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07 — WGS Germline Variant Calling#

A complete whole-genome sequencing (WGS) germline variant calling pipeline following GATK best practices: QC → alignment → deduplication → BQSR → variant calling → joint genotyping → VQSR → annotation.

Concepts Covered

  • GATK best-practices workflow
  • Twelve-rule cohort DAG with a branching VQSR path
  • Cohort joint genotyping with CombineGVCFs
  • Per-sample rule expansion and expand_inputs fan-in
  • Mixed environments (conda, docker, singularity)
  • Clinical-grade variant annotation with VEP
  • Report configuration with provenance tracking

Pipeline Overview#

graph TD
    A[fastp_qc] --> B[bwa_mem2_align]
    B --> C[mark_duplicates]
    C --> D[base_recalibration]
    D --> E[haplotype_caller]
    E --> F[combine_gvcfs]
    F --> G[genotype_gvcfs]
    G --> H[vqsr_snps]
    G --> I[apply_vqsr_snps]
    H --> I
    I --> J[vqsr_indels]
    J --> K[apply_vqsr_indels]
    K --> L[annotate_variants]

Edges are shown as realized after per-sample expansion (for example, haplotype_caller → combine_gvcfs exists because expand_inputs resolves the three per-sample GVCFs). In the unexpanded template DAG (oxo-flow graph), combine_gvcfs is a root rule — its inputs arrive via expand_inputs, not file-path inference. See Fan-out vs Fan-in for how the two expansion mechanisms differ.

Steps:

  1. fastp_qc — Read quality control and adapter trimming
  2. bwa_mem2_align — Paired-end alignment with BWA-MEM2 (faster than BWA-MEM)
  3. mark_duplicates — Mark PCR and optical duplicates with GATK MarkDuplicates
  4. base_recalibration — Base quality score recalibration (BQSR) using known variant sites
  5. haplotype_caller — Per-sample variant calling in GVCF mode
  6. combine_gvcfs — Combine the per-sample GVCFs into a cohort GVCF
  7. genotype_gvcfs — Joint genotyping across the cohort
  8. vqsr_snps — Variant Quality Score Recalibration (VQSR) for SNPs
  9. apply_vqsr_snps — Apply the VQSR model to filter the cohort's SNPs
  10. vqsr_indels — Variant Quality Score Recalibration (VQSR) for INDELs
  11. apply_vqsr_indels — Apply the VQSR model to filter the cohort's INDELs
  12. annotate_variants — Functional annotation with Ensembl VEP

Workflow Definition#

# examples/gallery/07_wgs_germline.oxoflow
# 07 — Whole-Genome Germline Variant Calling
# Complete WGS pipeline: QC → alignment → dedup → BQSR → variant calling → annotation.
# Demonstrates: GATK best practices, per-chromosome scatter, clinical environments.

[workflow]
name = "wgs-germline-calling"
version = "1.0.0"
description = "GATK best-practices whole-genome germline variant calling pipeline"
author = "oxo-flow examples"

[config]
reference = "/data/references/GRCh38/genome.fa"
known_sites = "/data/references/GRCh38/dbsnp_146.hg38.vcf.gz"
known_indels = "/data/references/GRCh38/Mills_and_1000G_gold_standard.indels.hg38.vcf.gz"
intervals = "/data/references/GRCh38/wgs_calling_regions.hg38.interval_list"
hapmap = "/data/references/GRCh38/hapmap_3.3.hg38.vcf.gz"
omni = "/data/references/GRCh38/1000G_omni2.5.hg38.vcf.gz"
thousand_g = "/data/references/GRCh38/1000G_phase1.snps.high_confidence.hg38.vcf.gz"

[[sample_groups]]
name = "cohort"
samples = ["NA12878", "NA12879", "NA12880"]

[defaults]
threads = 4
memory = "8G"

