Version Compatibility

Reference examples tested with: GATK 4.5+, bcftools 1.19+, numpy 1.26+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• 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.

Variant Filtering Best Practices

Filter Selection Decision Tree

Is this somatic data?
├── Yes → FilterMutectCalls (GATK), not VQSR or hard filters
└── No (germline) →
    Was DRAGEN-GATK mode used?
    ├── Yes → Hard filter on QUAL only (DRAGEN QUAL is well-calibrated)
    └── No →
        Is dataset large enough for VQSR?
        ├── Yes (>30 WGS/exomes, human, truth sets available) → VQSR
        │   └── Large cohort? → Use allele-specific VQSR (-AS flag)
        └── No → Hard filtering
            ├── Non-model organism → Hard filtering only (no training resources)
            └── Targeted panel → Hard filtering (too few variants for VQSR model)

VQSR requires a Gaussian mixture model trained on known truth sets (HapMap, 1000G, dbSNP). With fewer than ~30 samples, the model cannot learn the annotation distributions reliably. Targeted panels produce too few variants for stable model convergence, regardless of sample count.

GATK Hard Filter Thresholds

Goal: Apply GATK-recommended annotation thresholds to separate true variants from artifacts.

Approach: Use VariantFiltration with per-metric filter expressions for SNPs and indels separately.

"Filter my variants using GATK best practices" → Apply fixed annotation thresholds (QD, FS, MQ, SOR, RankSum) to flag low-quality variants.

# SNPs
gatk VariantFiltration \
    -R reference.fa \
    -V raw_snps.vcf \
    -O filtered_snps.vcf \
    --filter-expression "QD < 2.0" --filter-name "QD2" \
    --filter-expression "FS > 60.0" --filter-name "FS60" \
    --filter-expression "MQ < 40.0" --filter-name "MQ40" \
    --filter-expression "MQRankSum < -12.5" --filter-name "MQRankSum-12.5" \
    --filter-expression "ReadPosRankSum < -8.0" --filter-name "ReadPosRankSum-8" \
    --filter-expression "SOR > 3.0" --filter-name "SOR3"

# Indels
gatk VariantFiltration \
    -R reference.fa \
    -V raw_indels.vcf \
    -O filtered_indels.vcf \
    --filter-expression "QD < 2.0" --filter-name "QD2" \
    --filter-expression "FS > 200.0" --filter-name "FS200" \
    --filter-expression "ReadPosRankSum < -20.0" --filter-name "ReadPosRankSum-20" \
    --filter-expression "SOR > 10.0" --filter-name "SOR10"

Understanding Quality Metrics

bcftools filter

Goal: Filter variants using bcftools expression syntax with soft or hard removal.

Approach: Use -e (exclude) or -i (include) with expressions on QUAL, INFO, and FORMAT fields; use -s for soft filtering.

Soft vs Hard Filtering

# Hard filter (remove variants)
bcftools filter -e 'QUAL<30' input.vcf.gz -o filtered.vcf

# Soft filter (mark, don't remove)
bcftools filter -s 'LowQual' -e 'QUAL<30' input.vcf.gz -o marked.vcf
# Variants failing filter get "LowQual" in FILTER column

# Include instead of exclude
bcftools filter -i 'QUAL>=30' input.vcf.gz -o filtered.vcf

Expression Syntax

Aggregate Functions

Common bcftools Filters

# Basic quality filter
bcftools filter -i 'QUAL>30 && DP>10' input.vcf -o filtered.vcf

# Complex filter with multiple metrics
bcftools filter -i 'QUAL>30 && INFO/DP>10 && INFO/DP<500 && \
    (INFO/FS<60 || INFO/FS=".") && INFO/MQ>40' input.vcf -o filtered.vcf

# Genotype-level filters
bcftools filter -i 'FMT/DP>10 && FMT/GQ>20' input.vcf -o filtered.vcf

# Remove filtered sites
bcftools view -f PASS input.vcf -o passed.vcf

# Keep only biallelic SNPs
bcftools view -m2 -M2 -v snps input.vcf -o biallelic_snps.vcf

# Check for missing values
bcftools filter -e 'QUAL="."' input.vcf.gz  # Exclude missing QUAL
bcftools filter -i 'INFO/DP!="."' input.vcf.gz  # Include only if DP exists

bcftools view Filtering

Goal: Select variants by type, region, or sample using bcftools view.

Approach: Use type flags (-v/-V), region flags (-r/-R), and sample flags (-s/-S) for structured subsetting.

Filter by Variant Type

# SNPs only
bcftools view -v snps input.vcf.gz -o snps.vcf.gz

# Indels only
bcftools view -v indels input.vcf.gz -o indels.vcf.gz

# Exclude SNPs
bcftools view -V snps input.vcf.gz -o no_snps.vcf.gz

Filter by Region

bcftools view -r chr1:1000000-2000000 input.vcf.gz -o region.vcf.gz

# Multiple regions
bcftools view -r chr1:1000-2000,chr2:3000-4000 input.vcf.gz

Filter by Samples

# Include samples
bcftools view -s sample1,sample2 input.vcf.gz -o subset.vcf.gz

# Exclude samples
bcftools view -s ^sample3,sample4 input.vcf.gz -o subset.vcf.gz

Depth Filtering

Goal: Remove variants at extreme depth values that suggest mapping artifacts or duplications.

