bio-variant-calling-structural-variant-calling

name	bio-variant-calling-structural-variant-calling
description	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.
tool_type	cli
primary_tool	manta

Version Compatibility

Reference examples tested with: bcftools 1.19+, samtools 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 Calling (Short Reads)

"Call structural variants from my WGS data" → Detect large genomic rearrangements (deletions, insertions, inversions, duplications, translocations) using split-read and discordant-pair evidence.

• CLI: configManta.py (Manta), delly call, lumpyexpress/smoove call

Manta (Recommended)

# 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)

Manta Tumor-Normal Mode

# 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)

Manta Options

# 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

Delly

# 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

Delly Somatic Mode

# 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

Delly SV Types

# 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

LUMPY

# 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

Smoove (LUMPY Wrapper)

# 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

Merge Multiple Callers

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.

# 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

Filter SV Calls

# 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

Annotate SVs

# AnnotSV annotation
AnnotSV \
    -SVinputFile svs.vcf \
    -genomeBuild GRCh38 \
    -outputFile annotated_svs

# Output includes: genes, DGV, gnomAD-SV, ClinVar

SV Types

Type	Code	Description
Deletion	DEL	Sequence removed
Insertion	INS	Sequence inserted
Inversion	INV	Sequence reversed
Duplication	DUP	Sequence duplicated
Translocation	BND	Breakend (inter-chromosomal)

Comparison: Manta vs Delly vs LUMPY

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 Guidelines

Coverage	Detection Ability
10x	Large SVs (>1kb)
30x	Most SVs
50x+	Small SVs, better breakpoints

Long-Read SV Callers

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.

Related Skills

• long-read-sequencing/structural-variants - Long-read SV calling
• copy-number/cnvkit-analysis - Copy number variants
• variant-calling/filtering-best-practices - Filter VCF files
• alignment-files/alignment-filtering - Prepare BAM files