Bio Workflows Fastq To Variants

Primary Path: BWA + bcftools

Step 1: Quality Control with fastp

# Single sample
fastp -i sample_R1.fastq.gz -I sample_R2.fastq.gz \
    -o sample_R1.trimmed.fq.gz -O sample_R2.trimmed.fq.gz \
    --detect_adapter_for_pe \
    --qualified_quality_phred 20 \
    --length_required 50 \
    --html sample_fastp.html

# Batch processing
for sample in sample1 sample2 sample3; do
    fastp -i ${sample}_R1.fastq.gz -I ${sample}_R2.fastq.gz \
        -o trimmed/${sample}_R1.fq.gz -O trimmed/${sample}_R2.fq.gz \
        --detect_adapter_for_pe \
        --html qc/${sample}_fastp.html
done

QC Checkpoint 1: Check fastp reports

• Q30 bases >85% (DNA typically higher quality than RNA)
• Adapter content <1%
• No unusual GC distribution

Step 2: BWA-MEM2 Alignment

# Index reference (once)
bwa-mem2 index reference.fa

# Align with read group info
for sample in sample1 sample2 sample3; do
    bwa-mem2 mem -t 8 \
        -R "@RG\tID:${sample}\tSM:${sample}\tPL:ILLUMINA\tLB:lib1" \
        reference.fa \
        trimmed/${sample}_R1.fq.gz \
        trimmed/${sample}_R2.fq.gz \
    | samtools view -bS - > aligned/${sample}.bam
done

QC Checkpoint 2: Check alignment stats

samtools flagstat aligned/${sample}.bam

• Mapped reads >95%
• Properly paired >90%

Step 3: BAM Processing

for sample in sample1 sample2 sample3; do
    # Sort by coordinate
    samtools sort -@ 8 -o aligned/${sample}.sorted.bam aligned/${sample}.bam

    # Mark duplicates (samtools method)
    samtools fixmate -m aligned/${sample}.sorted.bam - | \
        samtools sort -@ 8 - | \
        samtools markdup -@ 8 - aligned/${sample}.markdup.bam

    # Index
    samtools index aligned/${sample}.markdup.bam

    # Cleanup intermediate
    rm aligned/${sample}.bam aligned/${sample}.sorted.bam
done

QC Checkpoint 3: Check duplication rate

samtools flagstat aligned/${sample}.markdup.bam | grep "duplicates"

• WGS: <30% duplicates
• Exome/targeted: <50% duplicates

Step 4: Variant Calling with bcftools

# Single sample calling
bcftools mpileup -Ou -f reference.fa aligned/sample1.markdup.bam | \
    bcftools call -mv -Oz -o variants/sample1.vcf.gz

# Multi-sample calling (joint calling)
bcftools mpileup -Ou -f reference.fa \
    aligned/sample1.markdup.bam \
    aligned/sample2.markdup.bam \
    aligned/sample3.markdup.bam | \
    bcftools call -mv -Oz -o variants/cohort.vcf.gz

bcftools index variants/cohort.vcf.gz

Step 5: Variant Filtering

# Basic quality filter
bcftools filter -Oz \
    -e 'QUAL<20 || DP<10 || MQ<30' \
    -o variants/cohort.filtered.vcf.gz \
    variants/cohort.vcf.gz

# More stringent filter
bcftools filter -Oz \
    -e 'QUAL<30 || DP<10 || DP>200 || MQ<40 || MQB<0.1' \
    -s "LowQual" \
    -o variants/cohort.filtered.vcf.gz \
    variants/cohort.vcf.gz

# Stats
bcftools stats variants/cohort.filtered.vcf.gz > variants/vcf_stats.txt

QC Checkpoint 4: Check variant stats

• Ti/Tv ratio ~2.1 for whole genome
• Ti/Tv ratio ~2.8-3.0 for exome

• 95% overlap with dbSNP for known sites

Alternative Path: BWA + GATK HaplotypeCaller

Step 4 Alternative: GATK Variant Calling

# Create sequence dictionary (once)
gatk CreateSequenceDictionary -R reference.fa

# Index reference (once)
samtools faidx reference.fa

# Base Quality Score Recalibration (BQSR)
gatk BaseRecalibrator \
    -R reference.fa \
    -I aligned/sample1.markdup.bam \
    --known-sites dbsnp.vcf.gz \
    -O recal_data.table

gatk ApplyBQSR \
    -R reference.fa \
    -I aligned/sample1.markdup.bam \
    --bqsr-recal-file recal_data.table \
    -O aligned/sample1.recal.bam

# HaplotypeCaller (per-sample GVCF mode)
gatk HaplotypeCaller \
    -R reference.fa \
    -I aligned/sample1.recal.bam \
    -O variants/sample1.g.vcf.gz \
    -ERC GVCF

# Joint genotyping (for multiple samples)
gatk GenomicsDBImport \
    -V variants/sample1.g.vcf.gz \
    -V variants/sample2.g.vcf.gz \
    -V variants/sample3.g.vcf.gz \
    --genomicsdb-workspace-path genomicsdb \
    -L intervals.bed

gatk GenotypeGVCFs \
    -R reference.fa \
    -V gendb://genomicsdb \
    -O variants/cohort.vcf.gz