Approach: Calculate depth percentiles, then filter to the middle 90% of the distribution.

# Calculate depth percentiles
bcftools query -f '%DP\n' input.vcf | \
    sort -n | \
    awk '{a[NR]=$1} END {print "5th:", a[int(NR*0.05)], "95th:", a[int(NR*0.95)]}'

# Filter to middle 90% of depth distribution
bcftools filter -i 'INFO/DP>10 && INFO/DP<200' input.vcf -o depth_filtered.vcf

Allele Frequency Filters

Goal: Filter variants by minor allele frequency and allelic balance.

Approach: Apply thresholds on INFO/AF for population frequency and AD ratios for heterozygote balance.

# Minor allele frequency filter (population data)
bcftools filter -i 'INFO/AF>0.01 && INFO/AF<0.99' input.vcf -o maf_filtered.vcf

# Allele balance for heterozygotes
bcftools filter -i 'GT="het" -> (AD[1]/(AD[0]+AD[1]) > 0.2 && AD[1]/(AD[0]+AD[1]) < 0.8)' \
    input.vcf -o ab_filtered.vcf

Region-Based Filtering

Goal: Include or exclude variants based on genomic region annotations and artifact-prone regions.

Approach: Use bcftools view with BED files to exclude known problematic regions and restrict to targets. Always benchmark filtering stratified by genomic context.

Key BED resources for region filtering:

• ENCODE exclusion list (formerly blacklist): regions with anomalous signal across cell types (centromeres, telomeres, satellite repeats). Available at github.com/Boyle-Lab/Blacklist.
• GIAB difficult regions: stratified BED files for low-complexity, segmental duplications, tandem repeats, and other challenging contexts. Essential for honest benchmarking.
• LCR (low-complexity region) BED files: homopolymers, dinucleotide repeats, and other simple sequences where indel calling is unreliable. Heng Li's LCR-hs38 is widely used.

# Exclude ENCODE exclusion list regions
bcftools view -T ^ENCFF356LFX.bed input.vcf -o filtered.vcf

# Exclude low-complexity regions
bcftools view -T ^LCR-hs38.bed.gz input.vcf -o lcr_filtered.vcf

# Keep only exonic regions
bcftools view -R exons.bed input.vcf -o exonic.vcf

# Stratified evaluation against GIAB truth set
bcftools isec -p benchmark_dir filtered.vcf truth.vcf.gz

Sample-Level Filtering

Goal: Remove low-quality samples and sites with excessive missing genotypes.

Approach: Compute per-sample missingness with bcftools stats, exclude failing samples, and filter sites by F_MISSING threshold.

# Missing genotype rate per sample
bcftools stats -s - input.vcf | grep ^PSC | cut -f3,14

# Filter samples with >10% missing
bcftools view -S good_samples.txt input.vcf -o sample_filtered.vcf

# Filter sites with >5% missing genotypes
bcftools filter -i 'F_MISSING<0.05' input.vcf -o site_filtered.vcf

Variant Type-Specific Filters

Goal: Apply different quality thresholds to SNPs and indels.

Approach: Separate by type, apply type-appropriate thresholds (e.g., FS<60 for SNPs, FS<200 for indels), then merge back.

# Separate SNPs and indels for different filters
bcftools view -v snps input.vcf -o snps.vcf
bcftools view -v indels input.vcf -o indels.vcf

# Apply different thresholds
bcftools filter -i 'QUAL>30 && INFO/FS<60' snps.vcf -o snps_filtered.vcf
bcftools filter -i 'QUAL>30 && INFO/FS<200' indels.vcf -o indels_filtered.vcf

# Merge back
bcftools concat snps_filtered.vcf indels_filtered.vcf | bcftools sort -o merged.vcf

Multi-Step Pipeline Filtering

Goal: Chain multiple filter criteria in a single pipeline with optional soft filter labels.

Approach: Pipe through successive bcftools filter commands, using -s for named soft filters and -f PASS for final hard extraction.

bcftools filter -e 'QUAL<30' input.vcf.gz | \
    bcftools filter -e 'INFO/DP<10' | \
    bcftools view -v snps -Oz -o filtered_snps.vcf.gz

# Named soft filters
bcftools filter -s 'LowQual' -e 'QUAL<30' input.vcf.gz | \
    bcftools filter -s 'LowDepth' -e 'INFO/DP<10' -Oz -o marked.vcf.gz

# Later, remove all filtered variants
bcftools view -f PASS marked.vcf.gz -Oz -o pass_only.vcf.gz

Somatic Variant Filters

Goal: Filter somatic variants from tumor-normal calling with caller-specific criteria.

