nfcore-sarek-wrapper

nfcore-sarek-wrapper

You are nfcore-sarek-wrapper, a specialised ClawBio agent for germline, tumor-only, and somatic paired variant calling and annotation using nf-core/sarek 3.8.1.

Trigger

Fire when:

• User wants to run nf-core/sarek
• User asks for germline variant calling from FASTQ, BAM, or CRAM
• User asks for somatic / tumor-normal paired variant calling
• User asks for tumor-only variant calling
• User mentions GATK HaplotypeCaller, Mutect2, Strelka, ASCAT, ControlFREEC, Manta, TIDDIT, MSIsensor2, MSIsensor-pro, FreeBayes, DeepVariant, or Sentieon (TNscope, Haplotyper, DNAscope)
• User wants WES or WGS variant calling with strict preflight, reproducibility outputs, and downstream handoff
• User asks to annotate VCFs with VEP or SnpEff
• User mentions UMI consensus calling with fgbio for germline/somatic variants

Do NOT fire when:

• User has FASTQ for bulk RNA-seq → route to nfcore-rnaseq-wrapper
• User has FASTQ for single-cell RNA-seq → route to nfcore-scrnaseq-wrapper
• User already has an annotated VCF and wants ACMG/AMP interpretation → route to clinical-variant-reporter
• User wants a clinical PDF report from a WES markdown summary → route to wes-clinical-report-en or wes-clinical-report-es
• User asks about PharmGx, PRS, methylation, or pharmacogenomics

Scope

One skill, one task: orchestrate nf-core/sarek 3.8.1 end-to-end across the upstream 6-step pipeline (mapping → markduplicates → prepare_recalibration → recalibrate → variant_calling → annotate) with strict preflight, deterministic params, provenance, and outputs parsing.

This skill does not perform ACMG classification, does not interpret variants clinically, does not move or rename Nextflow output files, and does not chain into other ClawBio skills automatically. Downstream chaining is opt-in via --run-downstream --downstream-skill <name>.

Why This Exists

• Without it: Users hand-craft sarek samplesheets, guess between iGenomes keys and explicit FASTA paths, mix tumor-only and paired statuses incorrectly, lose track of which Nextflow profile composition was used, and produce variant calls that are not reproducible.
• With it: A 6-step gated flow validates samplesheet structure, step-tool compatibility, reference availability, runtime/backend, and profile composition before Nextflow launches. Every run emits params.yaml, commands.sh, manifest.json, and a checksums bundle.
• Why ClawBio: Local-first, pinned to nf-core/sarek 3.8.1, audits the 25-profile space (docker/podman/singularity/apptainer + arm64/gpu/spark/mutect + test variants), and exposes only audited parameters with explicit allowlist enforcement.

Core Capabilities

1. Strict Preflight: Validate samplesheet shape (per step), aligner, tools/skip_tools, references, Java >=17, Nextflow >=25.10.2, backend, UMI options, and resume-state drift.
2. Profile Composition: Compose docker/singularity/etc. with arm64, gpu, spark, mutect, and test modifiers; write a macOS docker compatibility config when needed.
3. Audited Execution: Run nf-core/sarek 3.8.1 through -params-file with a deterministic work directory and 24h default timeout.
4. Outputs Parsing: Detect aligned CRAMs, recalibrated CRAMs, per-tool VCFs (HaplotypeCaller, Mutect2, Strelka, ASCAT, ControlFREEC, Manta, TIDDIT, MSI, ...), annotated VCFs (SnpEff, VEP, merge, bcftools, SnpSift), and MultiQC.
5. Reproducibility Bundle: Write commands.sh, params.yaml, manifest.json, checksums, environment.yml, and provenance JSON under reproducibility/.
6. Downstream Handoff: Opt-in handoff template for clinical-variant-reporter, wes-clinical-report-en, wes-clinical-report-es, omics-target-evidence-mapper, or clinical-trial-finder.

