Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Call structural variants (SVs) from short-read sequencing using Manta, Delly, and LUMPY. Detects deletions, insertions, inversions, duplications, and translocations that are too large for standard SNV callers. Use when detecting structural variants from short-read data.
.claude/skills/bio-variant-calling-structural-variant-calling/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-18 | ✗→✓ | ▲ Improved | — | — |
| case-22 | ✓→✓ | = Same ✓ | — | — |
| case-10 | ✓→✓ | = Same ✓ | — | — |
| case-13 | ✗→✗ | = Same ✗ | — | — |
| case-17 | ✗→✗ | = Same ✗ | — | — |
Reference examples tested with: bcftools 1.19+, samtools 1.19+
Before using code patterns, verify installed versions match. If versions differ:
<tool> --version then <tool> --help to confirm flagsIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Call structural variants from my WGS data" → Detect large genomic rearrangements (deletions, insertions, inversions, duplications, translocations) using split-read and discordant-pair evidence.
configManta.py (Manta), delly call, lumpyexpress/smoove callbash# Configure Manta run (creates runWorkflow.py) configManta.py \ --bam sample.bam \ --referenceFasta reference.fa \ --runDir manta_run # Execute manta_run/runWorkflow.py -j 8 # Output: manta_run/results/variants/ # - diploidSV.vcf.gz (germline SVs) # - candidateSV.vcf.gz (all candidates) # - candidateSmallIndels.vcf.gz (small indels)
bash# Somatic SV calling configManta.py \ --tumorBam tumor.bam \ --normalBam normal.bam \ --referenceFasta reference.fa \ --runDir manta_somatic manta_somatic/runWorkflow.py -j 8 # Output includes: # - somaticSV.vcf.gz (somatic SVs) # - diploidSV.vcf.gz (germline SVs)
bash# WES mode (for exome data) configManta.py \ --bam sample.bam \ --referenceFasta reference.fa \ --exome \ # Use exome settings --callRegions regions.bed.gz \ # Restrict to regions --runDir manta_exome # RNA-seq mode configManta.py \ --bam rnaseq.bam \ --referenceFasta reference.fa \ --rna \ # RNA-seq mode --runDir manta_rna
bash# Call SVs delly call \ -g reference.fa \ -o sv_calls.bcf \ sample.bam # Convert to VCF bcftools view sv_calls.bcf > sv_calls.vcf # Multiple samples (joint calling) delly call \ -g reference.fa \ -o joint_svs.bcf \ sample1.bam sample2.bam sample3.bam
bash# Call with tumor-normal delly call \ -g reference.fa \ -o svs.bcf \ tumor.bam normal.bam # Create sample file echo -e "tumor\ttumor\nnormal\tcontrol" > samples.tsv # Filter for somatic delly filter \ -f somatic \ -o somatic_svs.bcf \ -s samples.tsv \ svs.bcf
bash# Call specific SV type delly call -t DEL -g ref.fa -o deletions.bcf sample.bam delly call -t DUP -g ref.fa -o duplications.bcf sample.bam delly call -t INV -g ref.fa -o inversions.bcf sample.bam delly call -t BND -g ref.fa -o translocations.bcf sample.bam delly call -t INS -g ref.fa -o insertions.bcf sample.bam
bash# Extract split reads and discordant pairs samtools view -b -F 1294 sample.bam > discordant.bam samtools view -h sample.bam | \ /path/to/lumpy-sv/scripts/extractSplitReads_BwaMem -i stdin | \ samtools view -Sb - > splitters.bam # Run LUMPY lumpyexpress \ -B sample.bam \ -S splitters.bam \ -D discordant.bam \ -o lumpy_svs.vcf
bash# Simplified LUMPY workflow smoove call \ --name sample \ --fasta reference.fa \ --outdir smoove_output \ -p 8 \ sample.bam # Output: smoove_output/sample-smoove.genotyped.vcf.gz
Goal: Increase confidence in SV calls by requiring support from multiple callers.
Approach: Run 2-3 callers independently, then merge callsets with SURVIVOR requiring agreement on breakpoint proximity and SV type.
bash# Use SURVIVOR to merge callsets # Create file listing VCFs ls manta_svs.vcf delly_svs.vcf lumpy_svs.vcf > vcf_list.txt # Merge with parameters SURVIVOR merge vcf_list.txt 1000 2 1 1 0 50 merged_svs.vcf # Parameters: max_dist min_callers type_agree strand_agree estimate_dist min_size
bash# Filter by quality bcftools view -i 'QUAL >= 20' svs.vcf > svs.filtered.vcf # Filter by size bcftools view -i 'ABS(SVLEN) >= 50' svs.vcf > svs.min50.vcf # Filter by SV type bcftools view -i 'SVTYPE="DEL"' svs.vcf > deletions.vcf bcftools view -i 'SVTYPE="INS"' svs.vcf > insertions.vcf bcftools view -i 'SVTYPE="INV"' svs.vcf > inversions.vcf bcftools view -i 'SVTYPE="DUP"' svs.vcf > duplications.vcf bcftools view -i 'SVTYPE="BND"' svs.vcf > translocations.vcf # Keep only PASS bcftools view -f PASS svs.vcf > svs.pass.vcf
bash# AnnotSV annotation AnnotSV \ -SVinputFile svs.vcf \ -genomeBuild GRCh38 \ -outputFile annotated_svs # Output includes: genes, DGV, gnomAD-SV, ClinVar
| Type | Code | Description | |------|------|-------------| | Deletion | DEL | Sequence removed | | Insertion | INS | Sequence inserted | | Inversion | INV | Sequence reversed | | Duplication | DUP | Sequence duplicated | | Translocation | BND | Breakend (inter-chromosomal) |
| Feature | Manta | Delly | LUMPY | |---------|-------|-------|-------| | Speed | Fast | Medium | Medium | | Sensitivity | High | High | High | | Small SVs | Good | Moderate | Good | | Large SVs | Good | Good | Good | | RNA-seq | Yes | No | No | | Somatic | Yes | Yes | Limited |
| Coverage | Detection Ability | |----------|-------------------| | 10x | Large SVs (>1kb) | | 30x | Most SVs | | 50x+ | Small SVs, better breakpoints |
For long-read data (ONT/PacBio HiFi), use specialized callers with higher sensitivity:
| Caller | Best For | Notes | |--------|----------|-------| | CuteSV | ONT/HiFi | Fast, accurate for all SV types | | Sniffles2 | ONT/HiFi | Population-scale, multisample | | PBSV | PacBio | Official PacBio caller |
See long-read-sequencing/structural-variants for long-read SV workflows.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-13 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-17 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-08 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-16 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-02 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-15 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-11 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-22 | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-20 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-14 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-19 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-05 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-04 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-12 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-10 | pass→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-01 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-03 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-06 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-07 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-09 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
case-18 | fail→pass | — | — | — | — | — | — | — | — | — | — | — | — |
case-21 | fail→fail | — | — | — | — | — | — | — | — | — | — | — | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +5 percentage points is the difference between those two pass rates over the 22 comparable cases.
The per-case answers from this run were removed by the retention sweep, so the case table below shows the verdicts without the text either arm produced. The counts above were recorded at the time and are unaffected. Answers are now kept for 180 days.
Other measured skills in the registry, with their headline benchmark lift.