Pipelines

Overview

Pipelines are the computational heart of Flow - they are sophisticated bioinformatics workflows that transform raw sequencing data into meaningful biological insights. Each pipeline represents a complete, reproducible analysis workflow built using Nextflow, ensuring consistent results across different computing environments.

What is a Pipeline?

A pipeline in Flow is:

• A complete workflow: Chains together multiple bioinformatics tools into a coherent analysis
• Version controlled: Every pipeline version is tracked for reproducibility
• Containerized: Uses Docker/Singularity to ensure consistent environments
• Scalable: Automatically parallelizes across available compute resources
• Validated: Tested on reference datasets with known results

Pipeline Architecture

Nextflow Foundation

All Flow pipelines are built using Nextflow DSL2, which provides:

• Process isolation: Each step runs in its own container
• Automatic parallelization: Processes scale across samples
• Fault tolerance: Failed steps can be retried
• Resource optimization: Efficient use of CPU, memory, and storage

Pipeline Components

Each pipeline consists of:

1. Processes: Individual computational steps

   FASTQC → TRIMMING → ALIGNMENT → QUANTIFICATION
   

2. Channels: Data flow between processes

   Raw reads → QC reports
             → Trimmed reads → Aligned reads → Gene counts
   

3. Parameters: Configurable options

   • Input/output specifications
   • Tool-specific settings
   • Resource requirements

Pipeline Categories

Flow organizes pipelines into functional categories:

Primary Analysis

Processes raw sequencing data into initial results:

• RNA-seq: Gene expression quantification
• ChIP-seq: Chromatin binding analysis
• ATAC-seq: Chromatin accessibility
• WGS/WES: Variant calling
• Ampliseq: Amplicon analysis

Secondary Analysis

Performs downstream statistical analysis:

• Differential Expression: Compare conditions
• Peak Analysis: Find enriched regions
• Variant Annotation: Interpret mutations
• Pathway Analysis: Biological interpretation

Utilities

Helper pipelines for data management:

• Demultiplexing: Split pooled samples
• File Conversion: Format transformations
• Quality Control: Data validation
• Subsetting: Extract data subsets

Genome Preparation

Prepares reference genomes:

• Index Building: Create alignment indices
• Annotation Processing: Prepare gene models
• Custom References: User-specific genomes

Pipeline Inputs and Outputs

Input Types

Pipelines accept different input types:

1. Data Objects: Individual files or directories

   • FASTQ files for sequencing pipelines
   • BAM files for downstream analysis
   • Count matrices for statistical analysis

2. Sample Objects: Biological samples with metadata

   • Links to associated data files
   • Carries experimental metadata
   • Enables batch processing

3. Execution Objects: Results from other pipelines

   • Complete output directory structure
   • Useful for multi-stage analyses
   • Example: Genome preparation → Alignment

Output Structure

Pipeline outputs are organized hierarchically:

execution_id/
├── results/
│   ├── fastqc/           # QC reports
│   ├── trimming/         # Processed reads
│   ├── alignment/        # BAM files
│   └── quantification/   # Count matrices
├── pipeline_info/
│   ├── execution_report.html
│   └── execution_trace.txt
└── work/                 # Temporary files (cleaned up)

Pipeline Versions

Version Management

Each pipeline maintains multiple versions:

• Latest: Most recent stable version
• Specific versions: Tagged releases (e.g., v3.12.0)
• Development: Preview versions for testing

Version Selection

Consider these factors:

• Stability: Use latest stable for production
• Features: Check changelog for new capabilities
• Compatibility: Ensure input format compatibility
• Reproducibility: Record version for publications

Pipeline Configuration

Parameters

Pipelines expose parameters in categories:

1. Required Parameters

   • Input data paths
   • Output directory
   • Reference genome

2. Optional Parameters

   • Algorithm settings
   • Quality thresholds
   • Resource limits

3. Advanced Parameters

   • Tool-specific options
   • Performance tuning
   • Debug settings

Parameter Sources

Parameters can be set via:

• Web interface: User-friendly forms
• Parameter files: JSON/YAML configuration
• Command line: Direct specification
• Defaults: Pre-configured settings

Running Pipelines

Execution Flow

1. Select Pipeline: Choose from available pipelines
2. Configure Inputs: Select data/samples
3. Set Parameters: Adjust settings
4. Review Configuration: Verify settings
5. Submit: Create execution
6. Monitor: Track progress

Compute Resources

Pipelines automatically scale based on:

• Available cores: Parallel sample processing
• Memory limits: Per-process allocation
• Queue systems: SLURM, LSF, PBS support
• Cloud resources: AWS, GCP, Azure

Pipeline Development

Custom Pipelines

Flow supports custom pipeline development:

• Local development: Test on small datasets
• Version control: Git integration
• Schema definition: Define inputs/outputs
• Testing framework: Validate results

Best Practices

When developing pipelines:

1. Modular design: Reusable processes
2. Clear documentation: Usage instructions
3. Comprehensive testing: Multiple datasets
4. Resource profiles: Efficient allocation
5. Error handling: Graceful failures

Quality and Validation

Pipeline Testing

All pipelines undergo:

• Unit tests: Individual process validation
• Integration tests: Full workflow runs
• Regression tests: Result consistency
• Performance tests: Resource usage

Result Validation

Ensure quality through:

• Reference datasets: Known good results
• Quality metrics: Built-in QC checks
• Cross-validation: Multiple tool comparison
• Community standards: Follow best practices

Troubleshooting

Common Issues

Pipeline fails immediately

• Check input file formats
• Verify parameter syntax
• Ensure sufficient permissions

Pipeline runs but produces no output

• Review execution logs
• Check process exit codes
• Verify input data quality

Pipeline uses excessive resources

• Adjust parallelization settings
• Set memory limits
• Use subset for testing

Results differ from expected

• Verify pipeline version
• Check parameter settings
• Review input data quality

Getting Help

For pipeline issues:

1. Check execution logs
2. Review pipeline documentation
3. Search community forums
4. Contact pipeline maintainers

Best Practices

Before Running

1. Understand the pipeline: Read documentation
2. Check requirements: Input formats, reference data
3. Test on subset: Validate on small data
4. Plan resources: Estimate time and compute needs

During Execution

1. Monitor progress: Check status regularly
2. Review logs: Catch issues early
3. Document settings: Record for reproducibility
4. Save configurations: Reuse successful runs

After Completion

1. Validate results: Check quality metrics
2. Organize outputs: Move to project storage
3. Clean up: Remove temporary files
4. Document findings: Update project notes

Advanced Features

Pipeline Chaining

Connect pipelines for complex analyses:

Demultiplex → RNA-seq → Differential Expression
     ↓
  Samples → QC Reports → Count Matrix → DE Results

Batch Processing

Process multiple samples efficiently:

• Sample sheets: Define batch inputs
• Parallel execution: Multiple samples simultaneously
• Merged reporting: Combined quality metrics

Resource Optimization

Tune pipeline performance:

• Process-specific resources: Allocate per step
• Retry strategies: Handle transient failures
• Caching: Reuse intermediate results
• Profiles: Environment-specific settings

Next Steps

• Browse available pipelines
• Run your first pipeline
• Understand executions
• Create custom pipelines