Step 5 Alternative: GATK Variant Filtering

# Hard filtering (for small cohorts)
gatk VariantFiltration \
    -R reference.fa \
    -V variants/cohort.vcf.gz \
    --filter-expression "QD < 2.0" --filter-name "LowQD" \
    --filter-expression "FS > 60.0" --filter-name "HighFS" \
    --filter-expression "MQ < 40.0" --filter-name "LowMQ" \
    --filter-expression "MQRankSum < -12.5" --filter-name "LowMQRS" \
    --filter-expression "ReadPosRankSum < -8.0" --filter-name "LowRPRS" \
    -O variants/cohort.filtered.vcf.gz

# VQSR (for large cohorts >30 samples)
gatk VariantRecalibrator \
    -R reference.fa \
    -V variants/cohort.vcf.gz \
    --resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap.vcf.gz \
    --resource:omni,known=false,training=true,truth=false,prior=12.0 omni.vcf.gz \
    --resource:1000G,known=false,training=true,truth=false,prior=10.0 1000G.vcf.gz \
    --resource:dbsnp,known=true,training=false,truth=false,prior=2.0 dbsnp.vcf.gz \
    -an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR \
    -mode SNP \
    -O cohort.snp.recal \
    --tranches-file cohort.snp.tranches

gatk ApplyVQSR \
    -R reference.fa \
    -V variants/cohort.vcf.gz \
    -O variants/cohort.vqsr.vcf.gz \
    --recal-file cohort.snp.recal \
    --tranches-file cohort.snp.tranches \
    -mode SNP \
    --truth-sensitivity-filter-level 99.5

Parameter Recommendations

Choosing Between bcftools and GATK

Troubleshooting

Complete Pipeline Script

#!/bin/bash
set -e

# Configuration
THREADS=8
REF="reference.fa"
SAMPLES="sample1 sample2 sample3"
OUTDIR="results"

mkdir -p ${OUTDIR}/{trimmed,aligned,variants,qc}

echo "=== Step 1: QC with fastp ==="
for sample in $SAMPLES; do
    fastp -i ${sample}_R1.fastq.gz -I ${sample}_R2.fastq.gz \
        -o ${OUTDIR}/trimmed/${sample}_R1.fq.gz \
        -O ${OUTDIR}/trimmed/${sample}_R2.fq.gz \
        --detect_adapter_for_pe \
        --html ${OUTDIR}/qc/${sample}_fastp.html \
        -w ${THREADS}
done

echo "=== Step 2: Alignment with bwa-mem2 ==="
for sample in $SAMPLES; do
    bwa-mem2 mem -t ${THREADS} \
        -R "@RG\tID:${sample}\tSM:${sample}\tPL:ILLUMINA" \
        ${REF} \
        ${OUTDIR}/trimmed/${sample}_R1.fq.gz \
        ${OUTDIR}/trimmed/${sample}_R2.fq.gz | \
    samtools view -@ ${THREADS} -bS - > ${OUTDIR}/aligned/${sample}.bam
done

echo "=== Step 3: BAM Processing ==="
for sample in $SAMPLES; do
    samtools fixmate -@ ${THREADS} -m ${OUTDIR}/aligned/${sample}.bam - | \
    samtools sort -@ ${THREADS} - | \
    samtools markdup -@ ${THREADS} - ${OUTDIR}/aligned/${sample}.markdup.bam
    samtools index ${OUTDIR}/aligned/${sample}.markdup.bam
    rm ${OUTDIR}/aligned/${sample}.bam
done

echo "=== Step 4: Joint Variant Calling ==="
bcftools mpileup -Ou -f ${REF} ${OUTDIR}/aligned/*.markdup.bam | \
    bcftools call -mv -Oz -o ${OUTDIR}/variants/cohort.vcf.gz
bcftools index ${OUTDIR}/variants/cohort.vcf.gz

echo "=== Step 5: Filtering ==="
bcftools filter -Oz \
    -e 'QUAL<20 || DP<10 || MQ<30' \
    -o ${OUTDIR}/variants/cohort.filtered.vcf.gz \
    ${OUTDIR}/variants/cohort.vcf.gz
bcftools index ${OUTDIR}/variants/cohort.filtered.vcf.gz

echo "=== Stats ==="
bcftools stats ${OUTDIR}/variants/cohort.filtered.vcf.gz > ${OUTDIR}/variants/stats.txt

echo "=== Pipeline Complete ==="
echo "Filtered VCF: ${OUTDIR}/variants/cohort.filtered.vcf.gz"

Related Skills

• read-qc/fastp-workflow - Detailed QC options
• read-alignment/bwa-alignment - BWA-MEM2 parameters
• alignment-files/duplicate-handling - Duplicate marking details
• variant-calling/variant-calling - bcftools calling options
• variant-calling/gatk-variant-calling - GATK HaplotypeCaller details
• variant-calling/filtering-best-practices - Advanced filtering strategies
• variant-calling/variant-annotation - Annotate variants with VEP