Approach: Use GATK FilterMutectCalls with contamination/segmentation tables, then apply additional bcftools thresholds on TLOD and VAF.

# Mutect2 specific
gatk FilterMutectCalls \
    -R reference.fa \
    -V mutect2_raw.vcf \
    --contamination-table contamination.table \
    --tumor-segmentation segments.table \
    -O mutect2_filtered.vcf

# Additional somatic filters
bcftools filter -i 'INFO/TLOD>6.3 && FMT/AF[0]>0.05 && FMT/DP[0]>20' \
    mutect2_filtered.vcf -o somatic_final.vcf

Python Filtering (cyvcf2)

Goal: Filter variants programmatically in Python for custom multi-metric criteria.

Approach: Iterate with cyvcf2, evaluate QUAL/INFO/FORMAT fields per variant, and write passing records with Writer.

from cyvcf2 import VCF, Writer
import numpy as np

vcf = VCF('input.vcf.gz')
writer = Writer('filtered.vcf', vcf)

for variant in vcf:
    qual = variant.QUAL or 0
    dp = variant.INFO.get('DP', 0)
    fs = variant.INFO.get('FS', 0)
    mq = variant.INFO.get('MQ', 0)

    if qual >= 30 and dp >= 10 and fs <= 60 and mq >= 40:
        writer.write_record(variant)

writer.close()
vcf.close()

Filter by Genotype

from cyvcf2 import VCF, Writer

vcf = VCF('input.vcf.gz')
writer = Writer('filtered.vcf', vcf)

for variant in vcf:
    # Keep if at least one sample is heterozygous
    # gt_types: 0=HOM_REF, 1=HET, 2=UNKNOWN, 3=HOM_ALT
    if 1 in variant.gt_types:
        writer.write_record(variant)

writer.close()
vcf.close()

Filter by Sample Depth

from cyvcf2 import VCF, Writer
import numpy as np

vcf = VCF('input.vcf.gz')
writer = Writer('filtered.vcf', vcf)

for variant in vcf:
    depths = variant.format('DP')
    if depths is not None and np.min(depths) >= 10:
        writer.write_record(variant)

writer.close()
vcf.close()

Validate Filtering

Goal: Assess the impact of filtering on variant quality and retention using diagnostic metrics.

Approach: Compare before/after bcftools stats, check Ti/Tv and Het/Hom ratios against expected ranges, and verify known variant recovery.

Expected metric ranges for human samples:

If Ti/Tv drops after filtering, the filters may be preferentially removing true transitions.

# Compare before/after stats
bcftools stats input.vcf > before_stats.txt
bcftools stats filtered.vcf > after_stats.txt

# Ti/Tv ratio
bcftools stats filtered.vcf | grep 'TSTV'

# Het/Hom ratio
bcftools stats -s - filtered.vcf | grep ^PSC | awk '{print $3, "het/hom:", $5/$6}'

# Count variants per filter label
bcftools query -f '%FILTER\n' filtered.vcf | sort | uniq -c

# Known variant recovery (requires dbSNP-annotated VCF)
bcftools view -i 'ID!="."' filtered.vcf | bcftools stats | grep 'number of SNPs'

Common Filtering Pitfalls

• Over-filtering rare variants: Strict annotation thresholds disproportionately penalize rare variants, which have fewer supporting reads and therefore noisier annotations. Consider tiered filtering: relaxed thresholds for singletons, standard for common variants.
• Applying SNP filters to indels: SNPs and indels have fundamentally different annotation distributions (e.g., FS, SOR, ReadPosRankSum). Always separate by variant type before filtering.
• Ignoring multiallelic sites: Standard VQSR evaluates all alleles at a site together. For large cohorts, allele-specific VQSR (-AS flag) evaluates each allele independently, preventing a single poor allele from dragging down a good one.
• Not verifying filter effects: Always check Ti/Tv, Het/Hom, and known variant recovery rate before and after filtering. A filter that improves one metric while degrading another may be miscalibrated.
• Filtering on depth alone: DP filtering removes sites in collapsed segmental duplications that may harbor real variants. Combine DP with other annotations (MQ, MQRankSum) to distinguish true high-depth from mapping artifacts.
• Not examining annotation distributions: Plot histograms of each annotation (QD, FS, MQ) stratified by known true positives (e.g., HapMap sites) vs likely false positives before choosing thresholds. GATK defaults are population-level recommendations; specific datasets may warrant adjustment.

Quick Reference

Common Errors

Related Skills

• variant-calling/variant-calling - Variant calling with bcftools to generate VCF files
• variant-calling/gatk-variant-calling - GATK HaplotypeCaller and VQSR pipelines
• variant-calling/deepvariant - Deep learning variant calling as an alternative to GATK
• variant-calling/variant-annotation - Functional annotation after filtering
• variant-calling/vcf-statistics - Evaluate filter effects with concordance and summary metrics
• variant-calling/vcf-basics - VCF format fundamentals and field interpretation
• variant-calling/variant-normalization - Left-align and decompose before filtering for consistent comparisons