brick location

Example Analysis Output:

## Nextflow Pipeline Analysis

### Pipeline Parameters
- `input_file`: Input FASTQ file (default: "reads.fastq")
- `quality_threshold`: Minimum quality score (default: 30)
- `output_format`: Output format (default: "bam")

### Processes to Convert

#### 1. quality_filter
- **Input**: FASTQ file (`input_file`), Integer (`quality_threshold`)
- **Output**: `filtered.fastq`
- **Description**: Filters reads based on quality scores

#### 2. align_reads
- **Input**: FASTQ file (`filtered_file`), String (`output_format`)
- **Output**: Alignment file
- **Description**: Aligns reads to reference genome

### Workflow
quality_filter → align_reads

---

**Questions for Validation:**
1. Convert both processes? (Yes/No)
2. Convert bash to Python? (Recommended: Yes)
3. Task names: `QualityFilter` and `AlignReads`?
4. Create in which brick? (e.g., gws_bioinformatics)

Step 4: Confirm Conversion

Answer the validation questions:

Yes, convert both processes.
Convert bash to Python where possible.
Task names are good.
Create in a new brick called "gws_genomics".

Step 5: Automatic Task Generation

The AI will:

1. Generate Constellab Task classes for each process
2. Convert Nextflow script logic to Python
3. Map inputs/outputs to Constellab resources (File, Folder, etc.)
4. Create configuration parameters from Nextflow params
5. Add comprehensive documentation
6. Generate unit tests

Each generated task will be a standalone Python file in your specified brick:

bricks/
└── gws_genomics/
    └── src/
        └── gws_genomics/
            ├── quality_filter.py
            ├── align_reads.py
            └── ...

After Conversion: Using Your Tasks

1. Tasks are Available in the System

Once converted, your tasks are immediately available in Constellab:

• Navigate to the Task Library in the web interface
• Search for your newly created tasks (e.g., "QualityFilter", "AlignReads")
• View task documentation, inputs, outputs, and parameters

2. Create Scenarios

You can now run individual tasks as scenarios:

1. Go to Scenarios → Create New Scenario
2. Select a task from the library
3. Configure inputs and parameters
4. Execute and monitor the scenario
5. View results and execution traces

3. Build Protocol Templates (Recommended)

The Protocol Template is Constellab's equivalent to a Nextflow workflow. It allows you to:

• Chain tasks together in a logical sequence
• Define data flow between tasks
• Set default configurations for reproducible workflows
• Share and reuse complete pipelines

Creating a Protocol Template

Option A: Visual Protocol Editor (GUI)

1. Go to Protocols → Create New Protocol
2. Drag and drop tasks from the library onto the canvas
3. Connect task outputs to inputs by drawing links
4. Configure default parameters for each task
5. Add annotations and documentation
6. Save as a template

Benefits of Protocol Templates

• Reusability: Apply the same workflow to different datasets
• Consistency: Ensure standardized processing across experiments
• Collaboration: Share templates with team members
• Versioning: Track changes to workflow structure over time
• Parameterization: Create flexible templates with adjustable parameters

4. Execute Complete Workflows

Once your protocol template is created:

1. Create a Scenario from Template:Select your protocol templateProvide input dataAdjust parameters if neededRun the entire pipeline
2. Monitor Execution:Real-time progress trackingView logs for each taskInspect intermediate results
3. Access Results:Download output filesVisualize data with built-in viewsExport results to external systems
4. Trace Execution:Full audit trail of what ran, when, and with what parametersReproduce results by re-running with identical settingsCompare different runs side-by-side

Summary

Converting from Nextflow to Constellab offers:

• ✅ Automated conversion with AI assistance
• ✅ Preserved logic from original Nextflow processes
• ✅ Enhanced traceability and audit trails
• ✅ User-friendly interface for non-programmers
• ✅ Complete platform for data and pipeline management
• ✅ Flexible deployment in any environment
• ✅ Reusable protocol templates for standardized workflows

Start your conversion today with /gws-nextflow-to-constellab and experience the power of a complete data lab automation platform!