[[rules]]
name = "fastp_qc"
input = ["raw/{sample}_R1.fastq.gz", "raw/{sample}_R2.fastq.gz"]
output = ["trimmed/{sample}_R1.fastq.gz", "trimmed/{sample}_R2.fastq.gz", "qc/{sample}_fastp.json"]
description = "Read QC and adapter trimming"
shell = """
mkdir -p trimmed qc
fastp -i {input[0]} -I {input[1]} \
      -o {output[0]} -O {output[1]} \
      --json {output[2]} --thread {threads} \
      --qualified_quality_phred 20 --length_required 50
"""

[rules.resources]
threads = 8
memory = "16G"

[rules.environment]
conda = "envs/fastp.yaml"

[[rules]]
name = "bwa_mem2_align"
input = ["trimmed/{sample}_R1.fastq.gz", "trimmed/{sample}_R2.fastq.gz"]
output = ["aligned/{sample}.sorted.bam"]
description = "Paired-end alignment with BWA-MEM2 and coordinate sorting"
shell = """
mkdir -p aligned
rg=$(printf '@RG\tID:%s\tSM:%s\tLB:WGS\tPL:ILLUMINA\tPU:%s' '{sample}' '{sample}' '{sample}')
bwa-mem2 mem -M -t {threads} -R "$rg" \
    {config.reference} {input[0]} {input[1]} \
    | samtools sort -@ 4 -m 2G -o {output[0]}
samtools index {output[0]}
"""

[rules.resources]
threads = 16
memory = "32G"

# The pipe needs BOTH bwa-mem2 and samtools; single-tool docker images
# cannot run it — use a conda environment that ships the pair.
[rules.environment]
conda = "envs/alignment.yaml"

[[rules]]
name = "mark_duplicates"
input = ["aligned/{sample}.sorted.bam"]
output = ["dedup/{sample}.dedup.bam", "dedup/{sample}.dedup.metrics.txt"]
description = "Mark PCR and optical duplicates"
shell = """
mkdir -p dedup
gatk MarkDuplicates --VALIDATION_STRINGENCY SILENT \
    -I {input[0]} \
    -O {output[0]} \
    -M {output[1]} \
    --CREATE_INDEX true
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "base_recalibration"
input = ["dedup/{sample}.dedup.bam"]
output = ["bqsr/{sample}.recal.bam"]
description = "Base quality score recalibration (BQSR)"
shell = """
mkdir -p bqsr
gatk BaseRecalibrator --read-validation-stringency SILENT \
    -I {input[0]} -R {config.reference} \
    --known-sites {config.known_sites} \
    --known-sites {config.thousand_g} \
    --known-sites {config.known_indels} \
    -O bqsr/{sample}.recal_data.table