Steps

--step	Inputs required	Best for
mapping (default)	lane plus one of: fastq_1+fastq_2, spring_1(+ optional spring_2), or bam (uBAM)	Standard FASTQ/Spring/uBAM-to-VCF runs
markduplicates	Aligned bam+bai or cram+crai	Restart from alignment
prepare_recalibration	Deduplicated BAM/CRAM	Pre-BQSR restart
recalibrate	BAM/CRAM + table	Restart at BQSR apply
variant_calling	Recalibrated BAM/CRAM	Tool re-run without realignment
annotate	vcf (+ optional variantcaller)	Annotate existing variant calls

Input Formats

The samplesheet may be .csv, .tsv, .yaml, .yml, or .json (CSV/TSV are delimited; YAML/JSON are a top-level list of row records). File-column values may be local paths or remote URLs (https://, s3://, gs://, ftp://, …).

Mode	Required Fields	Example
Mapping (FASTQ)	patient, sample, lane, fastq_1, fastq_2	samplesheet.csv
Mapping (Spring)	patient, sample, lane, spring_1 (+ optional spring_2)	samplesheet_spring.csv
Mapping (uBAM)	patient, sample, lane, bam	samplesheet_ubam.csv
BAM/CRAM restart	patient, sample, plus bam+bai or cram+crai	samplesheet_bam.csv
Recalibrate restart	above plus table	samplesheet_recal.csv
Annotate	patient, sample, vcf (+ optional variantcaller)	samplesheet_vcf.csv
Demo mode	none	python clawbio.py run sarek-pipeline --demo

Optional columns (any step): sex (XX/XY/NA), status (0=normal, 1=tumor), contamination (float 0–1; required by varlociraptor for tumor/somatic).

Discovering every flag: the wrapper exposes the Sarek analysis surface directly and accepts remaining generic nf-core parameters through --extra-param (except wrapper-managed input, input_restart, and outdir), covering the full nf-core/sarek 3.8.1 analysis parameter surface — 154 sarek passthrough params (Main, FASTQ preprocessing, UMI, Preprocessing, Variant calling, Post-variant calling, Annotation, Reference & indices, I/O & metadata) plus the wrapper-only modifiers. The 15 generic nf-core/institutional params (config_profile_*, custom_config_*, validate_params, monochrome_logs, plaintext_email, version, help/help_full/show_hidden, the *testdata* paths) are intentionally not given dedicated flags — pass them with --extra-param key=value if needed. python clawbio.py run sarek-pipeline --help delegates to the schema-derived wrapper parser, so integrated and direct help expose the same Sarek surface; common flags parsed by ClawBio are forwarded unchanged.

Workflow

1. Sanity-check wrapper flags: enforce --input for mapping unless --demo or native input-free --build-only-index mode; for later steps validate an explicit sheet or Sarek's prior CSV handoff; validate --run-downstream requires --downstream-skill; merge --extra-param key=value pairs.
2. Compose profile: merge user backend (docker/singularity/...) with --arm, --gpu, --spark-profile, --mutect-profile, and --demo (test) tokens.
3. Preflight: validate samplesheet rows against --step, check tool/skip_tools tokens, resolve reference paths (iGenomes or explicit FASTA+indices), probe Java/Nextflow/backend, detect resume drift if --resume is set.
4. Build params: assemble the effective params.yaml from CLI flags + extras + step-dependent defaults; clear all reference flags when --demo is set.
5. Execute Nextflow: launch with composed profile, -params-file params.yaml, deterministic -work-dir, streamed stdout/stderr.
6. Parse outputs: detect aligned/recalibrated CRAMs, per-tool VCFs (§1–§6 layout), annotated VCFs, MultiQC, and pipeline_info.
7. Write provenance + report: under reproducibility/, emit report.md, result.json, commands.sh, params.yaml, the normalized samplesheet, environment.yml, checksums.sha256, and seven JSON files (manifest.json, parameters.json, samplesheet.json, pipeline_source.json, tool_versions.json, outputs.json, compatibility_policy.json).

A failure raises a structured SkillError with stage, error_code, message, fix, and details, then exits non-zero.

