Structural Variant Calling (Short Reads) Usage Guide

Structural Variant Calling (Short Reads) Usage Guide

Overview

Structural variants (SVs) are genomic alterations typically >50bp that include deletions, insertions, inversions, duplications, and translocations. Short-read SV calling uses paired-end and split-read information to detect these events.

Prerequisites

conda install -c bioconda manta delly smoove survivor annotsv bcftools pysam

Quick Start

Tell your AI agent what you want to do:

• "Call structural variants from my BAM file using multiple callers"
• "Run somatic SV calling on my tumor-normal pair with Manta and Delly"
• "Filter SV calls to keep only high-confidence variants with PE and SR support"
• "Annotate structural variants with gene overlaps and pathogenicity predictions"

SV Detection Signatures

Tool Selection Guide

Complete Pipeline

#!/bin/bash
set -euo pipefail

BAM=$1
REFERENCE=$2
SAMPLE=$(basename $BAM .bam)
OUTDIR=sv_calls/$SAMPLE

mkdir -p $OUTDIR

echo "=== Running SV calling for $SAMPLE ==="

# 1. Manta
echo "Running Manta..."
configManta.py \
    --bam $BAM \
    --referenceFasta $REFERENCE \
    --runDir $OUTDIR/manta

$OUTDIR/manta/runWorkflow.py -j 8
cp $OUTDIR/manta/results/variants/diploidSV.vcf.gz $OUTDIR/${SAMPLE}_manta.vcf.gz

# 2. Delly
echo "Running Delly..."
delly call -g $REFERENCE -o $OUTDIR/${SAMPLE}_delly.bcf $BAM
bcftools view $OUTDIR/${SAMPLE}_delly.bcf -Oz -o $OUTDIR/${SAMPLE}_delly.vcf.gz
bcftools index $OUTDIR/${SAMPLE}_delly.vcf.gz

# 3. Smoove (LUMPY)
echo "Running Smoove..."
smoove call --name $SAMPLE --fasta $REFERENCE --outdir $OUTDIR/smoove -p 8 $BAM
cp $OUTDIR/smoove/${SAMPLE}-smoove.genotyped.vcf.gz $OUTDIR/${SAMPLE}_lumpy.vcf.gz

# 4. Merge callers
echo "Merging callsets..."
gunzip -c $OUTDIR/${SAMPLE}_manta.vcf.gz > $OUTDIR/manta.vcf
gunzip -c $OUTDIR/${SAMPLE}_delly.vcf.gz > $OUTDIR/delly.vcf
gunzip -c $OUTDIR/${SAMPLE}_lumpy.vcf.gz > $OUTDIR/lumpy.vcf

echo "$OUTDIR/manta.vcf" > $OUTDIR/vcf_list.txt
echo "$OUTDIR/delly.vcf" >> $OUTDIR/vcf_list.txt
echo "$OUTDIR/lumpy.vcf" >> $OUTDIR/vcf_list.txt

SURVIVOR merge $OUTDIR/vcf_list.txt 500 2 1 1 0 50 $OUTDIR/${SAMPLE}_merged.vcf

# 5. Filter
echo "Filtering..."
bcftools view -i 'QUAL >= 20' $OUTDIR/${SAMPLE}_merged.vcf -Oz -o $OUTDIR/${SAMPLE}_final.vcf.gz
bcftools index $OUTDIR/${SAMPLE}_final.vcf.gz

# 6. Annotate
echo "Annotating..."
AnnotSV -SVinputFile $OUTDIR/${SAMPLE}_final.vcf.gz \
    -genomeBuild GRCh38 \
    -outputFile $OUTDIR/${SAMPLE}_annotated

echo "Done! Final SVs: $OUTDIR/${SAMPLE}_final.vcf.gz"

Somatic SV Calling

#!/bin/bash
TUMOR=$1
NORMAL=$2
REFERENCE=$3
OUTDIR=somatic_svs

mkdir -p $OUTDIR

# Manta somatic
configManta.py \
    --tumorBam $TUMOR \
    --normalBam $NORMAL \
    --referenceFasta $REFERENCE \
    --runDir $OUTDIR/manta

$OUTDIR/manta/runWorkflow.py -j 8

# Delly somatic
delly call -g $REFERENCE -o $OUTDIR/delly.bcf $TUMOR $NORMAL

echo -e "tumor\ttumor\nnormal\tcontrol" > $OUTDIR/samples.tsv

delly filter -f somatic -o $OUTDIR/delly_somatic.bcf \
    -s $OUTDIR/samples.tsv $OUTDIR/delly.bcf

bcftools view $OUTDIR/delly_somatic.bcf -Oz -o $OUTDIR/delly_somatic.vcf.gz

Quality Filtering

import pandas as pd
import pysam

def filter_sv_calls(vcf_in, vcf_out, min_qual=20, min_size=50, min_support=3):
    '''Filter SV calls by quality metrics.'''
    vcf = pysam.VariantFile(vcf_in)
    out = pysam.VariantFile(vcf_out, 'w', header=vcf.header)

    kept, filtered = 0, 0
    for rec in vcf:
        # Quality filter
        if rec.qual and rec.qual < min_qual:
            filtered += 1
            continue

        # Size filter
        svlen = abs(rec.info.get('SVLEN', [0])[0])
        if svlen < min_size:
            filtered += 1
            continue

        # Support filter (if available)
        support = rec.info.get('SUPPORT', rec.info.get('SR', [0]))
        if isinstance(support, tuple):
            support = sum(support)
        if support < min_support:
            filtered += 1
            continue

        out.write(rec)
        kept += 1

    out.close()
    print(f'Kept: {kept}, Filtered: {filtered}')

Interpreting SV VCF

Key INFO Fields

Format Fields

Benchmarking

Compare calls to truth set:

# Using Truvari
truvari bench \
    -b truth.vcf.gz \
    -c calls.vcf.gz \
    -o benchmark_results/ \
    --passonly \
    -r 500 \
    -p 0.7

Common Issues

High False Positives

• Filter by multiple caller support
• Require both PE and SR evidence
• Remove blacklist regions

Missing Large SVs

• Check BAM insert size distribution
• Ensure sufficient coverage
• Consider long-read sequencing

Breakpoint Imprecision

• Normal for short reads (~100bp uncertainty)
• Long reads improve resolution
• Use CIPOS/CIEND for confidence intervals

Relationship with CNV Detection

For complete structural variation analysis, combine SV callers with CNV detection tools.

Example Prompts

"Call structural variants from my BAM file using multiple callers and merge the results"

"Run somatic SV calling on my tumor-normal pair with Manta and Delly"

"Filter my SV calls to keep only high-confidence variants with PE and SR support"

"Annotate my structural variants with gene overlaps and pathogenicity predictions"