gatk ApplyBQSR --read-validation-stringency SILENT \
    -I {input[0]} -R {config.reference} \
    --bqsr-recal-file bqsr/{sample}.recal_data.table \
    -O {output[0]}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "haplotype_caller"
input = ["bqsr/{sample}.recal.bam"]
output = ["variants/{sample}.g.vcf.gz"]
description = "Per-sample variant calling in GVCF mode"
shell = """
mkdir -p variants
gatk HaplotypeCaller --read-validation-stringency SILENT \
    -I {input[0]} -R {config.reference} \
    -O {output[0]} \
    -ERC GVCF \
    --native-pair-hmm-threads {threads} \
    -L {config.intervals}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "combine_gvcfs"
input = []
expand_inputs = [
    { pattern = "variants/{sample}.g.vcf.gz", variables = { sample = "config.samples_list" } }
]
output = ["variants/cohort.g.vcf.gz"]
description = "Combine per-sample GVCFs into a multi-sample GVCF"
shell = """
gatk CombineGVCFs \
    -R {config.reference} \
    $(for f in {input}; do echo "-V $f "; done) \
    -O {output[0]}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "genotype_gvcfs"
input = ["variants/cohort.g.vcf.gz"]
output = ["variants/cohort.genotyped.vcf.gz"]
description = "Joint genotyping across the cohort"
shell = """
gatk GenotypeGVCFs \
    -R {config.reference} \
    -V {input[0]} \
    -O {output[0]}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "vqsr_snps"
input = ["variants/cohort.genotyped.vcf.gz"]
output = [
    "variants/cohort.snps.recal",
    "variants/cohort.snps.tranches"
]
description = "Variant Quality Score Recalibration (VQSR) for SNPs"
shell = """
gatk VariantRecalibrator \
    -R {config.reference} \
    -V {input[0]} \
    -resource:hapmap,known=false,training=true,truth=true,prior=15.0 {config.hapmap} \
    -resource:omni,known=false,training=true,truth=false,prior=12.0 {config.omni} \
    -resource:1000G,known=false,training=true,truth=false,prior=10.0 {config.thousand_g} \
    -resource:dbsnp,known=true,training=false,truth=false,prior=2.0 {config.known_sites} \
    -an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR \
    -mode SNP \
    -O {output[0]} \
    --tranches-file {output[1]}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "apply_vqsr_snps"
input = [
    "variants/cohort.genotyped.vcf.gz",
    "variants/cohort.snps.recal",
    "variants/cohort.snps.tranches"
]
output = ["variants/cohort.snps.filtered.vcf.gz"]
description = "Apply VQSR model to filter SNPs"
shell = """
gatk ApplyVQSR \
    -R {config.reference} \
    -V {input[0]} \
    -O {output[0]} \
    --truth-sensitivity-filter-level 99.7 \
    --tranches-file {input[2]} \
    --recal-file {input[1]} \
    -mode SNP \
    --create-output-variant-index true
"""

[rules.resources]
threads = 4
memory = "8G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "vqsr_indels"
input = ["variants/cohort.snps.filtered.vcf.gz"]
output = [
    "variants/cohort.indels.recal",
    "variants/cohort.indels.tranches"
]
description = "Variant Quality Score Recalibration (VQSR) for INDELs"
shell = """
gatk VariantRecalibrator \
    -R {config.reference} \
    -V {input[0]} \
    -resource:mills,known=false,training=true,truth=true,prior=12.0 {config.known_indels} \
    -resource:dbsnp,known=true,training=false,truth=false,prior=2.0 {config.known_sites} \
    -an QD -an FS -an SOR -an MQRankSum -an ReadPosRankSum \
    -mode INDEL \
    -O {output[0]} \
    --tranches-file {output[1]}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "apply_vqsr_indels"
input = [
    "variants/cohort.snps.filtered.vcf.gz",
    "variants/cohort.indels.recal",
    "variants/cohort.indels.tranches"
]
output = ["variants/cohort.filtered.vcf.gz"]
description = "Apply VQSR model to filter INDELs"
shell = """
gatk ApplyVQSR \
    -R {config.reference} \
    -V {input[0]} \
    -O {output[0]} \
    --truth-sensitivity-filter-level 99.7 \
    --tranches-file {input[2]} \
    --recal-file {input[1]} \
    -mode INDEL \
    --create-output-variant-index true
"""

[rules.resources]
threads = 4
memory = "8G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "annotate_variants"
input = ["variants/cohort.filtered.vcf.gz"]
output = ["annotation/cohort.annotated.vcf.gz"]
description = "Functional variant annotation with VEP"
shell = """
mkdir -p annotation
vep --input_file {input[0]} \
    --output_file {output[0]} \
    --format vcf --vcf --compress_output bgzip \
    --assembly GRCh38 --offline --cache \
    --sift b --polyphen b --symbol --numbers --biotype \
    --total_length --canonical --ccds \
    --force_overwrite --fork {threads}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
conda = "envs/vep.yaml"

[report]
# template: reserved — supply a real Tera template file path to use one
# template = "germline_report"
format = ["html", "json"]
sections = ["summary", "qc_metrics", "coverage", "variants", "annotations", "provenance"]

Read Group Escape Sequences

The \\t in the read group string (-R '@RG\\tID:...') is a double-escaped tab character. TOML requires \\ to represent a literal backslash, so the shell receives \t; inside single quotes it passes the two characters through literally, and bwa-mem2 itself converts the \t escape to a real tab when it writes the read group header into the SAM — the delimiter it expects between read group fields.