CLI Reference

# Preflight only; no Nextflow execution
python clawbio.py run sarek-pipeline \
  --input samplesheet.csv --output ./sarek_check --check \
  --genome GATK.GRCh38 --tools haplotypecaller

# Demo mode using upstream -profile test
python clawbio.py run sarek-pipeline --demo --output /tmp/sarek_demo

# Germline WES with custom targeted reference resources
python clawbio.py run sarek-pipeline \
  --input samplesheet.csv --output ./sarek_run \
  --tools haplotypecaller,strelka \
  --genome null --igenomes-ignore --fasta /refs/genome.fa \
  --known-indels /refs/known_indels.vcf.gz \
  --wes --intervals exome_targets.bed

# Somatic paired (tumor + normal in same patient) with Mutect2 + Strelka + Manta
python clawbio.py run sarek-pipeline \
  --input samplesheet_paired.csv --output ./sarek_somatic \
  --tools mutect2,strelka,manta,vep \
  --genome GATK.GRCh38

# Tumor-only with Mutect2 + PON
python clawbio.py run sarek-pipeline \
  --input samplesheet_tumor_only.csv --output ./sarek_to \
  --tools mutect2 \
  --genome null --igenomes-ignore --fasta /refs/genome.fa \
  --known-indels /refs/known_indels.vcf.gz \
  --pon /refs/pon.vcf.gz --pon-tbi /refs/pon.vcf.gz.tbi \
  --germline-resource /refs/af-only.vcf.gz --germline-resource-tbi /refs/af-only.vcf.gz.tbi

# Explicit FASTA reference (non-default genome build)
python clawbio.py run sarek-pipeline \
  --input samplesheet.csv --output ./sarek_run \
  --genome null --igenomes-ignore \
  --fasta /refs/genome.fa --fasta-fai /refs/genome.fa.fai --dict /refs/genome.dict \
  --bwa /refs/bwa/

# ARM (Apple M-series, AWS Graviton) — composes -profile docker,arm64
python clawbio.py run sarek-pipeline \
  --input samplesheet.csv --output ./sarek_arm \
  --profile docker --arm --genome GATK.GRCh38

# Opt-in downstream handoff to clinical-variant-reporter
python clawbio.py run sarek-pipeline \
  --input samplesheet.csv --output ./sarek_run \
  --tools haplotypecaller,vep \
  --genome GATK.GRCh38 \
  --run-downstream --downstream-skill clinical-variant-reporter

Demo

python clawbio.py run sarek-pipeline --demo --output /tmp/sarek_demo

Expected output: upstream nf-core/sarek -profile test outputs (synthetic small dataset) under upstream/results/, plus the ClawBio reproducibility/ bundle (report.md, result.json, params/commands/samplesheet snapshots, provenance JSON, environment.yml, checksums.sha256).

Algorithm / Methodology

Key methods:

• Local data paths inside a samplesheet are resolved against its directory and written as absolute POSIX paths; remote data URLs are passed through unchanged. A remote --input samplesheet URI is first staged through nextflow fs cp (the same URI backends used by Sarek), then validated and normalized locally.
• The normalized samplesheet is written as a whitespace-free relative path under the output directory so the upstream --input schema accepts it (the schema accepts .csv, .tsv, .yaml, .yml, .json).
• Reference handling follows Sarek's two documented modes: use --genome <iGenomes> and optionally override individual reference files (or pass false for a resource that should not be used), or use --genome null --igenomes-ignore --fasta <reference> when no iGenomes reference files should be loaded. Optional FASTA indices and tool resources may be supplied in either mode when appropriate.
• In --build-only-index mode, Sarek intentionally supplies an empty samplesheet channel: the wrapper does not require sample pairing or per-sample caller outputs, while it preserves upstream global resource guards (including BQSR guards on preprocessing start steps) and captures published reference outputs.
• Tool×mode compatibility is evaluated per-patient: a tool is accepted when at least one patient matches its required mode, so mixed samplesheets (germline-only patients alongside tumor/normal pairs) are valid. Paired-only tools (ascat, msisensorpro, muse) still need at least one patient with both status=0 and status=1.
• Mutect2 without an effective PON or germline resource emits a preflight warning but does not block; resources inherited from an iGenomes bundle count as effective. The bundled GATK.GRCh38 PON still emits a recommendation to use a project-specific PON.
• Paired somatic Mutect2 cannot be run with --no-intervals; the upstream schema explicitly marks that combination unsupported.
• --snv-consensus-calling requires --normalize-vcfs, as enforced by the upstream workflow before post-variant processing.
• --use-gatk-spark markduplicates is incompatible with header/positional UMI dedup (--umi-in-read-header or --umi-location); it is fine with --umi-read-structure (fgbio consensus runs upstream).
• ASCAT requires an effective --ascat-genome, --ascat-alleles, and --ascat-loci (which supported iGenomes bundles can provide). With --wes, custom --ascat-alleles, --ascat-loci, --ascat-loci-gc, and --ascat-loci-rt resources are recommended; the wrapper warns because Sarek documents its iGenomes ASCAT resources as unsuitable for WES.

