Bio Workflows Fastq To Variants

Version Compatibility

Reference examples tested with: BWA-MEM2 2.2.1+, Ensembl VEP 111+, GATK 4.5+, bcftools 1.19+, fastp 0.23+, 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.

FASTQ to Variants Workflow

"Call variants from my whole-genome or exome FASTQ files" → Orchestrate fastp QC, BWA-MEM2 alignment, duplicate marking, BQSR, GATK HaplotypeCaller variant calling, and VQSR/hard filtering to produce filtered VCF output.

Complete pipeline from raw DNA sequencing FASTQ files to filtered variant calls.

Workflow Overview

FASTQ files
    |
    v
[1. QC & Trimming] -----> fastp
    |
    v
[2. Alignment] ---------> bwa-mem2
    |
    v
[3. BAM Processing] ----> sort, markdup, index
    |
    v
[4. Variant Calling] ---> bcftools (primary) or GATK
    |
    v
[5. Filtering] ---------> Quality filters
    |
    v
Filtered VCF

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 (DRAGEN Mode -- Recommended)

gatk CreateSequenceDictionary -R reference.fa
samtools faidx reference.fa

# DRAGEN-GATK mode: no BQSR needed, improved STR handling and QUAL calibration
gatk HaplotypeCaller \
    -R reference.fa \
    -I aligned/sample1.markdup.bam \
    -O variants/sample1.g.vcf.gz \
    -ERC GVCF \
    --dragen-mode

Step 4 Alternative: GATK Standard Mode (with 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

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 a Variant Caller

For human germline: DeepVariant provides the highest accuracy. GATK DRAGEN-mode is the recommended standard-mode alternative (no BQSR needed). bcftools is best for exploratory work, non-model organisms, or when computational resources are limited.

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

• database-access/sra-data - Pull public FASTQ for reanalysis (ENA mirror or STRIDES cloud)
• database-access/ncbi-datasets-cli - Pull reference genome assembly via Datasets v2 CLI
• database-access/ensembl-rest - Pull annotated reference and Compara orthologs for cross-species variants
• 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