How do I install the Bio Variant Calling Filtering Best Practices skill?

Run npx skillmds@latest add bg-szy/bio-variant-calling-filtering-best-practices in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Bio Variant Calling Filtering Best Practices skill do?

Comprehensive variant filtering including GATK VQSR, hard filters, bcftools expressions, and quality metric interpretation for SNPs and indels. Use when filtering variants using GATK best practices. It is listed under Coding & Dev Tools on SkillMD.

Is Bio Variant Calling Filtering Best Practices safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Bio Variant Calling Filtering Best Practices?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Bio Variant Calling Filtering Best Practices free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Bio Variant Calling Filtering Best Practices?

bg-szy (@bg-szy) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bio Variant Calling Filtering Best Practices by bg-szy

Name: Bio Variant Calling Filtering Best Practices
Author: bg-szy

Variant Filtering Best Practices

Filter Selection Decision Tree

Is dataset large enough for VQSR? (>30 exomes or WGS)
├── Yes → Use VQSR (machine learning)
└── No → Use hard filters
    ├── Germline → GATK recommended thresholds
    └── Somatic → Caller-specific filters + manual review

GATK Hard Filter Thresholds

# 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

Metric	Meaning	Good Value
QD	Quality by Depth	>2 (variant quality normalized by depth)
FS	Fisher Strand	<60 SNP, <200 indel (strand bias)
MQ	Mapping Quality	>40 (RMS mapping quality)
MQRankSum	MQ Rank Sum	>-12.5 (ref vs alt mapping quality)
ReadPosRankSum	Read Position	>-8 (position in read bias)
SOR	Strand Odds Ratio	<3 SNP, <10 indel (strand bias)
DP	Depth	Sample-specific, avoid extremes
GQ	Genotype Quality	>20 (confidence in genotype)

bcftools filter

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

Operator	Meaning
`<`, `<=`, `>`, `>=`	Comparison
`=`, `==`	Equals
`!=`	Not equals
`&&`, `\|\|`	AND, OR
`!`	NOT

Aggregate Functions

Function	Description
`MIN(x)`	Minimum across samples
`MAX(x)`	Maximum across samples
`AVG(x)`	Average across samples
`SUM(x)`	Sum across samples

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

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

# 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

# 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

# Exclude problematic regions
bcftools view -T ^blacklist.bed input.vcf -o filtered.vcf

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

Sample-Level Filtering

# 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

# 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

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

# 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)

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

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

# Ti/Tv ratio (should be ~2.1 for WGS, ~2.8-3.3 for exomes)
bcftools stats filtered.vcf | grep 'TSTV'

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

Quick Reference

Task	Command
Quality filter	`bcftools filter -e 'QUAL<30' in.vcf.gz`
Depth filter	`bcftools filter -e 'INFO/DP<10' in.vcf.gz`
SNPs only	`bcftools view -v snps in.vcf.gz`
Indels only	`bcftools view -v indels in.vcf.gz`
PASS only	`bcftools view -f PASS in.vcf.gz`
Soft filter	`bcftools filter -s 'LowQ' -e 'QUAL<30' in.vcf.gz`
Region	`bcftools view -r chr1:1-1000 in.vcf.gz`
Biallelic	`bcftools view -m2 -M2 in.vcf.gz`

Common Errors

Error	Cause	Solution
`no such INFO tag`	Tag not in VCF	Check header with `bcftools view -h`
`syntax error`	Invalid expression	Check operator syntax (`\|\|` not `or`)
`empty output`	Filter too strict	Relax thresholds

Related Skills

variant-calling/variant-calling - Generate VCF files
variant-calling/gatk-variant-calling - GATK VQSR
variant-calling/variant-annotation - Annotation after filtering
variant-calling/vcf-statistics - Evaluate filter effects

Bio Variant Calling Filtering Best Practices

File contents

Variant Filtering Best Practices

Filter Selection Decision Tree

GATK Hard Filter Thresholds

Understanding Quality Metrics

bcftools filter

Soft vs Hard Filtering

Expression Syntax

Aggregate Functions

Common bcftools Filters

bcftools view Filtering

Filter by Variant Type

Filter by Region

Filter by Samples

Depth Filtering

Allele Frequency Filters

Region-Based Filtering

Sample-Level Filtering

Variant Type-Specific Filters

Multi-Step Pipeline Filtering

Somatic Variant Filters

Python Filtering (cyvcf2)

Filter by Genotype

Filter by Sample Depth

Validate Filtering

Quick Reference

Common Errors

Related Skills

Frequently asked questions

Bio Variant Calling Filtering Best Practices

File contents

Variant Filtering Best Practices

Filter Selection Decision Tree

GATK Hard Filter Thresholds

Understanding Quality Metrics

bcftools filter

Soft vs Hard Filtering

Expression Syntax

Aggregate Functions

Common bcftools Filters

bcftools view Filtering

Filter by Variant Type

Filter by Region

Filter by Samples

Depth Filtering

Allele Frequency Filters

Region-Based Filtering

Sample-Level Filtering

Variant Type-Specific Filters

Multi-Step Pipeline Filtering

Somatic Variant Filters

Python Filtering (cyvcf2)

Filter by Genotype

Filter by Sample Depth

Validate Filtering

Quick Reference

Common Errors

Related Skills

Related skills

Frequently asked questions

How do I install the Bio Variant Calling Filtering Best Practices skill?

What does the Bio Variant Calling Filtering Best Practices skill do?

Is Bio Variant Calling Filtering Best Practices safe to use?

Which AI agents work with Bio Variant Calling Filtering Best Practices?

Is Bio Variant Calling Filtering Best Practices free to use?

Who published Bio Variant Calling Filtering Best Practices?