nf-core/longraredisease is a specialized bioinformatics pipeline for structural variant (SV) detection and clinical interpretation from long-read sequencing data (Oxford Nanopore and PacBio). Designed for rare disease diagnostics, it delivers high-confidence variant discovery through multi-caller consensus, family-based analysis, and phenotype-driven prioritization.

The pipeline excels at identifying and interpreting structural variants through:

• Multi-caller SV consensus - Sniffles, CuteSV, SVIM with JASMINE merging
• Phase-aware calling - Haplotype-resolved SV detection using LongPhase
• Family analysis - Trio-based joint calling and de novo variant detection
• Clinical annotation - AnnotSV with disease database integration
• Phenotype prioritization - SVANNA-based ranking using HPO terms

Core SV Analysis (Always Enabled):

• ✅ Structural Variants - Multi-caller detection (DEL, INS, DUP, INV, BND)
• ✅ Phasing - Long-range haplotyping with LongPhase
• ✅ Quality Control - Comprehensive QC with NanoPlot, mosdepth, MultiQC

Optional Analyses:

• 🧬 Single Nucleotide Variants - Clair3 or DeepVariant (enable with --snv true)
• 📈 Copy Number Variants - Spectre or HiFiCNV (enable with --cnv true)
• 🔁 Short Tandem Repeats - Straglr genotyping (enable with --str true)
• 🧪 DNA Methylation - Modkit extraction for ONT (enable with --methyl true)

• Nextflow ≥25.04.6 (DSL2)
• Container engine: Docker, Singularity/Apptainer, or Podman
• Java ≥17 (required by Nextflow)

Notes:

• Coverage recommendations: ≥10x for accurate SV calling, ≥30x for high-confidence trio analysis
• Storage includes space for input data, intermediate files, and results
• Adjust --max_cpus and --max_memory parameters based on available resources

# Install Nextflow (≥25.04.6) curl -s https://get.nextflow.io | bash sudo mv nextflow /usr/local/bin/ # Verify installation nextflow -version

# Run with test data nextflow run nf-core/longraredisease \ -profile test,docker \ --outdir test_results

Minimal SV-focused run:

nextflow run nf-core/longraredisease \ --input samplesheet.csv \ --outdir results \ --fasta reference.fasta \ --sequencing_platform ont \ -profile docker

With family analysis and phenotype prioritization:

nextflow run nf-core/longraredisease \ --input samplesheet.csv \ --outdir results \ --fasta reference.fasta \ --sequencing_platform ont \ --trio_analysis true \ --run_svanna true \ --svanna_db /path/to/svanna_db \ -profile docker

See docs/usage.md for complete examples and parameter details.

The input samplesheet is a CSV file with the following columns:

Minimal format (single samples):

sample,bam,bai sample1,/path/to/sample1.bam,/path/to/sample1.bam.bai sample2,/path/to/sample2.bam,/path/to/sample2.bam.bai

Family analysis format (trios):

sample,bam,bai,family,paternal_id,maternal_id,sex,phenotype,hpo_terms proband,proband.bam,proband.bam.bai,family1,father,mother,1,affected,"HP:0001250,HP:0002066" father,father.bam,father.bam.bai,family1,0,0,1,unaffected, mother,mother.bam,mother.bam.bai,family1,0,0,2,unaffected,

Column descriptions:

• sample - Unique sample identifier
• bam - Path to aligned BAM file
• bai - Path to BAM index file
• family - Family identifier (for trio analysis)
• paternal_id - Father's sample ID (or 0 if not in study)
• maternal_id - Mother's sample ID (or 0 if not in study)
• sex - 1 = male, 2 = female, 0 = unknown
• phenotype - affected or unaffected
• hpo_terms - Comma-separated HPO terms (e.g., HP:0001250,HP:0002066)

Structural variant analysis is always enabled. Optional analyses:

nextflow run nf-core/longraredisease \ --input samplesheet.csv \ --outdir results \ --fasta GRCh38.fasta \ --sequencing_platform ont \ -profile docker

nextflow run nf-core/longraredisease \ --input samplesheet.csv \ --outdir results \ --fasta GRCh38.fasta \ --sequencing_platform pacbio \ --snv true \ --cnv true \ --str true \ -profile singularity

nextflow run nf-core/longraredisease \ --input trio_samplesheet.csv \ --outdir family_results \ --fasta GRCh38.fasta \ --sequencing_platform ont \ --trio_analysis true \ --run_svanna true \ --svanna_db /databases/svanna_data \ --annotsv_db /databases/AnnotSV \ -profile docker

nextflow run nf-core/longraredisease \ --input samplesheet.csv \ --outdir sensitive_results \ --fasta GRCh38.fasta \ --sequencing_platform ont \ --run_svim true \ --min_sv_size 20 \ --min_read_support 2 \ --jasmine_min_support 1 \ -profile docker