[[rules]]
name = "mark_duplicates"
input = ["aligned/{sample}.sorted.bam"]
output = ["dedup/{sample}.dedup.bam", "dedup/{sample}.dedup.metrics.txt"]
description = "Mark PCR and optical duplicates"
shell = """
mkdir -p dedup
gatk MarkDuplicates \
    -I {input[0]} \
    -O {output[0]} \
    -M {output[1]} \
    --CREATE_INDEX true
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "base_recalibration"
input = ["dedup/{sample}.dedup.bam"]
output = ["bqsr/{sample}.recal.bam"]
description = "Base quality score recalibration (BQSR)"
shell = """
mkdir -p bqsr
gatk BaseRecalibrator \
    -I {input[0]} -R {config.reference} \
    --known-sites {config.known_sites} \
    --known-sites {config.thousand_g} \
    --known-sites {config.known_indels} \
    -O bqsr/{sample}.recal_data.table

gatk ApplyBQSR \
    -I {input[0]} -R {config.reference} \
    --bqsr-recal-file bqsr/{sample}.recal_data.table \
    -O {output[0]}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "haplotype_caller"
input = ["bqsr/{sample}.recal.bam"]
output = ["variants/{sample}.g.vcf.gz"]
description = "Per-sample variant calling in GVCF mode"
shell = """
mkdir -p variants
gatk HaplotypeCaller \
    -I {input[0]} -R {config.reference} \
    -O {output[0]} \
    -ERC GVCF \
    --native-pair-hmm-threads {threads} \
    -L {config.intervals}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "combine_gvcfs"
input = []
expand_inputs = [
    { pattern = "variants/{sample}.g.vcf.gz", variables = { sample = "config.samples_list" } }
]
output = ["variants/cohort.g.vcf.gz"]
description = "Combine per-sample GVCFs into a multi-sample GVCF"
shell = """
gatk CombineGVCFs \
    -R {config.reference} \
    $(for f in {input}; do echo "-V $f "; done) \
    -O {output[0]}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

!!! note "Why `input = []` + `expand_inputs` — not `{sample}` in `input`"
    `combine_gvcfs` must run **once**, with all per-sample GVCFs in its
    input list. `expand_inputs` does exactly that: the engine splits the
    injected `config.samples_list` (`"NA12878,NA12879,NA12880"`) into its
    three values, substitutes each into the pattern, and appends the three
    paths to the rule's input list — while the rule itself stays **one task**.

    Writing `input = ["variants/{sample}.g.vcf.gz"]` instead would have the
    opposite effect: `{sample}` clones the rule once per sample, producing
    three one-sample combines that all write the same output file.

    Full mechanics: [Fan-out vs Fan-in](../reference/wildcards.md#fan-out-vs-fan-in),
    [Gathering Inputs with `expand_inputs`](../reference/wildcards.md#gathering-inputs-with-expand_inputs),
    and the [field reference](../reference/workflow-format.md#expand-inputs).

