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Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.

Use this Skill: https://skilld.dev/gh/anthropics/knowledge-work-plugins/nextflow-development

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referencespipelinessarek.md

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nf-core/sarek

Version: 3.7.1

Official Documentation: https://nf-co.re/sarek/3.7.1/ GitHub: https://github.com/nf-core/sarek

Note: When updating to a new version, check the releases page for breaking changes and update the version in commands below.

Contents

Test command

nextflow run nf-core/sarek -r 3.7.1 -profile test,docker --outdir test_sarek

Expected: ~20 min, creates aligned BAMs and variant calls.

Samplesheet format

From FASTQ

patient,sample,lane,fastq_1,fastq_2
patient1,tumor,L001,/path/to/tumor_L001_R1.fq.gz,/path/to/tumor_L001_R2.fq.gz
patient1,tumor,L002,/path/to/tumor_L002_R1.fq.gz,/path/to/tumor_L002_R2.fq.gz
patient1,normal,L001,/path/to/normal_R1.fq.gz,/path/to/normal_R2.fq.gz

From BAM/CRAM

patient,sample,bam,bai
patient1,tumor,/path/to/tumor.bam,/path/to/tumor.bam.bai
patient1,normal,/path/to/normal.bam,/path/to/normal.bam.bai

With tumor/normal status

patient,sample,lane,fastq_1,fastq_2,status
patient1,tumor,L001,tumor_R1.fq.gz,tumor_R2.fq.gz,1
patient1,normal,L001,normal_R1.fq.gz,normal_R2.fq.gz,0

status: 0 = normal, 1 = tumor

Variant calling modes

Germline (single sample)

nextflow run nf-core/sarek -r 3.7.1 -profile docker \
    --input samplesheet.csv --outdir results --genome GRCh38 \
    --tools haplotypecaller,snpeff

Somatic (tumor-normal pair)

nextflow run nf-core/sarek -r 3.7.1 -profile docker \
    --input samplesheet.csv --outdir results --genome GRCh38 \
    --tools mutect2,strelka,snpeff

WES (exome)

nextflow run nf-core/sarek -r 3.7.1 -profile docker \
    --input samplesheet.csv --outdir results --genome GRCh38 \
    --wes --intervals /path/to/targets.bed \
    --tools haplotypecaller,snpeff

Joint germline (cohort)

--tools haplotypecaller --joint_germline

Parameters

Available tools

Germline callers:

  • haplotypecaller: GATK HaplotypeCaller
  • freebayes: FreeBayes
  • deepvariant: DeepVariant (GPU optional)
  • strelka: Strelka2 germline

Somatic callers:

  • mutect2: GATK Mutect2
  • strelka: Strelka2 somatic
  • manta: Structural variants

CNV callers:

  • ascat: Copy number
  • controlfreec: CNV detection
  • tiddit: SV calling

Annotation:

  • snpeff: Functional annotation
  • vep: Variant Effect Predictor

Key parameters

Parameter Default Description
--tools - Comma-separated list of tools
--genome - GRCh38, GRCh37
--wes false Exome mode (requires --intervals)
--intervals - BED file for targeted regions
--joint_germline false Joint calling for cohorts
--skip_bqsr false Skip base quality recalibration

Output files

results/
├── preprocessing/
│   └── recalibrated/           # Analysis-ready BAMs
│       └── *.recal.bam
├── variant_calling/
│   ├── haplotypecaller/        # Germline VCFs
│   ├── mutect2/                # Somatic VCFs (filtered)
│   └── strelka/
├── annotation/
│   └── snpeff/                 # Annotated VCFs
└── multiqc/

Troubleshooting

BQSR fails: Check known sites available for genome. Skip with --skip_bqsr for non-standard references.

Mutect2 no variants: Verify tumor/normal pairing in samplesheet (check status column).

Out of memory: --max_memory '128.GB' for WGS.

DeepVariant GPU: Ensure NVIDIA Docker runtime configured.

More Information

Source: SKILL.md on GitHub

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    This skill provides a comprehensive workflow for running nf-core bioinformatics pipelines. It includes some security considerations such as the suggestion to install tools via remote scripts and instructions for using administrative privileges during environment setup. While these represent potential risks, they are part of the standard deployment process for the intended scientific tools and target well-known services. The skill also processes metadata from public databases, which creates a surface for indirect prompt injection that warrants standard review.

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