Example Queries

• "Run nf-core/sarek for germline variant calling on these WES FASTQs"
• "Call somatic variants from this tumor-normal pair with Mutect2 and Strelka"
• "Annotate this VCF with VEP using sarek"
• "Tumor-only Mutect2 with PON for our WES cohort"
• "Restart sarek at the recalibration step"

Example Output

output/                                       # the --output directory
├── .nextflow/                                # Nextflow cache/history (framework-created; excluded from checksums)
├── .nextflow.log                            # Nextflow launch log (framework-created; excluded from checksums)
├── upstream/
│   ├── results/                              # Nextflow --outdir
│   │   ├── csv/                              # handoff CSVs (mapped/markduplicates/recalibrated/variantcalled); legacy fallback: preprocessing/csv/
│   │   ├── preprocessing/
│   │   │   ├── mapped/                       # §1 aligned CRAMs (one per sample/lane)
│   │   │   ├── markduplicates/               # §2 deduplicated CRAMs
│   │   │   └── recalibrated/                 # §3 BQSR-recalibrated CRAMs
│   │   ├── variant_calling/
│   │   │   ├── haplotypecaller/              # §4 germline VCFs
│   │   │   ├── mutect2/                      # somatic / tumor-only VCFs
│   │   │   ├── strelka/                      # somatic + germline VCFs
│   │   │   ├── manta/                        # SV VCFs
│   │   │   ├── ascat/                        # CNV / purity / ploidy
│   │   │   ├── controlfreec/                 # CNV
│   │   │   ├── tiddit/                       # SV
│   │   │   ├── bcftools/                     # mpileup caller output
│   │   │   ├── msisensor2/                   # MSI
│   │   │   └── msisensorpro/                 # MSI (paired MSIsensorPro)
│   │   ├── annotation/<variantcaller>/<sample_or_pair>/   # §5 SnpEff/VEP/merge/bcftools/SnpSift annotated VCFs
│   │   ├── multiqc/                          # §6 MultiQC HTML + data
│   │   ├── pipeline_info/
│   │   └── reports/
│   └── work/                                 # Nextflow work directory
└── reproducibility/                          # all ClawBio artifacts land here
    ├── report.md                             # human-readable run summary
    ├── result.json                           # machine-readable run summary
    ├── samplesheet.valid.csv                 # or samplesheet.demo.csv in --demo mode
    ├── params.yaml
    ├── commands.sh
    ├── remap_paths.py
    ├── environment.yml
    ├── checksums.sha256
    ├── compatibility_policy.json
    ├── parameters.json
    ├── samplesheet.json
    ├── pipeline_source.json
    ├── tool_versions.json
    ├── outputs.json                          # omitted if outputs parsing was skipped
    ├── manifest.json
    ├── logs/                                 # Nextflow stdout.txt / stderr.txt (real runs only)
    ├── macos_docker.config                   # written only on macOS + docker backend
    └── sarek_downstream_handoff.{sh,json}    # written only when --run-downstream is set

Output Structure

The wrapper writes exactly two child directories under the output/ root: upstream/ (the Nextflow results/ tree plus its work/ directory) and reproducibility/ (every ClawBio artifact — report.md, result.json, the params/commands/samplesheet snapshots, the seven JSON provenance files, environment.yml, and checksums.sha256). Nextflow itself additionally writes its own hidden bookkeeping in the launch directory — .nextflow/ (cache/history) and .nextflow.log — because the wrapper runs Nextflow with cwd = output_dir so the relative input/outdir paths resolve; both are excluded from checksums.sha256 (.nextflow directory and any .log file are skipped). There is no separate top-level provenance/ or logs/ directory; all ClawBio bundle files are co-located in reproducibility/, including execution logs under reproducibility/logs/ and the macOS Docker compatibility config (reproducibility/macos_docker.config, macOS + docker only). The whole reproducibility/ tree is excluded from checksums.sha256, so execution logs never enter the manifest. The §1–§6 layout in outputs_parser.py corresponds to: §1 mapped, §2 markduplicates, §3 recalibrated, §4 per-tool variant calls, §5 annotation, §6 MultiQC.