[[rules]]
name = "genotype_gvcfs"
input = ["variants/cohort.g.vcf.gz"]
output = ["variants/cohort.genotyped.vcf.gz"]
description = "Joint genotyping across the cohort"
shell = """
gatk GenotypeGVCFs \
    -R {config.reference} \
    -V {input[0]} \
    -O {output[0]}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "vqsr_snps"
input = ["variants/cohort.genotyped.vcf.gz"]
output = [
    "variants/cohort.snps.recal",
    "variants/cohort.snps.tranches"
]
description = "Variant Quality Score Recalibration (VQSR) for SNPs"
shell = """
gatk VariantRecalibrator \
    -R {config.reference} \
    -V {input[0]} \
    -resource:hapmap,known=false,training=true,truth=true,prior=15.0 {config.hapmap} \
    -resource:omni,known=false,training=true,truth=false,prior=12.0 {config.omni} \
    -resource:1000G,known=false,training=true,truth=false,prior=10.0 {config.thousand_g} \
    -resource:dbsnp,known=true,training=false,truth=false,prior=2.0 {config.known_sites} \
    -an QD -an MQ -an MQRankSum -an ReadPosRankSum -an FS -an SOR \
    -mode SNP \
    -O {output[0]} \
    --tranches-file {output[1]}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "apply_vqsr_snps"
input = [
    "variants/cohort.genotyped.vcf.gz",
    "variants/cohort.snps.recal",
    "variants/cohort.snps.tranches"
]
output = ["variants/cohort.snps.filtered.vcf.gz"]
description = "Apply VQSR model to filter SNPs"
shell = """
gatk ApplyVQSR \
    -R {config.reference} \
    -V {input[0]} \
    -O {output[0]} \
    --truth-sensitivity-filter-level 99.7 \
    --tranches-file {input[2]} \
    --recal-file {input[1]} \
    -mode SNP \
    --create-output-variant-index true
"""

[rules.resources]
threads = 4
memory = "8G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "vqsr_indels"
input = ["variants/cohort.snps.filtered.vcf.gz"]
output = [
    "variants/cohort.indels.recal",
    "variants/cohort.indels.tranches"
]
description = "Variant Quality Score Recalibration (VQSR) for INDELs"
shell = """
gatk VariantRecalibrator \
    -R {config.reference} \
    -V {input[0]} \
    -resource:mills,known=false,training=true,truth=true,prior=12.0 {config.known_indels} \
    -resource:dbsnp,known=true,training=false,truth=false,prior=2.0 {config.known_sites} \
    -an QD -an FS -an SOR -an MQRankSum -an ReadPosRankSum \
    -mode INDEL \
    -O {output[0]} \
    --tranches-file {output[1]}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "apply_vqsr_indels"
input = [
    "variants/cohort.snps.filtered.vcf.gz",
    "variants/cohort.indels.recal",
    "variants/cohort.indels.tranches"
]
output = ["variants/cohort.filtered.vcf.gz"]
description = "Apply VQSR model to filter INDELs"
shell = """
gatk ApplyVQSR \
    -R {config.reference} \
    -V {input[0]} \
    -O {output[0]} \
    --truth-sensitivity-filter-level 99.7 \
    --tranches-file {input[2]} \
    --recal-file {input[1]} \
    -mode INDEL \
    --create-output-variant-index true
"""

[rules.resources]
threads = 4
memory = "8G"

[rules.environment]
singularity = "docker://broadinstitute/gatk:4.5.0.0"

[[rules]]
name = "annotate_variants"
input = ["variants/cohort.filtered.vcf.gz"]
output = ["annotation/cohort.annotated.vcf.gz"]
description = "Functional variant annotation with VEP"
shell = """
mkdir -p annotation
vep --input_file {input[0]} \
    --output_file {output[0]} \
    --format vcf --vcf --compress_output bgzip \
    --assembly GRCh38 --offline --cache \
    --sift b --polyphen b --symbol --numbers --biotype \
    --total_length --canonical --ccds \
    --force_overwrite --fork {threads}
"""

[rules.resources]
threads = 4
memory = "16G"

[rules.environment]
conda = "envs/vep.yaml"

[report]
sections = ["universal", "execution-status", "failure-diagnosis", "workflow-info", "commands", "file-manifest", "environment", "provenance", "task-summary"]
# [report].template is consumed for HTML output ("report.html" built-in or a
# template file path); [report].format is still unsupported — `report` warns
# when it is set (see docs/guide/src/commands/report.md)

Sample Expansion#

The [[sample_groups]] table is the single source of truth for the cohort:

  • Rules with {sample} in their paths (fastp_qc through haplotype_caller) are expanded once per sample: fastp_qc_cohort_NA12878, and so on.
  • The engine merges all sample sources into config.samples_list ("NA12878,NA12879,NA12880"), and combine_gvcfs references it in expand_inputs to collect the three per-sample GVCFs — no duplicate sample list is needed anywhere else.