nextflow run nf-core/longraredisease \ --input samplesheet.csv \ --outdir targeted_results \ --fasta GRCh38.fasta \ --bed targets.bed \ --sequencing_platform ont \ -profile docker

results/ ├── pipeline_info/ # Pipeline execution reports │ ├── execution_report.html # Resource usage timeline │ ├── execution_timeline.html # Process execution graph │ └── multiqc_report.html # Comprehensive QC report │ ├── qc/ # Quality control metrics │ ├── mosdepth/ # Coverage statistics per sample │ ├── nanoplot/ # Read quality metrics (ONT) │ └── cramino/ # CRAM-based QC (optional) │ ├── structural_variants/ # 🎯 PRIMARY OUTPUT: SV calls │ ├── sniffles/ # Per-sample Sniffles VCFs │ │ └── {sample}.sniffles.vcf.gz │ ├── cutesv/ # Per-sample CuteSV VCFs │ │ └── {sample}.cutesv.vcf.gz │ ├── svim/ # Per-sample SVIM VCFs (if enabled) │ │ └── {sample}.svim.vcf.gz │ ├── merged/ # Multi-caller consensus SVs │ │ ├── {sample}.jasmine.vcf.gz │ │ └── {sample}.survivor.vcf.gz │ ├── annotated/ # AnnotSV annotations │ │ └── {sample}.annotated.tsv │ └── svanna/ # Phenotype-prioritized SVs │ └── {sample}.svanna.html │ ├── phasing/ # Haplotype-resolved results │ ├── haplotagged_bams/ # Phase-tagged alignments │ │ └── {sample}.haplotagged.bam │ ├── whatshap/ # Phasing statistics │ │ └── {sample}.phased.vcf.gz │ └── longphase/ # Alternative phasing │ └── {sample}.longphase.vcf.gz │ ├── snv_calls/ # SNVs (if --snv enabled) │ ├── clair3/ │ │ └── {sample}.clair3.vcf.gz │ └── deepvariant/ │ └── {sample}.deepvariant.vcf.gz │ ├── cnv_calls/ # CNVs (if --cnv enabled) │ └── spectre/ │ └── {sample}.cnv.vcf.gz │ ├── str_calls/ # STRs (if --str enabled) │ └── straglr/ │ └── {sample}.straglr.tsv │ └── methylation/ # Methylation (if --methyl enabled, ONT only) └── modkit/ └── {sample}.bedmethyl.gz 

Key output files:

• Merged SVs: structural_variants/merged/{sample}.jasmine.vcf.gz (high-confidence consensus)
• Annotated SVs: structural_variants/annotated/{sample}.annotated.tsv (clinical interpretation)
• QC Report: pipeline_info/multiqc_report.html (overall quality assessment)
• Phenotype-prioritized: structural_variants/svanna/{sample}.svanna.html (ranked by phenotype match)

Available Profiles:

• test: Minimal test dataset
• docker: Use Docker containers
• singularity: Use Singularity containers

Custom Configuration

// custom.config params { max_cpus = 16 max_memory = '64.GB' outdir = '/scratch/results' } process { withName: 'CLAIR3' { cpus = 8 memory = '32.GB' } }

Run with:

nextflow run main.nf -c custom.config -profile docker

For family-based SV analysis, provide pedigree information in your samplesheet:

sample,bam,bai,family,paternal_id,maternal_id,sex,phenotype,hpo_terms child_001,child.bam,child.bam.bai,FAM001,father_001,mother_001,2,affected,"HP:0001250,HP:0002066,HP:0001263" father_001,father.bam,father.bam.bai,FAM001,0,0,1,unaffected, mother_001,mother.bam,mother.bam.bai,FAM001,0,0,2,unaffected,

Sex encoding: 1 = male, 2 = female, 0 = unknown Parental IDs: Use 0 for founders (individuals with no parents in the study)

Enable trio analysis to identify de novo structural variants:

nextflow run nf-core/longraredisease \ --input trio_samplesheet.csv \ --trio_analysis true \ --outdir trio_results \ --fasta GRCh38.fasta \ --sequencing_platform ont \ -profile docker

The pipeline will:

1. ✅ Call SVs in each family member independently
2. ✅ Merge calls using JASMINE with family-aware parameters
3. ✅ Identify variants present in child but absent in parents
4. ✅ Filter based on read support and quality metrics

When HPO terms are provided, SVANNA ranks SVs by phenotype relevance:

nextflow run nf-core/longraredisease \ --input trio_samplesheet.csv \ --trio_analysis true \ --run_svanna true \ --svanna_db /path/to/svanna/2302 \ --outdir prioritized_results \ --fasta GRCh38.fasta \ --sequencing_platform ont \ -profile docker

