home / skills / gptomics / bioskills / structural-variants
This skill detects structural variants from long-read alignments using Sniffles, cuteSV, and SVIM, enabling accurate calls and breakpoint resolution.
npx playbooks add skill gptomics/bioskills --skill structural-variantsReview the files below or copy the command above to add this skill to your agents.
---
name: bio-longread-structural-variants
description: Detect structural variants from long-read alignments using Sniffles, cuteSV, and SVIM. Use when detecting deletions, insertions, inversions, translocations, or complex rearrangements from ONT or PacBio data, especially those missed by short-read methods.
tool_type: cli
primary_tool: sniffles
---
## Version Compatibility
Reference examples tested with: bcftools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
- CLI: `<tool> --version` then `<tool> --help` to confirm flags
If code throws ImportError, AttributeError, or TypeError, introspect the installed
package and adapt the example to match the actual API rather than retrying.
# Structural Variant Detection
**"Call structural variants from my long reads"** → Detect large deletions, insertions, inversions, duplications, and translocations with precise breakpoint resolution from ONT or PacBio alignments.
- CLI: `sniffles --input aligned.bam --vcf svs.vcf`, `cuteSV aligned.bam ref.fa svs.vcf output/`
## Sniffles2 - Basic SV Calling
```bash
# Call SVs from aligned BAM
sniffles --input aligned.bam \
--vcf structural_variants.vcf \
--reference reference.fa \
--threads 4
```
## Sniffles2 - Common Options
```bash
sniffles --input aligned.bam \
--vcf structural_variants.vcf \
--reference reference.fa \
--threads 8 \
--minsupport 3 \ # Min supporting reads
--minsvlen 50 \ # Min SV length
--mapq 20 \ # Min mapping quality
--output-rnames \ # Include read names
--mosaic # Detect mosaic SVs
```
## Sniffles2 - Population Calling
**Goal:** Jointly call and genotype structural variants across a cohort of long-read samples for population-level SV analysis.
**Approach:** Generate per-sample SNF signature files from individual BAMs, then merge and jointly genotype all samples in a single Sniffles2 call.
```bash
# Step 1: Call SVs per sample with SNF output
sniffles --input sample1.bam --snf sample1.snf --reference reference.fa
sniffles --input sample2.bam --snf sample2.snf --reference reference.fa
# Step 2: Merge and genotype
sniffles --input sample1.snf sample2.snf \
--vcf population_svs.vcf \
--reference reference.fa
```
## cuteSV - Alternative Caller
```bash
# cuteSV SV calling
cuteSV aligned.bam reference.fa output.vcf work_dir/ \
--threads 8 \
--min_support 3 \
--min_size 50 \
--genotype
```
## cuteSV - ONT Optimized
```bash
# Settings optimized for ONT
cuteSV aligned.bam reference.fa output.vcf work_dir/ \
--threads 8 \
--max_cluster_bias_INS 100 \
--diff_ratio_merging_INS 0.3 \
--max_cluster_bias_DEL 100 \
--diff_ratio_merging_DEL 0.3 \
--genotype
```
## cuteSV - PacBio HiFi Optimized
```bash
# Settings optimized for HiFi
cuteSV aligned.bam reference.fa output.vcf work_dir/ \
--threads 8 \
--max_cluster_bias_INS 1000 \
--diff_ratio_merging_INS 0.9 \
--max_cluster_bias_DEL 1000 \
--diff_ratio_merging_DEL 0.5 \
--genotype
```
## SVIM - Another Alternative
```bash
# SVIM for ONT data
svim alignment output_dir/ aligned.bam reference.fa \
--insertion_sequences \
--read_names \
--sample sample_name
```
## pbsv - PacBio Specific
```bash
# Discover signatures
pbsv discover aligned.bam signatures.svsig.gz
# Call SVs
pbsv call reference.fa signatures.svsig.gz structural_variants.vcf
```
## Filter SV Calls
```bash
# Filter by quality and size
bcftools filter -i 'QUAL>=20 && ABS(SVLEN)>=50' svs.vcf > svs.filtered.vcf
# Keep only PASS
bcftools view -f PASS svs.vcf > svs.pass.vcf
# Filter specific SV types
bcftools view -i 'SVTYPE="DEL"' svs.vcf > deletions.vcf
bcftools view -i 'SVTYPE="INS"' svs.vcf > insertions.vcf
```
## Merge Multiple Callers
```bash
# Use SURVIVOR to merge SV callsets
SURVIVOR merge sample_files.txt 1000 2 1 1 0 50 merged_svs.vcf
# sample_files.txt contains VCF paths, one per line
# Parameters: max_distance, min_callers, type_agree, strand_agree, est_distance, min_size
```
## Annotate SVs
```bash
# Annotate with AnnotSV
AnnotSV -SVinputFile svs.vcf \
-genomeBuild GRCh38 \
-outputFile annotated_svs
# Or with bcftools
bcftools annotate -a gnomad_sv.vcf.gz -c INFO svs.vcf > svs.annotated.vcf
```
## SV Types
| Type | Code | Description |
|------|------|-------------|
| Deletion | DEL | Sequence removed |
| Insertion | INS | Sequence added |
| Inversion | INV | Sequence inverted |
| Duplication | DUP | Sequence duplicated |
| Translocation | BND | Breakend (complex) |
## Key Parameters - Sniffles2
| Parameter | Default | Description |
|-----------|---------|-------------|
| --minsupport | auto | Min supporting reads |
| --minsvlen | 50 | Min SV length |
| --mapq | 20 | Min mapping quality |
| --reference | none | Reference (for INS sequences) |
| --tandem-repeats | none | BED of tandem repeats |
| --mosaic | off | Detect mosaic SVs |
## Key Parameters - cuteSV
| Parameter | Default | Description |
|-----------|---------|-------------|
| --min_support | 10 | Min supporting reads |
| --min_size | 30 | Min SV length |
| --max_size | 100000 | Max SV length |
| --genotype | off | Output genotypes |
| --report_readid | off | Report read IDs |
## Coverage Guidelines
| Coverage | SV Detection |
|----------|--------------|
| 5-10x | Large SVs (>1kb) |
| 10-20x | Most SVs |
| 20-30x | High confidence |
| >30x | Mosaic/rare SVs |
## Related Skills
- long-read-alignment - Generate input BAM
- medaka-polishing - Polish assembly with SVs
- variant-calling/structural-variant-calling - Short-read SV comparison
This skill detects structural variants from long-read alignments using Sniffles, cuteSV, and SVIM to find deletions, insertions, inversions, duplications, translocations, and complex rearrangements. It is optimized for ONT and PacBio data and for cases where short-read methods miss events. The skill includes caller options, population calling patterns, filtering, merging, and annotation guidance.
The skill inspects long-read alignments (BAM/CRAM) and runs one or more SV callers to produce VCF outputs. It covers per-sample discovery (Sniffles2, cuteSV, SVIM), joint population calling using Sniffles SNF files, and downstream steps: filtering with bcftools, merging with SURVIVOR, and annotation with AnnotSV or bcftools annotate. It also provides caller-specific tuning for ONT and PacBio HiFi.
Which caller should I pick for ONT versus PacBio HiFi?
Sniffles2 and cuteSV work well for both; tune parameters for platform bias. SVIM is a strong alternative for ONT. For PacBio HiFi, increase merging thresholds in cuteSV or use pbsv for PacBio-specific workflows.
How do I reduce false positives?
Apply quality and size filters (QUAL>=20, min SVLEN ~50), require support from multiple callers via SURVIVOR, and inspect calls in IGV with read-level evidence.