See the wildcards reference for the complete mechanics of per-sample expansion and expand_inputs.

Clinical Considerations#

BQSR (Base Quality Score Recalibration)#

BQSR corrects systematic errors in base quality scores assigned by the sequencer. It uses known variant sites (dbSNP, Mills indels) to distinguish true variants from sequencing artifacts. This step is critical for clinical-grade variant calling accuracy.

GVCF Mode#

HaplotypeCaller runs in GVCF mode (-ERC GVCF) to produce genomic VCFs that contain information about both variant and reference-confident sites. This enables downstream joint genotyping across cohorts without re-running variant calling.

The combine_gvcfs rule merges the per-sample GVCFs into a single cohort GVCF (variants/cohort.g.vcf.gz), and genotype_gvcfs performs joint genotyping across the cohort in one run.

VQSR (Variant Quality Score Recalibration)#

Instead of fixed hard filters, the pipeline applies VQSR for clinical-grade variant calling:

  1. vqsr_snps — GATK VariantRecalibrator builds a recalibration model from annotation features (QD, MQ, MQRankSum, ReadPosRankSum, FS, SOR), using hapmap, omni, and 1000G as training/truth resources and dbSNP as a known-sites resource.
  2. apply_vqsr_snps — GATK ApplyVQSR applies the SNP model at --truth-sensitivity-filter-level 99.7, retaining high sensitivity while filtering false positives.
  3. vqsr_indels — VariantRecalibrator builds a recalibration model for INDELs using the Mills gold-standard indels as the training/truth resource.
  4. apply_vqsr_indels — GATK ApplyVQSR applies the INDEL model at --truth-sensitivity-filter-level 99.7 to produce the final filtered variant set.

VQSR adaptively models the variant quality profile rather than applying fixed thresholds, which generally preserves more true variants than hard filtering.

VQSR assumes a large callset

GATK recommends VQSR for cohorts of roughly 30+ samples, where enough sites are available to train the model reliably. For a small cohort like the three samples here, GATK instead recommends hard filtering with VariantFiltration (e.g. QD < 2.0, FS > 60.0, MQ < 40.0 for SNPs). The example keeps VQSR to demonstrate the tool; match the filtering strategy to your cohort size. The engine's scientific preflight flags this automatically: oxo-flow dry-run emits SCI-VQSR-COHORT for small-cohort VQSR runs, so a pilot never wastes compute failing at this step for scientific reasons.

Running the Workflow#

Validate#

$ oxo-flow validate examples/gallery/07_wgs_germline.oxoflow
✓ examples/gallery/07_wgs_germline.oxoflow — 12 rules, 15 dependencies

Resource Summary#

Rule Threads Memory Environment
fastp_qc 8 16G conda
bwa_mem2_align 16 32G docker
mark_duplicates 4 16G singularity
base_recalibration 4 16G singularity
haplotype_caller 4 16G singularity
combine_gvcfs 4 16G singularity
genotype_gvcfs 4 16G singularity
vqsr_snps 4 16G singularity
apply_vqsr_snps 4 8G singularity
vqsr_indels 4 16G singularity
apply_vqsr_indels 4 8G singularity
annotate_variants 4 16G conda

What's Next?#

Move on to Multi-Omics Integration for a complex pipeline that combines WGS, RNA-seq, and methylation data.