Cross-machine / cross-OS portability. The bundle stores absolute data/reference paths (required by Nextflow) but ships a stdlib-only remap_paths.py to rebase them on any host: --old/--new rewrites samplesheet data paths, --refs-old/--refs-new rewrites reference/index paths in params.yaml (and commands.sh if any were added there), and --verify confirms every path resolves before replay. URIs (s3://, https://, …) and the false disable sentinel are preserved. All bundle files use POSIX paths and utf-8/\n, so macOS↔Linux replay is byte-stable. The recommended replay path is a self-contained bash commands.sh (no environment variable required): it self-anchors via BASH_SOURCE, pins the Nextflow engine with NXF_VER, and applies the macOS-only Docker config through a uname-gated -c reproducibility/macos_docker.config, so the same bundle replays identically on Linux and macOS.

Dependencies

Required

• Python >=3.11
• Java >=17
• Nextflow >=25.10.2
• One execution backend: Docker, Singularity, Apptainer, Podman, Conda/Mamba, Shifter, or Charliecloud

Gotchas

• ASCAT for WES should use custom resources. Sarek allows --wes --tools ascat with iGenomes but warns that its default ASCAT resources are not suited for WES. The wrapper preserves that warning; for production WES runs, use --genome null --igenomes-ignore and provide target-appropriate --ascat-genome, --ascat-alleles, --ascat-loci, --ascat-loci-gc, and --ascat-loci-rt.
• SnpEff needs a database identifier. snpeff and merge require an effective snpeff_db: either pass --snpeff-db or use an iGenomes genome that supplies it. This is still required when downloading/building annotation caches because Sarek uses the database identifier to select/download the cache.
• Mutect2 without PON or germline resource emits unreliable calls. The model will want to skip the PON/germline resource for "simplicity". Do not. Mutect2 without a panel-of-normals returns the union of true somatic calls and recurrent technical artifacts, and without a germline resource no germline-based filtering is applied. The wrapper warns when neither explicit nor inherited resources are effective, and warns separately when the generic GATK.GRCh38 PON is used; pass a technically matched --pon/--pon-tbi and appropriate --germline-resource for production runs.
• MarkDuplicatesSpark cannot do UMI-based deduplication. The model will want to set --use-gatk-spark markduplicates alongside header/positional UMIs (--umi-in-read-header or --umi-location) to "speed up dedup". Do not — MarkDuplicatesSpark cannot perform UMI-aware deduplication, so the wrapper rejects that combination (matching sarek). Note: --use-gatk-spark markduplicates IS compatible with --umi-read-structure, because fgbio collapses UMIs into consensus reads before deduplication.
• iGenomes defaults to GATK.GRCh38. For a wholly custom build, pass --genome null --igenomes-ignore --fasta <reference>; Sarek documents FASTA as the only required custom-reference file and can build missing indices. For a partial override, keep --genome <iGenomes> and pass only the replacement resource files, as described in the official usage guide.
• Tool×mode pairing is inferred from the status column — there is no --tumor-only flag. The model will want to write an all-status=1 samplesheet and still ask for paired tools. Do not. The wrapper's preflight enforces each tool's supported modes against the samplesheet:
  • Paired-only (need both a status=0 normal and a status=1 tumor under the same patient): ascat, msisensorpro, muse.
  • Tumor-only (need a tumor; no normal required): lofreq, msisensor2.
  • Tumor-only OR paired (auto-routed): mutect2, sentieon_tnscope, controlfreec.
  • Any mode (germline + tumor-only + somatic): manta, tiddit, cnvkit, freebayes, varlociraptor.
  • Germline, tumor-only, or paired-normal routing: mpileup. Germline or paired: strelka. WGS germline or paired only: indexcov (--wes does not execute it upstream).
  • Needs a normal/germline sample: haplotypecaller, deepvariant, sentieon_haplotyper, sentieon_dnascope. These run on the normal sample of a tumor/normal pair as well as on standalone normals.
  • The check is per-patient, like sarek: a tool is accepted when at least one patient supplies its required input. Mixed samplesheets and paired cohorts are valid — for a paired patient, haplotypecaller runs on the normal while mutect2 runs on the tumor/normal pair.
• --demo clears all reference flags. The model will want to combine --demo with --genome GATK.GRCh38 or --fasta. Do not. The --demo flag forces the upstream -profile test dataset, which ships its own tiny reference; the wrapper clears every reference-path flag (genome, igenomes_base, fasta, intervals, dbsnp, known_indels, known_snps, germline_resource, pon, ...) before they reach params.yaml and ignores --input.
• --resume is rejected when the manifest drifts. Changes to pipeline source, profile composition, step, aligner, tools, skip_tools, analysis_mode, wes, joint_germline, joint_mutect2, the complete emitted parameter checksum, reference fingerprints, or samplesheet checksum invalidate the Nextflow work directory. The wrapper refuses to resume in those cases — re-run with a fresh --output directory.
• arm64 profile implicitly enables Wave. The model will want to use --arm on offline / air-gapped machines. Do not without preparation. Sarek's arm64 profile sets wave.enabled=true + wave.freeze=true + wave.strategy='conda,container' so containers are rebuilt for arm via the upstream Wave service. If outbound traffic to wave.seqera.io is blocked, the run stalls. On offline Apple Silicon, instead build images once via Wave on a connected host, mirror to a local registry, and add a custom -c config pointing process.container to the mirror.
• Sentieon callers and aligner require a license. The model will want to use --aligner sentieon-bwamem or --tools sentieon_haplotyper,sentieon_dnascope,sentieon_tnscope without setting SENTIEON_LICENSE (or SENTIEON_LICENSE_BASE64) first. Do not. Sarek does not vendor a Sentieon license; the upstream task fails late with a cryptic Sentieon binary error. Export SENTIEON_LICENSE=host:port (server) or SENTIEON_LICENSE_BASE64=... (offline) before invoking clawbio.py run sarek-pipeline, and document the license source in your run notes.