Required: Download SVANNA database from Monarch Initiative

Output: HTML report ranking SVs by:

• Overlap with disease-associated genes
• Regulatory impact predictions
• Phenotype similarity scores
• De novo status (if trio data available)

Enable comprehensive SV annotation:

nextflow run nf-core/longraredisease \ --input samplesheet.csv \ --annotsv_db /path/to/AnnotSV_db \ --outdir annotated_results \ --fasta GRCh38.fasta \ --sequencing_platform ont \ -profile docker

AnnotSV provides:

• Gene overlap and functional impact
• ClinGen/ClinVar annotations
• DGV/gnomAD population frequencies
• Pathogenicity predictions (ACMG criteria)
• Regulatory element disruption

Symptoms: Fewer SVs than expected

Solutions:

# Lower read support threshold --min_read_support 2 # Reduce minimum SV size --min_sv_size 20 # Enable SVIM for better breakend detection --run_svim true # Lower consensus requirement --jasmine_min_support 1

Symptoms: Many low-quality SV calls

Solutions:

# Increase read support --min_read_support 5 # Require multiple caller agreement --jasmine_min_support 2 # Increase minimum SV size --min_sv_size 50

Symptoms: Process killed due to OOM

Solutions:

# Increase max memory --max_memory 128.GB # Reduce parallel processes --max_cpus 16 # Use chromosome-based parallelization (automatic)

Symptoms: Expected de novo variants not detected

Checklist:

• ✅ Ensure --trio_analysis true is set
• ✅ Verify pedigree information in samplesheet
• ✅ Check read coverage in all samples (≥30×)
• ✅ Review structural_variants/merged/ for family calls
• ✅ Lower --jasmine_min_support if needed

Symptoms: SVANNA fails or produces no rankings

Solutions:

# Verify database path and version ls -lh /path/to/svanna/2302 # Ensure HPO terms are valid (HP:XXXXXXX format) # Check samplesheet for proper HPO term formatting # Download latest SVANNA database: wget https://storage.googleapis.com/svanna-db/svanna-data-2302.tar.gz tar -xzf svanna-data-2302.tar.gz

For large cohorts (>10 samples):

# Enable resource-efficient mode --max_cpus 64 --max_memory 256.GB # Use Singularity for better resource isolation -profile singularity # Enable work directory cleanup -resume -with-dag flowchart.html

For whole genome sequencing:

• Expect 8-24 hours runtime (depending on coverage)
• Allocate 64-128GB RAM per sample for SV calling
• Use SSD storage for work directory (I/O intensive)

The pipeline includes test data for validation:

• Location: assets/test_data/
• Genome: Chromosome 22 subset
• Samples: Simulated nanopore data
• Runtime: ~10-15 minutes

Debugging Failed Runs:

# Check Nextflow log for detailed errors less .nextflow.log # Resume from last successful step nextflow run nf-core/longraredisease -resume # Enable debug mode for verbose output nextflow run nf-core/longraredisease --debug -profile docker

Reporting Issues:

When reporting issues, please include:

• Nextflow version (nextflow -version)
• Command used to run the pipeline
• Relevant error messages from .nextflow.log
• Sample metadata (anonymized if sensitive)
• System specifications (CPU, RAM, storage)

If you use nf-core/longraredisease in your research, please cite:

nf-core/longraredisease: A Nextflow pipeline for long-read sequencing analysis in rare disease research > Citation to be added upon publication

Additionally, please cite the tools used in your analysis:

Core SV Tools:

• Sniffles2: Sedlazeck et al. (2018) Nature Methods
• CuteSV: Jiang et al. (2020) Genome Biology
• JASMINE: Kirsche et al. (2023) Nature Methods
• LongPhase: Luo et al. (2023) Nature Communications
• AnnotSV: Geoffroy et al. (2018) Bioinformatics

Optional Analysis Tools:

• SVANNA: Danis et al. (2022) AJHG
• Clair3: Zheng et al. (2022) Nature Computational Science
• Spectre: Suvakov et al. (2021) Genome Research
• Straglr: Chin et al. (2023) Genome Research

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Make your changes following nf-core guidelines
4. Test with nextflow run . -profile test,docker
5. Commit your changes (git commit -m 'Add AmazingFeature')
6. Push to the branch (git push origin feature/AmazingFeature)
7. Open a Pull Request

Please ensure:

• ✅ Code follows nf-core style guidelines
• ✅ All tests pass successfully
• ✅ Documentation is updated accordingly
• ✅ Commit messages are descriptive

This project is licensed under the MIT License – see the LICENSE file for details.

This pipeline was developed with support from [institution/funding sources]. We thank the nf-core community for infrastructure and best practices, and all tool developers whose software makes this pipeline possible.

Pipeline Version: 1.0.0 Nextflow Version: ≥25.04.6 Last Updated: 2024