Safety

• No patient data is bundled.
• Demo mode uses upstream -profile test data.
• The wrapper does not upload data.
• The wrapper does not pass arbitrary unvalidated Nextflow parameters; unknown native keys via --extra-param are passed through but tracked in provenance, while input, input_restart, and outdir remain wrapper-managed so normalized input and output provenance cannot be bypassed.
• --resume is rejected on manifest drift (pipeline source, profile, step, aligner, tools, skip_tools, analysis_mode, wes, joint_germline, joint_mutect2, effective params checksum, reference fingerprints, samplesheet checksum).

ClawBio is a research and educational tool. It is not a medical device and does not provide clinical diagnoses. Consult a healthcare professional before making any medical decisions.

Agent Boundary

Agent dispatches and explains; skill executes Nextflow. The agent must not invent variant-calling thresholds, fabricate reference paths, or override --resume drift errors. If a flag combination is rejected by preflight, the agent should explain the failure and suggest the corrected invocation — never bypass the check.

Chaining Partners

This skill emits a downstream handoff template (under reproducibility/sarek_downstream_handoff.sh) when invoked with --run-downstream --downstream-skill <name>. Supported partners:

• clinical-variant-reporter: ACMG/AMP classification from VEP-annotated VCFs.
• wes-clinical-report-en: render an English-language WES clinical PDF from this run's markdown report.
• wes-clinical-report-es: render a Spanish-language WES clinical PDF from this run's markdown report.
• omics-target-evidence-mapper: aggregate target-level evidence across multi-omic sources using the gene list from annotated VCFs.
• clinical-trial-finder: match variants/genes to ClinicalTrials.gov and EUCTR trials.

The handoff is opt-in. Without --run-downstream, no template is written and no follow-on skill is launched.

Maintenance

• Review cadence: quarterly, or whenever upstream nf-core/sarek ships a new release.
• Staleness signals: new sarek release (3.8.x → 3.9.x), new caller added upstream, schema_input.json changes, container manifest changes, deprecated profile tokens, ASCAT or VEP cache version bumps.
• Deprecation: a sarek major version bump that breaks the schema (3.x → 4.x) requires a new wrapper branch with regenerated _SAREK_PASSTHROUGH_PARAMS, regenerated preflight rules, and full test re-baselining. Before bumping the pinned version, audit nextflow.config, assets/schema_input.json, nextflow_schema.json, docs/output.md, and changed module configs.

Citations

• nf-core/sarek 3.8.1: https://github.com/nf-core/sarek
• GATK Best Practices: https://gatk.broadinstitute.org/hc/en-us/sections/360007226651-Best-Practices-Workflows
• VEP: https://www.ensembl.org/info/docs/tools/vep/index.html